INSTRUCTION
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Insert item at selected position.
Parameters
----------
loc : int
item : hashable
value : array_like
allow_duplicates: bool
If False, trying to insert non-unique item will raise | def insert(self, loc, item, value, allow_duplicates=False):
"""
Insert item at selected position.
Parameters
----------
loc : int
item : hashable
value : array_like
allow_duplicates: bool
If False, trying to insert non-unique item will raise
"""
if not allow_duplicates and item in self.items:
# Should this be a different kind of error??
raise ValueError('cannot insert {}, already exists'.format(item))
if not isinstance(loc, int):
raise TypeError("loc must be int")
# insert to the axis; this could possibly raise a TypeError
new_axis = self.items.insert(loc, item)
block = make_block(values=value, ndim=self.ndim,
placement=slice(loc, loc + 1))
for blkno, count in _fast_count_smallints(self._blknos[loc:]):
blk = self.blocks[blkno]
if count == len(blk.mgr_locs):
blk.mgr_locs = blk.mgr_locs.add(1)
else:
new_mgr_locs = blk.mgr_locs.as_array.copy()
new_mgr_locs[new_mgr_locs >= loc] += 1
blk.mgr_locs = new_mgr_locs
if loc == self._blklocs.shape[0]:
# np.append is a lot faster, let's use it if we can.
self._blklocs = np.append(self._blklocs, 0)
self._blknos = np.append(self._blknos, len(self.blocks))
else:
self._blklocs = np.insert(self._blklocs, loc, 0)
self._blknos = np.insert(self._blknos, loc, len(self.blocks))
self.axes[0] = new_axis
self.blocks += (block,)
self._shape = None
self._known_consolidated = False
if len(self.blocks) > 100:
self._consolidate_inplace() |
Conform block manager to new index. | def reindex_axis(self, new_index, axis, method=None, limit=None,
fill_value=None, copy=True):
"""
Conform block manager to new index.
"""
new_index = ensure_index(new_index)
new_index, indexer = self.axes[axis].reindex(new_index, method=method,
limit=limit)
return self.reindex_indexer(new_index, indexer, axis=axis,
fill_value=fill_value, copy=copy) |
Parameters
----------
new_axis : Index
indexer : ndarray of int64 or None
axis : int
fill_value : object
allow_dups : bool
pandas-indexer with -1's only. | def reindex_indexer(self, new_axis, indexer, axis, fill_value=None,
allow_dups=False, copy=True):
"""
Parameters
----------
new_axis : Index
indexer : ndarray of int64 or None
axis : int
fill_value : object
allow_dups : bool
pandas-indexer with -1's only.
"""
if indexer is None:
if new_axis is self.axes[axis] and not copy:
return self
result = self.copy(deep=copy)
result.axes = list(self.axes)
result.axes[axis] = new_axis
return result
self._consolidate_inplace()
# some axes don't allow reindexing with dups
if not allow_dups:
self.axes[axis]._can_reindex(indexer)
if axis >= self.ndim:
raise IndexError("Requested axis not found in manager")
if axis == 0:
new_blocks = self._slice_take_blocks_ax0(indexer,
fill_tuple=(fill_value,))
else:
new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(
fill_value if fill_value is not None else blk.fill_value,))
for blk in self.blocks]
new_axes = list(self.axes)
new_axes[axis] = new_axis
return self.__class__(new_blocks, new_axes) |
Slice/take blocks along axis=0.
Overloaded for SingleBlock
Returns
-------
new_blocks : list of Block | def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None):
"""
Slice/take blocks along axis=0.
Overloaded for SingleBlock
Returns
-------
new_blocks : list of Block
"""
allow_fill = fill_tuple is not None
sl_type, slobj, sllen = _preprocess_slice_or_indexer(
slice_or_indexer, self.shape[0], allow_fill=allow_fill)
if self._is_single_block:
blk = self.blocks[0]
if sl_type in ('slice', 'mask'):
return [blk.getitem_block(slobj, new_mgr_locs=slice(0, sllen))]
elif not allow_fill or self.ndim == 1:
if allow_fill and fill_tuple[0] is None:
_, fill_value = maybe_promote(blk.dtype)
fill_tuple = (fill_value, )
return [blk.take_nd(slobj, axis=0,
new_mgr_locs=slice(0, sllen),
fill_tuple=fill_tuple)]
if sl_type in ('slice', 'mask'):
blknos = self._blknos[slobj]
blklocs = self._blklocs[slobj]
else:
blknos = algos.take_1d(self._blknos, slobj, fill_value=-1,
allow_fill=allow_fill)
blklocs = algos.take_1d(self._blklocs, slobj, fill_value=-1,
allow_fill=allow_fill)
# When filling blknos, make sure blknos is updated before appending to
# blocks list, that way new blkno is exactly len(blocks).
#
# FIXME: mgr_groupby_blknos must return mgr_locs in ascending order,
# pytables serialization will break otherwise.
blocks = []
for blkno, mgr_locs in libinternals.get_blkno_placements(blknos,
self.nblocks,
group=True):
if blkno == -1:
# If we've got here, fill_tuple was not None.
fill_value = fill_tuple[0]
blocks.append(self._make_na_block(placement=mgr_locs,
fill_value=fill_value))
else:
blk = self.blocks[blkno]
# Otherwise, slicing along items axis is necessary.
if not blk._can_consolidate:
# A non-consolidatable block, it's easy, because there's
# only one item and each mgr loc is a copy of that single
# item.
for mgr_loc in mgr_locs:
newblk = blk.copy(deep=True)
newblk.mgr_locs = slice(mgr_loc, mgr_loc + 1)
blocks.append(newblk)
else:
blocks.append(blk.take_nd(blklocs[mgr_locs.indexer],
axis=0, new_mgr_locs=mgr_locs,
fill_tuple=None))
return blocks |
Take items along any axis. | def take(self, indexer, axis=1, verify=True, convert=True):
"""
Take items along any axis.
"""
self._consolidate_inplace()
indexer = (np.arange(indexer.start, indexer.stop, indexer.step,
dtype='int64')
if isinstance(indexer, slice)
else np.asanyarray(indexer, dtype='int64'))
n = self.shape[axis]
if convert:
indexer = maybe_convert_indices(indexer, n)
if verify:
if ((indexer == -1) | (indexer >= n)).any():
raise Exception('Indices must be nonzero and less than '
'the axis length')
new_labels = self.axes[axis].take(indexer)
return self.reindex_indexer(new_axis=new_labels, indexer=indexer,
axis=axis, allow_dups=True) |
Return a blockmanager with all blocks unstacked.
Parameters
----------
unstacker_func : callable
A (partially-applied) ``pd.core.reshape._Unstacker`` class.
fill_value : Any
fill_value for newly introduced missing values.
Returns
-------
unstacked : BlockManager | def unstack(self, unstacker_func, fill_value):
"""Return a blockmanager with all blocks unstacked.
Parameters
----------
unstacker_func : callable
A (partially-applied) ``pd.core.reshape._Unstacker`` class.
fill_value : Any
fill_value for newly introduced missing values.
Returns
-------
unstacked : BlockManager
"""
n_rows = self.shape[-1]
dummy = unstacker_func(np.empty((0, 0)), value_columns=self.items)
new_columns = dummy.get_new_columns()
new_index = dummy.get_new_index()
new_blocks = []
columns_mask = []
for blk in self.blocks:
blocks, mask = blk._unstack(
partial(unstacker_func,
value_columns=self.items[blk.mgr_locs.indexer]),
new_columns,
n_rows,
fill_value
)
new_blocks.extend(blocks)
columns_mask.extend(mask)
new_columns = new_columns[columns_mask]
bm = BlockManager(new_blocks, [new_columns, new_index])
return bm |
Delete single item from SingleBlockManager.
Ensures that self.blocks doesn't become empty. | def delete(self, item):
"""
Delete single item from SingleBlockManager.
Ensures that self.blocks doesn't become empty.
"""
loc = self.items.get_loc(item)
self._block.delete(loc)
self.axes[0] = self.axes[0].delete(loc) |
Concatenate a list of SingleBlockManagers into a single
SingleBlockManager.
Used for pd.concat of Series objects with axis=0.
Parameters
----------
to_concat : list of SingleBlockManagers
new_axis : Index of the result
Returns
-------
SingleBlockManager | def concat(self, to_concat, new_axis):
"""
Concatenate a list of SingleBlockManagers into a single
SingleBlockManager.
Used for pd.concat of Series objects with axis=0.
Parameters
----------
to_concat : list of SingleBlockManagers
new_axis : Index of the result
Returns
-------
SingleBlockManager
"""
non_empties = [x for x in to_concat if len(x) > 0]
# check if all series are of the same block type:
if len(non_empties) > 0:
blocks = [obj.blocks[0] for obj in non_empties]
if len({b.dtype for b in blocks}) == 1:
new_block = blocks[0].concat_same_type(blocks)
else:
values = [x.values for x in blocks]
values = _concat._concat_compat(values)
new_block = make_block(
values, placement=slice(0, len(values), 1))
else:
values = [x._block.values for x in to_concat]
values = _concat._concat_compat(values)
new_block = make_block(
values, placement=slice(0, len(values), 1))
mgr = SingleBlockManager(new_block, new_axis)
return mgr |
Construct SparseSeries from array.
.. deprecated:: 0.23.0
Use the pd.SparseSeries(..) constructor instead. | def from_array(cls, arr, index=None, name=None, copy=False,
fill_value=None, fastpath=False):
"""Construct SparseSeries from array.
.. deprecated:: 0.23.0
Use the pd.SparseSeries(..) constructor instead.
"""
warnings.warn("'from_array' is deprecated and will be removed in a "
"future version. Please use the pd.SparseSeries(..) "
"constructor instead.", FutureWarning, stacklevel=2)
return cls(arr, index=index, name=name, copy=copy,
fill_value=fill_value, fastpath=fastpath) |
return my self as a sparse array, do not copy by default | def as_sparse_array(self, kind=None, fill_value=None, copy=False):
""" return my self as a sparse array, do not copy by default """
if fill_value is None:
fill_value = self.fill_value
if kind is None:
kind = self.kind
return SparseArray(self.values, sparse_index=self.sp_index,
fill_value=fill_value, kind=kind, copy=copy) |
perform a reduction operation | def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
""" perform a reduction operation """
return op(self.get_values(), skipna=skipna, **kwds) |
Return the i-th value or values in the SparseSeries by location
Parameters
----------
i : int, slice, or sequence of integers
Returns
-------
value : scalar (int) or Series (slice, sequence) | def _ixs(self, i, axis=0):
"""
Return the i-th value or values in the SparseSeries by location
Parameters
----------
i : int, slice, or sequence of integers
Returns
-------
value : scalar (int) or Series (slice, sequence)
"""
label = self.index[i]
if isinstance(label, Index):
return self.take(i, axis=axis)
else:
return self._get_val_at(i) |
Return an object with absolute value taken. Only applicable to objects
that are all numeric
Returns
-------
abs: same type as caller | def abs(self):
"""
Return an object with absolute value taken. Only applicable to objects
that are all numeric
Returns
-------
abs: same type as caller
"""
return self._constructor(np.abs(self.values),
index=self.index).__finalize__(self) |
Returns value occupying requested label, default to specified
missing value if not present. Analogous to dict.get
Parameters
----------
label : object
Label value looking for
default : object, optional
Value to return if label not in index
Returns
-------
y : scalar | def get(self, label, default=None):
"""
Returns value occupying requested label, default to specified
missing value if not present. Analogous to dict.get
Parameters
----------
label : object
Label value looking for
default : object, optional
Value to return if label not in index
Returns
-------
y : scalar
"""
if label in self.index:
loc = self.index.get_loc(label)
return self._get_val_at(loc)
else:
return default |
Retrieve single value at passed index label
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
index : label
takeable : interpret the index as indexers, default False
Returns
-------
value : scalar value | def get_value(self, label, takeable=False):
"""
Retrieve single value at passed index label
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
index : label
takeable : interpret the index as indexers, default False
Returns
-------
value : scalar value
"""
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(label, takeable=takeable) |
Quickly set single value at passed label. If label is not contained, a
new object is created with the label placed at the end of the result
index
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
label : object
Partial indexing with MultiIndex not allowed
value : object
Scalar value
takeable : interpret the index as indexers, default False
Notes
-----
This method *always* returns a new object. It is not particularly
efficient but is provided for API compatibility with Series
Returns
-------
series : SparseSeries | def set_value(self, label, value, takeable=False):
"""
Quickly set single value at passed label. If label is not contained, a
new object is created with the label placed at the end of the result
index
.. deprecated:: 0.21.0
Please use .at[] or .iat[] accessors.
Parameters
----------
label : object
Partial indexing with MultiIndex not allowed
value : object
Scalar value
takeable : interpret the index as indexers, default False
Notes
-----
This method *always* returns a new object. It is not particularly
efficient but is provided for API compatibility with Series
Returns
-------
series : SparseSeries
"""
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(label, value, takeable=takeable) |
Convert SparseSeries to a Series.
Returns
-------
s : Series | def to_dense(self):
"""
Convert SparseSeries to a Series.
Returns
-------
s : Series
"""
return Series(self.values.to_dense(), index=self.index,
name=self.name) |
Make a copy of the SparseSeries. Only the actual sparse values need to
be copied | def copy(self, deep=True):
"""
Make a copy of the SparseSeries. Only the actual sparse values need to
be copied
"""
# TODO: https://github.com/pandas-dev/pandas/issues/22314
# We skip the block manager till that is resolved.
new_data = self.values.copy(deep=deep)
return self._constructor(new_data, sparse_index=self.sp_index,
fill_value=self.fill_value,
index=self.index.copy(),
name=self.name).__finalize__(self) |
Conform sparse values to new SparseIndex
Parameters
----------
new_index : {BlockIndex, IntIndex}
Returns
-------
reindexed : SparseSeries | def sparse_reindex(self, new_index):
"""
Conform sparse values to new SparseIndex
Parameters
----------
new_index : {BlockIndex, IntIndex}
Returns
-------
reindexed : SparseSeries
"""
if not isinstance(new_index, splib.SparseIndex):
raise TypeError("new index must be a SparseIndex")
values = self.values
values = values.sp_index.to_int_index().reindex(
values.sp_values.astype('float64'), values.fill_value, new_index)
values = SparseArray(values,
sparse_index=new_index,
fill_value=self.values.fill_value)
return self._constructor(values, index=self.index).__finalize__(self) |
Cumulative sum of non-NA/null values.
When performing the cumulative summation, any non-NA/null values will
be skipped. The resulting SparseSeries will preserve the locations of
NaN values, but the fill value will be `np.nan` regardless.
Parameters
----------
axis : {0}
Returns
-------
cumsum : SparseSeries | def cumsum(self, axis=0, *args, **kwargs):
"""
Cumulative sum of non-NA/null values.
When performing the cumulative summation, any non-NA/null values will
be skipped. The resulting SparseSeries will preserve the locations of
NaN values, but the fill value will be `np.nan` regardless.
Parameters
----------
axis : {0}
Returns
-------
cumsum : SparseSeries
"""
nv.validate_cumsum(args, kwargs)
# Validate axis
if axis is not None:
self._get_axis_number(axis)
new_array = self.values.cumsum()
return self._constructor(
new_array, index=self.index,
sparse_index=new_array.sp_index).__finalize__(self) |
Analogous to Series.dropna. If fill_value=NaN, returns a dense Series | def dropna(self, axis=0, inplace=False, **kwargs):
"""
Analogous to Series.dropna. If fill_value=NaN, returns a dense Series
"""
# TODO: make more efficient
# Validate axis
self._get_axis_number(axis or 0)
dense_valid = self.to_dense().dropna()
if inplace:
raise NotImplementedError("Cannot perform inplace dropna"
" operations on a SparseSeries")
if isna(self.fill_value):
return dense_valid
else:
dense_valid = dense_valid[dense_valid != self.fill_value]
return dense_valid.to_sparse(fill_value=self.fill_value) |
Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
y : Series | def combine_first(self, other):
"""
Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
y : Series
"""
if isinstance(other, SparseSeries):
other = other.to_dense()
dense_combined = self.to_dense().combine_first(other)
return dense_combined.to_sparse(fill_value=self.fill_value) |
Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
True attempts to create a cache of converted values
convert_listlike : function
Conversion function to apply on dates
Returns
-------
cache_array : Series
Cache of converted, unique dates. Can be empty | def _maybe_cache(arg, format, cache, convert_listlike):
"""
Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
True attempts to create a cache of converted values
convert_listlike : function
Conversion function to apply on dates
Returns
-------
cache_array : Series
Cache of converted, unique dates. Can be empty
"""
from pandas import Series
cache_array = Series()
if cache:
# Perform a quicker unique check
from pandas import Index
unique_dates = Index(arg).unique()
if len(unique_dates) < len(arg):
cache_dates = convert_listlike(unique_dates.to_numpy(),
True, format)
cache_array = Series(cache_dates, index=unique_dates)
return cache_array |
Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
box : boolean
True boxes result as an Index-like, False returns an ndarray
errors : string
'ignore' plus box=True will convert result to Index
name : string, default None
Name for a DatetimeIndex
Returns
-------
result : datetime of converted dates
Returns:
- Index-like if box=True
- ndarray if box=False | def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
"""
Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
box : boolean
True boxes result as an Index-like, False returns an ndarray
errors : string
'ignore' plus box=True will convert result to Index
name : string, default None
Name for a DatetimeIndex
Returns
-------
result : datetime of converted dates
Returns:
- Index-like if box=True
- ndarray if box=False
"""
from pandas import Series, DatetimeIndex, Index
result = Series(arg).map(cache_array)
if box:
if errors == 'ignore':
return Index(result, name=name)
else:
return DatetimeIndex(result, name=name)
return result.values |
Return results from array_strptime if a %z or %Z directive was passed.
Parameters
----------
result : ndarray
int64 date representations of the dates
timezones : ndarray
pytz timezone objects
box : boolean
True boxes result as an Index-like, False returns an ndarray
tz : object
None or pytz timezone object
name : string, default None
Name for a DatetimeIndex
Returns
-------
tz_result : ndarray of parsed dates with timezone
Returns:
- Index-like if box=True
- ndarray of Timestamps if box=False | def _return_parsed_timezone_results(result, timezones, box, tz, name):
"""
Return results from array_strptime if a %z or %Z directive was passed.
Parameters
----------
result : ndarray
int64 date representations of the dates
timezones : ndarray
pytz timezone objects
box : boolean
True boxes result as an Index-like, False returns an ndarray
tz : object
None or pytz timezone object
name : string, default None
Name for a DatetimeIndex
Returns
-------
tz_result : ndarray of parsed dates with timezone
Returns:
- Index-like if box=True
- ndarray of Timestamps if box=False
"""
if tz is not None:
raise ValueError("Cannot pass a tz argument when "
"parsing strings with timezone "
"information.")
tz_results = np.array([Timestamp(res).tz_localize(zone) for res, zone
in zip(result, timezones)])
if box:
from pandas import Index
return Index(tz_results, name=name)
return tz_results |
Helper function for to_datetime. Performs the conversions of 1D listlike
of dates
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be parced
box : boolean
True boxes result as an Index-like, False returns an ndarray
name : object
None or string for the Index name
tz : object
None or 'utc'
unit : string
None or string of the frequency of the passed data
errors : string
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
infer_datetime_format : boolean
inferring format behavior from to_datetime
dayfirst : boolean
dayfirst parsing behavior from to_datetime
yearfirst : boolean
yearfirst parsing behavior from to_datetime
exact : boolean
exact format matching behavior from to_datetime
Returns
-------
ndarray of parsed dates
Returns:
- Index-like if box=True
- ndarray of Timestamps if box=False | def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
unit=None, errors=None,
infer_datetime_format=None, dayfirst=None,
yearfirst=None, exact=None):
"""
Helper function for to_datetime. Performs the conversions of 1D listlike
of dates
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be parced
box : boolean
True boxes result as an Index-like, False returns an ndarray
name : object
None or string for the Index name
tz : object
None or 'utc'
unit : string
None or string of the frequency of the passed data
errors : string
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
infer_datetime_format : boolean
inferring format behavior from to_datetime
dayfirst : boolean
dayfirst parsing behavior from to_datetime
yearfirst : boolean
yearfirst parsing behavior from to_datetime
exact : boolean
exact format matching behavior from to_datetime
Returns
-------
ndarray of parsed dates
Returns:
- Index-like if box=True
- ndarray of Timestamps if box=False
"""
from pandas import DatetimeIndex
from pandas.core.arrays import DatetimeArray
from pandas.core.arrays.datetimes import (
maybe_convert_dtype, objects_to_datetime64ns)
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if is_datetime64tz_dtype(arg):
if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
return DatetimeIndex(arg, tz=tz, name=name)
if tz == 'utc':
arg = arg.tz_convert(None).tz_localize(tz)
return arg
elif is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, (DatetimeArray, DatetimeIndex)):
try:
return DatetimeIndex(arg, tz=tz, name=name)
except ValueError:
pass
return arg
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
arg = getattr(arg, 'values', arg)
result = tslib.array_with_unit_to_datetime(arg, unit,
errors=errors)
if box:
if errors == 'ignore':
from pandas import Index
result = Index(result, name=name)
# GH 23758: We may still need to localize the result with tz
try:
return result.tz_localize(tz)
except AttributeError:
return result
return DatetimeIndex(result, tz=tz, name=name)
return result
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
# warn if passing timedelta64, raise for PeriodDtype
# NB: this must come after unit transformation
orig_arg = arg
arg, _ = maybe_convert_dtype(arg, copy=False)
arg = ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = _format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
tz_parsed = None
result = None
if format is not None:
try:
# shortcut formatting here
if format == '%Y%m%d':
try:
# pass orig_arg as float-dtype may have been converted to
# datetime64[ns]
orig_arg = ensure_object(orig_arg)
result = _attempt_YYYYMMDD(orig_arg, errors=errors)
except (ValueError, TypeError, tslibs.OutOfBoundsDatetime):
raise ValueError("cannot convert the input to "
"'%Y%m%d' date format")
# fallback
if result is None:
try:
result, timezones = array_strptime(
arg, format, exact=exact, errors=errors)
if '%Z' in format or '%z' in format:
return _return_parsed_timezone_results(
result, timezones, box, tz, name)
except tslibs.OutOfBoundsDatetime:
if errors == 'raise':
raise
elif errors == 'coerce':
result = np.empty(arg.shape, dtype='M8[ns]')
iresult = result.view('i8')
iresult.fill(tslibs.iNaT)
else:
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
elif errors == 'coerce':
result = np.empty(arg.shape, dtype='M8[ns]')
iresult = result.view('i8')
iresult.fill(tslibs.iNaT)
else:
result = arg
except ValueError as e:
# Fallback to try to convert datetime objects if timezone-aware
# datetime objects are found without passing `utc=True`
try:
values, tz = conversion.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
if result is None:
assert format is None or infer_datetime_format
utc = tz == 'utc'
result, tz_parsed = objects_to_datetime64ns(
arg, dayfirst=dayfirst, yearfirst=yearfirst,
utc=utc, errors=errors, require_iso8601=require_iso8601,
allow_object=True)
if tz_parsed is not None:
if box:
# We can take a shortcut since the datetime64 numpy array
# is in UTC
return DatetimeIndex._simple_new(result, name=name,
tz=tz_parsed)
else:
# Convert the datetime64 numpy array to an numpy array
# of datetime objects
result = [Timestamp(ts, tz=tz_parsed).to_pydatetime()
for ts in result]
return np.array(result, dtype=object)
if box:
# Ensure we return an Index in all cases where box=True
if is_datetime64_dtype(result):
return DatetimeIndex(result, tz=tz, name=name)
elif is_object_dtype(result):
# e.g. an Index of datetime objects
from pandas import Index
return Index(result, name=name)
return result |
Helper function for to_datetime.
Adjust input argument to the specified origin
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be adjusted
origin : 'julian' or Timestamp
origin offset for the arg
unit : string
passed unit from to_datetime, must be 'D'
Returns
-------
ndarray or scalar of adjusted date(s) | def _adjust_to_origin(arg, origin, unit):
"""
Helper function for to_datetime.
Adjust input argument to the specified origin
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be adjusted
origin : 'julian' or Timestamp
origin offset for the arg
unit : string
passed unit from to_datetime, must be 'D'
Returns
-------
ndarray or scalar of adjusted date(s)
"""
if origin == 'julian':
original = arg
j0 = Timestamp(0).to_julian_date()
if unit != 'D':
raise ValueError("unit must be 'D' for origin='julian'")
try:
arg = arg - j0
except TypeError:
raise ValueError("incompatible 'arg' type for given "
"'origin'='julian'")
# premptively check this for a nice range
j_max = Timestamp.max.to_julian_date() - j0
j_min = Timestamp.min.to_julian_date() - j0
if np.any(arg > j_max) or np.any(arg < j_min):
raise tslibs.OutOfBoundsDatetime(
"{original} is Out of Bounds for "
"origin='julian'".format(original=original))
else:
# arg must be numeric
if not ((is_scalar(arg) and (is_integer(arg) or is_float(arg))) or
is_numeric_dtype(np.asarray(arg))):
raise ValueError(
"'{arg}' is not compatible with origin='{origin}'; "
"it must be numeric with a unit specified ".format(
arg=arg,
origin=origin))
# we are going to offset back to unix / epoch time
try:
offset = Timestamp(origin)
except tslibs.OutOfBoundsDatetime:
raise tslibs.OutOfBoundsDatetime(
"origin {origin} is Out of Bounds".format(origin=origin))
except ValueError:
raise ValueError("origin {origin} cannot be converted "
"to a Timestamp".format(origin=origin))
if offset.tz is not None:
raise ValueError(
"origin offset {} must be tz-naive".format(offset))
offset -= Timestamp(0)
# convert the offset to the unit of the arg
# this should be lossless in terms of precision
offset = offset // tslibs.Timedelta(1, unit=unit)
# scalars & ndarray-like can handle the addition
if is_list_like(arg) and not isinstance(
arg, (ABCSeries, ABCIndexClass, np.ndarray)):
arg = np.asarray(arg)
arg = arg + offset
return arg |
Convert argument to datetime.
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
.. versionadded:: 0.18.1
or DataFrame/dict-like
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
dayfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
If True, parses dates with the day first, eg 10/11/12 is parsed as
2012-11-10.
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug, based on dateutil behavior).
yearfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
- If True parses dates with the year first, eg 10/11/12 is parsed as
2010-11-12.
- If both dayfirst and yearfirst are True, yearfirst is preceded (same
as dateutil).
Warning: yearfirst=True is not strict, but will prefer to parse
with year first (this is a known bug, based on dateutil behavior).
.. versionadded:: 0.16.1
utc : boolean, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well).
box : boolean, default True
- If True returns a DatetimeIndex or Index-like object
- If False returns ndarray of values.
.. deprecated:: 0.25.0
Use :meth:`.to_numpy` or :meth:`Timestamp.to_datetime64`
instead to get an ndarray of values or numpy.datetime64,
respectively.
format : string, default None
strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
See strftime documentation for more information on choices:
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
exact : boolean, True by default
- If True, require an exact format match.
- If False, allow the format to match anywhere in the target string.
unit : string, default 'ns'
unit of the arg (D,s,ms,us,ns) denote the unit, which is an
integer or float number. This will be based off the origin.
Example, with unit='ms' and origin='unix' (the default), this
would calculate the number of milliseconds to the unix epoch start.
infer_datetime_format : boolean, default False
If True and no `format` is given, attempt to infer the format of the
datetime strings, and if it can be inferred, switch to a faster
method of parsing them. In some cases this can increase the parsing
speed by ~5-10x.
origin : scalar, default is 'unix'
Define the reference date. The numeric values would be parsed as number
of units (defined by `unit`) since this reference date.
- If 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 0 is assigned to the day starting
at noon on January 1, 4713 BC.
- If Timestamp convertible, origin is set to Timestamp identified by
origin.
.. versionadded:: 0.20.0
cache : boolean, default False
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.23.0
Returns
-------
ret : datetime if parsing succeeded.
Return type depends on input:
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_timedelta : Convert argument to timedelta.
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5]})
>>> pd.to_datetime(df)
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
If a date does not meet the `timestamp limitations
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html
#timeseries-timestamp-limits>`_, passing errors='ignore'
will return the original input instead of raising any exception.
Passing errors='coerce' will force an out-of-bounds date to NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
datetime.datetime(1300, 1, 1, 0, 0)
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
NaT
Passing infer_datetime_format=True can often-times speedup a parsing
if its not an ISO8601 format exactly, but in a regular format.
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000)
>>> s.head()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
>>> %timeit pd.to_datetime(s,infer_datetime_format=True)
100 loops, best of 3: 10.4 ms per loop
>>> %timeit pd.to_datetime(s,infer_datetime_format=False)
1 loop, best of 3: 471 ms per loop
Using a unix epoch time
>>> pd.to_datetime(1490195805, unit='s')
Timestamp('2017-03-22 15:16:45')
>>> pd.to_datetime(1490195805433502912, unit='ns')
Timestamp('2017-03-22 15:16:45.433502912')
.. warning:: For float arg, precision rounding might happen. To prevent
unexpected behavior use a fixed-width exact type.
Using a non-unix epoch origin
>>> pd.to_datetime([1, 2, 3], unit='D',
origin=pd.Timestamp('1960-01-01'))
0 1960-01-02
1 1960-01-03
2 1960-01-04 | def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
utc=None, box=True, format=None, exact=True,
unit=None, infer_datetime_format=False, origin='unix',
cache=False):
"""
Convert argument to datetime.
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
.. versionadded:: 0.18.1
or DataFrame/dict-like
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
dayfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
If True, parses dates with the day first, eg 10/11/12 is parsed as
2012-11-10.
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug, based on dateutil behavior).
yearfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
- If True parses dates with the year first, eg 10/11/12 is parsed as
2010-11-12.
- If both dayfirst and yearfirst are True, yearfirst is preceded (same
as dateutil).
Warning: yearfirst=True is not strict, but will prefer to parse
with year first (this is a known bug, based on dateutil behavior).
.. versionadded:: 0.16.1
utc : boolean, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well).
box : boolean, default True
- If True returns a DatetimeIndex or Index-like object
- If False returns ndarray of values.
.. deprecated:: 0.25.0
Use :meth:`.to_numpy` or :meth:`Timestamp.to_datetime64`
instead to get an ndarray of values or numpy.datetime64,
respectively.
format : string, default None
strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
See strftime documentation for more information on choices:
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
exact : boolean, True by default
- If True, require an exact format match.
- If False, allow the format to match anywhere in the target string.
unit : string, default 'ns'
unit of the arg (D,s,ms,us,ns) denote the unit, which is an
integer or float number. This will be based off the origin.
Example, with unit='ms' and origin='unix' (the default), this
would calculate the number of milliseconds to the unix epoch start.
infer_datetime_format : boolean, default False
If True and no `format` is given, attempt to infer the format of the
datetime strings, and if it can be inferred, switch to a faster
method of parsing them. In some cases this can increase the parsing
speed by ~5-10x.
origin : scalar, default is 'unix'
Define the reference date. The numeric values would be parsed as number
of units (defined by `unit`) since this reference date.
- If 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 0 is assigned to the day starting
at noon on January 1, 4713 BC.
- If Timestamp convertible, origin is set to Timestamp identified by
origin.
.. versionadded:: 0.20.0
cache : boolean, default False
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.23.0
Returns
-------
ret : datetime if parsing succeeded.
Return type depends on input:
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_timedelta : Convert argument to timedelta.
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5]})
>>> pd.to_datetime(df)
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
If a date does not meet the `timestamp limitations
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html
#timeseries-timestamp-limits>`_, passing errors='ignore'
will return the original input instead of raising any exception.
Passing errors='coerce' will force an out-of-bounds date to NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
datetime.datetime(1300, 1, 1, 0, 0)
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
NaT
Passing infer_datetime_format=True can often-times speedup a parsing
if its not an ISO8601 format exactly, but in a regular format.
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000)
>>> s.head()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
>>> %timeit pd.to_datetime(s,infer_datetime_format=True)
100 loops, best of 3: 10.4 ms per loop
>>> %timeit pd.to_datetime(s,infer_datetime_format=False)
1 loop, best of 3: 471 ms per loop
Using a unix epoch time
>>> pd.to_datetime(1490195805, unit='s')
Timestamp('2017-03-22 15:16:45')
>>> pd.to_datetime(1490195805433502912, unit='ns')
Timestamp('2017-03-22 15:16:45.433502912')
.. warning:: For float arg, precision rounding might happen. To prevent
unexpected behavior use a fixed-width exact type.
Using a non-unix epoch origin
>>> pd.to_datetime([1, 2, 3], unit='D',
origin=pd.Timestamp('1960-01-01'))
0 1960-01-02
1 1960-01-03
2 1960-01-04
"""
if arg is None:
return None
if origin != 'unix':
arg = _adjust_to_origin(arg, origin, unit)
tz = 'utc' if utc else None
convert_listlike = partial(_convert_listlike_datetimes, tz=tz, unit=unit,
dayfirst=dayfirst, yearfirst=yearfirst,
errors=errors, exact=exact,
infer_datetime_format=infer_datetime_format)
if isinstance(arg, Timestamp):
result = arg
if tz is not None:
if arg.tz is not None:
result = result.tz_convert(tz)
else:
result = result.tz_localize(tz)
elif isinstance(arg, ABCSeries):
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
if not cache_array.empty:
result = arg.map(cache_array)
else:
values = convert_listlike(arg._values, True, format)
result = arg._constructor(values, index=arg.index, name=arg.name)
elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
result = _assemble_from_unit_mappings(arg, errors, box, tz)
elif isinstance(arg, ABCIndexClass):
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
if not cache_array.empty:
result = _convert_and_box_cache(arg, cache_array, box, errors,
name=arg.name)
else:
convert_listlike = partial(convert_listlike, name=arg.name)
result = convert_listlike(arg, box, format)
elif is_list_like(arg):
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
if not cache_array.empty:
result = _convert_and_box_cache(arg, cache_array, box, errors)
else:
result = convert_listlike(arg, box, format)
else:
result = convert_listlike(np.array([arg]), box, format)[0]
return result |
assemble the unit specified fields from the arg (DataFrame)
Return a Series for actual parsing
Parameters
----------
arg : DataFrame
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
box : boolean
- If True, return a DatetimeIndex
- If False, return an array
tz : None or 'utc'
Returns
-------
Series | def _assemble_from_unit_mappings(arg, errors, box, tz):
"""
assemble the unit specified fields from the arg (DataFrame)
Return a Series for actual parsing
Parameters
----------
arg : DataFrame
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
box : boolean
- If True, return a DatetimeIndex
- If False, return an array
tz : None or 'utc'
Returns
-------
Series
"""
from pandas import to_timedelta, to_numeric, DataFrame
arg = DataFrame(arg)
if not arg.columns.is_unique:
raise ValueError("cannot assemble with duplicate keys")
# replace passed unit with _unit_map
def f(value):
if value in _unit_map:
return _unit_map[value]
# m is case significant
if value.lower() in _unit_map:
return _unit_map[value.lower()]
return value
unit = {k: f(k) for k in arg.keys()}
unit_rev = {v: k for k, v in unit.items()}
# we require at least Ymd
required = ['year', 'month', 'day']
req = sorted(list(set(required) - set(unit_rev.keys())))
if len(req):
raise ValueError("to assemble mappings requires at least that "
"[year, month, day] be specified: [{required}] "
"is missing".format(required=','.join(req)))
# keys we don't recognize
excess = sorted(list(set(unit_rev.keys()) - set(_unit_map.values())))
if len(excess):
raise ValueError("extra keys have been passed "
"to the datetime assemblage: "
"[{excess}]".format(excess=','.join(excess)))
def coerce(values):
# we allow coercion to if errors allows
values = to_numeric(values, errors=errors)
# prevent overflow in case of int8 or int16
if is_integer_dtype(values):
values = values.astype('int64', copy=False)
return values
values = (coerce(arg[unit_rev['year']]) * 10000 +
coerce(arg[unit_rev['month']]) * 100 +
coerce(arg[unit_rev['day']]))
try:
values = to_datetime(values, format='%Y%m%d', errors=errors, utc=tz)
except (TypeError, ValueError) as e:
raise ValueError("cannot assemble the "
"datetimes: {error}".format(error=e))
for u in ['h', 'm', 's', 'ms', 'us', 'ns']:
value = unit_rev.get(u)
if value is not None and value in arg:
try:
values += to_timedelta(coerce(arg[value]),
unit=u,
errors=errors)
except (TypeError, ValueError) as e:
raise ValueError("cannot assemble the datetimes [{value}]: "
"{error}".format(value=value, error=e))
if not box:
return values.values
return values |
try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
arg is a passed in as an object dtype, but could really be ints/strings
with nan-like/or floats (e.g. with nan)
Parameters
----------
arg : passed value
errors : 'raise','ignore','coerce' | def _attempt_YYYYMMDD(arg, errors):
"""
try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
arg is a passed in as an object dtype, but could really be ints/strings
with nan-like/or floats (e.g. with nan)
Parameters
----------
arg : passed value
errors : 'raise','ignore','coerce'
"""
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
parsed = parsing.try_parse_year_month_day(carg / 10000,
carg / 100 % 100,
carg % 100)
return tslib.array_to_datetime(parsed, errors=errors)[0]
def calc_with_mask(carg, mask):
result = np.empty(carg.shape, dtype='M8[ns]')
iresult = result.view('i8')
iresult[~mask] = tslibs.iNaT
masked_result = calc(carg[mask].astype(np.float64).astype(np.int64))
result[mask] = masked_result.astype('M8[ns]')
return result
# try intlike / strings that are ints
try:
return calc(arg.astype(np.int64))
except ValueError:
pass
# a float with actual np.nan
try:
carg = arg.astype(np.float64)
return calc_with_mask(carg, notna(carg))
except ValueError:
pass
# string with NaN-like
try:
mask = ~algorithms.isin(arg, list(tslib.nat_strings))
return calc_with_mask(arg, mask)
except ValueError:
pass
return None |
Parse time strings to time objects using fixed strptime formats ("%H:%M",
"%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
"%I%M%S%p")
Use infer_time_format if all the strings are in the same format to speed
up conversion.
Parameters
----------
arg : string in time format, datetime.time, list, tuple, 1-d array, Series
format : str, default None
Format used to convert arg into a time object. If None, fixed formats
are used.
infer_time_format: bool, default False
Infer the time format based on the first non-NaN element. If all
strings are in the same format, this will speed up conversion.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as None
- If 'ignore', then invalid parsing will return the input
Returns
-------
datetime.time | def to_time(arg, format=None, infer_time_format=False, errors='raise'):
"""
Parse time strings to time objects using fixed strptime formats ("%H:%M",
"%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
"%I%M%S%p")
Use infer_time_format if all the strings are in the same format to speed
up conversion.
Parameters
----------
arg : string in time format, datetime.time, list, tuple, 1-d array, Series
format : str, default None
Format used to convert arg into a time object. If None, fixed formats
are used.
infer_time_format: bool, default False
Infer the time format based on the first non-NaN element. If all
strings are in the same format, this will speed up conversion.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as None
- If 'ignore', then invalid parsing will return the input
Returns
-------
datetime.time
"""
def _convert_listlike(arg, format):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = ensure_object(arg)
if infer_time_format and format is None:
format = _guess_time_format_for_array(arg)
times = []
if format is not None:
for element in arg:
try:
times.append(datetime.strptime(element, format).time())
except (ValueError, TypeError):
if errors == 'raise':
msg = ("Cannot convert {element} to a time with given "
"format {format}").format(element=element,
format=format)
raise ValueError(msg)
elif errors == 'ignore':
return arg
else:
times.append(None)
else:
formats = _time_formats[:]
format_found = False
for element in arg:
time_object = None
for time_format in formats:
try:
time_object = datetime.strptime(element,
time_format).time()
if not format_found:
# Put the found format in front
fmt = formats.pop(formats.index(time_format))
formats.insert(0, fmt)
format_found = True
break
except (ValueError, TypeError):
continue
if time_object is not None:
times.append(time_object)
elif errors == 'raise':
raise ValueError("Cannot convert arg {arg} to "
"a time".format(arg=arg))
elif errors == 'ignore':
return arg
else:
times.append(None)
return times
if arg is None:
return arg
elif isinstance(arg, time):
return arg
elif isinstance(arg, ABCSeries):
values = _convert_listlike(arg._values, format)
return arg._constructor(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, format)
elif is_list_like(arg):
return _convert_listlike(arg, format)
return _convert_listlike(np.array([arg]), format)[0] |
Return a new function that emits a deprecation warning on use.
To use this method for a deprecated function, another function
`alternative` with the same signature must exist. The deprecated
function will emit a deprecation warning, and in the docstring
it will contain the deprecation directive with the provided version
so it can be detected for future removal.
Parameters
----------
name : str
Name of function to deprecate.
alternative : func
Function to use instead.
version : str
Version of pandas in which the method has been deprecated.
alt_name : str, optional
Name to use in preference of alternative.__name__.
klass : Warning, default FutureWarning
stacklevel : int, default 2
msg : str
The message to display in the warning.
Default is '{name} is deprecated. Use {alt_name} instead.' | def deprecate(name, alternative, version, alt_name=None,
klass=None, stacklevel=2, msg=None):
"""
Return a new function that emits a deprecation warning on use.
To use this method for a deprecated function, another function
`alternative` with the same signature must exist. The deprecated
function will emit a deprecation warning, and in the docstring
it will contain the deprecation directive with the provided version
so it can be detected for future removal.
Parameters
----------
name : str
Name of function to deprecate.
alternative : func
Function to use instead.
version : str
Version of pandas in which the method has been deprecated.
alt_name : str, optional
Name to use in preference of alternative.__name__.
klass : Warning, default FutureWarning
stacklevel : int, default 2
msg : str
The message to display in the warning.
Default is '{name} is deprecated. Use {alt_name} instead.'
"""
alt_name = alt_name or alternative.__name__
klass = klass or FutureWarning
warning_msg = msg or '{} is deprecated, use {} instead'.format(name,
alt_name)
@wraps(alternative)
def wrapper(*args, **kwargs):
warnings.warn(warning_msg, klass, stacklevel=stacklevel)
return alternative(*args, **kwargs)
# adding deprecated directive to the docstring
msg = msg or 'Use `{alt_name}` instead.'.format(alt_name=alt_name)
doc_error_msg = ('deprecate needs a correctly formatted docstring in '
'the target function (should have a one liner short '
'summary, and opening quotes should be in their own '
'line). Found:\n{}'.format(alternative.__doc__))
# when python is running in optimized mode (i.e. `-OO`), docstrings are
# removed, so we check that a docstring with correct formatting is used
# but we allow empty docstrings
if alternative.__doc__:
if alternative.__doc__.count('\n') < 3:
raise AssertionError(doc_error_msg)
empty1, summary, empty2, doc = alternative.__doc__.split('\n', 3)
if empty1 or empty2 and not summary:
raise AssertionError(doc_error_msg)
wrapper.__doc__ = dedent("""
{summary}
.. deprecated:: {depr_version}
{depr_msg}
{rest_of_docstring}""").format(summary=summary.strip(),
depr_version=version,
depr_msg=msg,
rest_of_docstring=dedent(doc))
return wrapper |
Decorator to deprecate a keyword argument of a function.
Parameters
----------
old_arg_name : str
Name of argument in function to deprecate
new_arg_name : str or None
Name of preferred argument in function. Use None to raise warning that
``old_arg_name`` keyword is deprecated.
mapping : dict or callable
If mapping is present, use it to translate old arguments to
new arguments. A callable must do its own value checking;
values not found in a dict will be forwarded unchanged.
Examples
--------
The following deprecates 'cols', using 'columns' instead
>>> @deprecate_kwarg(old_arg_name='cols', new_arg_name='columns')
... def f(columns=''):
... print(columns)
...
>>> f(columns='should work ok')
should work ok
>>> f(cols='should raise warning')
FutureWarning: cols is deprecated, use columns instead
warnings.warn(msg, FutureWarning)
should raise warning
>>> f(cols='should error', columns="can\'t pass do both")
TypeError: Can only specify 'cols' or 'columns', not both
>>> @deprecate_kwarg('old', 'new', {'yes': True, 'no': False})
... def f(new=False):
... print('yes!' if new else 'no!')
...
>>> f(old='yes')
FutureWarning: old='yes' is deprecated, use new=True instead
warnings.warn(msg, FutureWarning)
yes!
To raise a warning that a keyword will be removed entirely in the future
>>> @deprecate_kwarg(old_arg_name='cols', new_arg_name=None)
... def f(cols='', another_param=''):
... print(cols)
...
>>> f(cols='should raise warning')
FutureWarning: the 'cols' keyword is deprecated and will be removed in a
future version please takes steps to stop use of 'cols'
should raise warning
>>> f(another_param='should not raise warning')
should not raise warning
>>> f(cols='should raise warning', another_param='')
FutureWarning: the 'cols' keyword is deprecated and will be removed in a
future version please takes steps to stop use of 'cols'
should raise warning | def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, stacklevel=2):
"""
Decorator to deprecate a keyword argument of a function.
Parameters
----------
old_arg_name : str
Name of argument in function to deprecate
new_arg_name : str or None
Name of preferred argument in function. Use None to raise warning that
``old_arg_name`` keyword is deprecated.
mapping : dict or callable
If mapping is present, use it to translate old arguments to
new arguments. A callable must do its own value checking;
values not found in a dict will be forwarded unchanged.
Examples
--------
The following deprecates 'cols', using 'columns' instead
>>> @deprecate_kwarg(old_arg_name='cols', new_arg_name='columns')
... def f(columns=''):
... print(columns)
...
>>> f(columns='should work ok')
should work ok
>>> f(cols='should raise warning')
FutureWarning: cols is deprecated, use columns instead
warnings.warn(msg, FutureWarning)
should raise warning
>>> f(cols='should error', columns="can\'t pass do both")
TypeError: Can only specify 'cols' or 'columns', not both
>>> @deprecate_kwarg('old', 'new', {'yes': True, 'no': False})
... def f(new=False):
... print('yes!' if new else 'no!')
...
>>> f(old='yes')
FutureWarning: old='yes' is deprecated, use new=True instead
warnings.warn(msg, FutureWarning)
yes!
To raise a warning that a keyword will be removed entirely in the future
>>> @deprecate_kwarg(old_arg_name='cols', new_arg_name=None)
... def f(cols='', another_param=''):
... print(cols)
...
>>> f(cols='should raise warning')
FutureWarning: the 'cols' keyword is deprecated and will be removed in a
future version please takes steps to stop use of 'cols'
should raise warning
>>> f(another_param='should not raise warning')
should not raise warning
>>> f(cols='should raise warning', another_param='')
FutureWarning: the 'cols' keyword is deprecated and will be removed in a
future version please takes steps to stop use of 'cols'
should raise warning
"""
if mapping is not None and not hasattr(mapping, 'get') and \
not callable(mapping):
raise TypeError("mapping from old to new argument values "
"must be dict or callable!")
def _deprecate_kwarg(func):
@wraps(func)
def wrapper(*args, **kwargs):
old_arg_value = kwargs.pop(old_arg_name, None)
if new_arg_name is None and old_arg_value is not None:
msg = (
"the '{old_name}' keyword is deprecated and will be "
"removed in a future version. "
"Please take steps to stop the use of '{old_name}'"
).format(old_name=old_arg_name)
warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
kwargs[old_arg_name] = old_arg_value
return func(*args, **kwargs)
if old_arg_value is not None:
if mapping is not None:
if hasattr(mapping, 'get'):
new_arg_value = mapping.get(old_arg_value,
old_arg_value)
else:
new_arg_value = mapping(old_arg_value)
msg = ("the {old_name}={old_val!r} keyword is deprecated, "
"use {new_name}={new_val!r} instead"
).format(old_name=old_arg_name,
old_val=old_arg_value,
new_name=new_arg_name,
new_val=new_arg_value)
else:
new_arg_value = old_arg_value
msg = ("the '{old_name}' keyword is deprecated, "
"use '{new_name}' instead"
).format(old_name=old_arg_name,
new_name=new_arg_name)
warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
if kwargs.get(new_arg_name, None) is not None:
msg = ("Can only specify '{old_name}' or '{new_name}', "
"not both").format(old_name=old_arg_name,
new_name=new_arg_name)
raise TypeError(msg)
else:
kwargs[new_arg_name] = new_arg_value
return func(*args, **kwargs)
return wrapper
return _deprecate_kwarg |
Returns a tuple containing the paramenter list with defaults
and parameter list.
Examples
--------
>>> def f(a, b, c=2):
>>> return a * b * c
>>> print(make_signature(f))
(['a', 'b', 'c=2'], ['a', 'b', 'c']) | def make_signature(func):
"""
Returns a tuple containing the paramenter list with defaults
and parameter list.
Examples
--------
>>> def f(a, b, c=2):
>>> return a * b * c
>>> print(make_signature(f))
(['a', 'b', 'c=2'], ['a', 'b', 'c'])
"""
spec = inspect.getfullargspec(func)
if spec.defaults is None:
n_wo_defaults = len(spec.args)
defaults = ('',) * n_wo_defaults
else:
n_wo_defaults = len(spec.args) - len(spec.defaults)
defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
args = []
for var, default in zip(spec.args, defaults):
args.append(var if default == '' else var + '=' + repr(default))
if spec.varargs:
args.append('*' + spec.varargs)
if spec.varkw:
args.append('**' + spec.varkw)
return args, spec.args |
Return a fixed frequency PeriodIndex, with day (calendar) as the default
frequency
Parameters
----------
start : string or period-like, default None
Left bound for generating periods
end : string or period-like, default None
Right bound for generating periods
periods : integer, default None
Number of periods to generate
freq : string or DateOffset, optional
Frequency alias. By default the freq is taken from `start` or `end`
if those are Period objects. Otherwise, the default is ``"D"`` for
daily frequency.
name : string, default None
Name of the resulting PeriodIndex
Returns
-------
prng : PeriodIndex
Notes
-----
Of the three parameters: ``start``, ``end``, and ``periods``, exactly two
must be specified.
To learn more about the frequency strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
Examples
--------
>>> pd.period_range(start='2017-01-01', end='2018-01-01', freq='M')
PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05',
'2017-06', '2017-06', '2017-07', '2017-08', '2017-09',
'2017-10', '2017-11', '2017-12', '2018-01'],
dtype='period[M]', freq='M')
If ``start`` or ``end`` are ``Period`` objects, they will be used as anchor
endpoints for a ``PeriodIndex`` with frequency matching that of the
``period_range`` constructor.
>>> pd.period_range(start=pd.Period('2017Q1', freq='Q'),
... end=pd.Period('2017Q2', freq='Q'), freq='M')
PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'],
dtype='period[M]', freq='M') | def period_range(start=None, end=None, periods=None, freq=None, name=None):
"""
Return a fixed frequency PeriodIndex, with day (calendar) as the default
frequency
Parameters
----------
start : string or period-like, default None
Left bound for generating periods
end : string or period-like, default None
Right bound for generating periods
periods : integer, default None
Number of periods to generate
freq : string or DateOffset, optional
Frequency alias. By default the freq is taken from `start` or `end`
if those are Period objects. Otherwise, the default is ``"D"`` for
daily frequency.
name : string, default None
Name of the resulting PeriodIndex
Returns
-------
prng : PeriodIndex
Notes
-----
Of the three parameters: ``start``, ``end``, and ``periods``, exactly two
must be specified.
To learn more about the frequency strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
Examples
--------
>>> pd.period_range(start='2017-01-01', end='2018-01-01', freq='M')
PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05',
'2017-06', '2017-06', '2017-07', '2017-08', '2017-09',
'2017-10', '2017-11', '2017-12', '2018-01'],
dtype='period[M]', freq='M')
If ``start`` or ``end`` are ``Period`` objects, they will be used as anchor
endpoints for a ``PeriodIndex`` with frequency matching that of the
``period_range`` constructor.
>>> pd.period_range(start=pd.Period('2017Q1', freq='Q'),
... end=pd.Period('2017Q2', freq='Q'), freq='M')
PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'],
dtype='period[M]', freq='M')
"""
if com.count_not_none(start, end, periods) != 2:
raise ValueError('Of the three parameters: start, end, and periods, '
'exactly two must be specified')
if freq is None and (not isinstance(start, Period)
and not isinstance(end, Period)):
freq = 'D'
data, freq = PeriodArray._generate_range(start, end, periods, freq,
fields={})
data = PeriodArray(data, freq=freq)
return PeriodIndex(data, name=name) |
Create RangeIndex from a range object. | def from_range(cls, data, name=None, dtype=None, **kwargs):
""" Create RangeIndex from a range object. """
if not isinstance(data, range):
raise TypeError(
'{0}(...) must be called with object coercible to a '
'range, {1} was passed'.format(cls.__name__, repr(data)))
start, stop, step = data.start, data.stop, data.step
return RangeIndex(start, stop, step, dtype=dtype, name=name, **kwargs) |
Return a list of tuples of the (attr, formatted_value) | def _format_attrs(self):
"""
Return a list of tuples of the (attr, formatted_value)
"""
attrs = self._get_data_as_items()
if self.name is not None:
attrs.append(('name', ibase.default_pprint(self.name)))
return attrs |
The minimum value of the RangeIndex | def min(self, axis=None, skipna=True):
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
return self._minmax('min') |
Returns the indices that would sort the index and its
underlying data.
Returns
-------
argsorted : numpy array
See Also
--------
numpy.ndarray.argsort | def argsort(self, *args, **kwargs):
"""
Returns the indices that would sort the index and its
underlying data.
Returns
-------
argsorted : numpy array
See Also
--------
numpy.ndarray.argsort
"""
nv.validate_argsort(args, kwargs)
if self._step > 0:
return np.arange(len(self))
else:
return np.arange(len(self) - 1, -1, -1) |
Determines if two Index objects contain the same elements. | def equals(self, other):
"""
Determines if two Index objects contain the same elements.
"""
if isinstance(other, RangeIndex):
ls = len(self)
lo = len(other)
return (ls == lo == 0 or
ls == lo == 1 and
self._start == other._start or
ls == lo and
self._start == other._start and
self._step == other._step)
return super().equals(other) |
Form the intersection of two Index objects.
Parameters
----------
other : Index or array-like
sort : False or None, default False
Sort the resulting index if possible
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default to ``False`` to match the behaviour
from before 0.24.0.
Returns
-------
intersection : Index | def intersection(self, other, sort=False):
"""
Form the intersection of two Index objects.
Parameters
----------
other : Index or array-like
sort : False or None, default False
Sort the resulting index if possible
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default to ``False`` to match the behaviour
from before 0.24.0.
Returns
-------
intersection : Index
"""
self._validate_sort_keyword(sort)
if self.equals(other):
return self._get_reconciled_name_object(other)
if not isinstance(other, RangeIndex):
return super().intersection(other, sort=sort)
if not len(self) or not len(other):
return RangeIndex._simple_new(None)
first = self[::-1] if self._step < 0 else self
second = other[::-1] if other._step < 0 else other
# check whether intervals intersect
# deals with in- and decreasing ranges
int_low = max(first._start, second._start)
int_high = min(first._stop, second._stop)
if int_high <= int_low:
return RangeIndex._simple_new(None)
# Method hint: linear Diophantine equation
# solve intersection problem
# performance hint: for identical step sizes, could use
# cheaper alternative
gcd, s, t = first._extended_gcd(first._step, second._step)
# check whether element sets intersect
if (first._start - second._start) % gcd:
return RangeIndex._simple_new(None)
# calculate parameters for the RangeIndex describing the
# intersection disregarding the lower bounds
tmp_start = first._start + (second._start - first._start) * \
first._step // gcd * s
new_step = first._step * second._step // gcd
new_index = RangeIndex._simple_new(tmp_start, int_high, new_step)
# adjust index to limiting interval
new_index._start = new_index._min_fitting_element(int_low)
if (self._step < 0 and other._step < 0) is not (new_index._step < 0):
new_index = new_index[::-1]
if sort is None:
new_index = new_index.sort_values()
return new_index |
Returns the smallest element greater than or equal to the limit | def _min_fitting_element(self, lower_limit):
"""Returns the smallest element greater than or equal to the limit"""
no_steps = -(-(lower_limit - self._start) // abs(self._step))
return self._start + abs(self._step) * no_steps |
Returns the largest element smaller than or equal to the limit | def _max_fitting_element(self, upper_limit):
"""Returns the largest element smaller than or equal to the limit"""
no_steps = (upper_limit - self._start) // abs(self._step)
return self._start + abs(self._step) * no_steps |
Extended Euclidean algorithms to solve Bezout's identity:
a*x + b*y = gcd(x, y)
Finds one particular solution for x, y: s, t
Returns: gcd, s, t | def _extended_gcd(self, a, b):
"""
Extended Euclidean algorithms to solve Bezout's identity:
a*x + b*y = gcd(x, y)
Finds one particular solution for x, y: s, t
Returns: gcd, s, t
"""
s, old_s = 0, 1
t, old_t = 1, 0
r, old_r = b, a
while r:
quotient = old_r // r
old_r, r = r, old_r - quotient * r
old_s, s = s, old_s - quotient * s
old_t, t = t, old_t - quotient * t
return old_r, old_s, old_t |
Form the union of two Index objects and sorts if possible
Parameters
----------
other : Index or array-like
sort : False or None, default None
Whether to sort resulting index. ``sort=None`` returns a
mononotically increasing ``RangeIndex`` if possible or a sorted
``Int64Index`` if not. ``sort=False`` always returns an
unsorted ``Int64Index``
.. versionadded:: 0.25.0
Returns
-------
union : Index | def union(self, other, sort=None):
"""
Form the union of two Index objects and sorts if possible
Parameters
----------
other : Index or array-like
sort : False or None, default None
Whether to sort resulting index. ``sort=None`` returns a
mononotically increasing ``RangeIndex`` if possible or a sorted
``Int64Index`` if not. ``sort=False`` always returns an
unsorted ``Int64Index``
.. versionadded:: 0.25.0
Returns
-------
union : Index
"""
self._assert_can_do_setop(other)
if len(other) == 0 or self.equals(other) or len(self) == 0:
return super().union(other, sort=sort)
if isinstance(other, RangeIndex) and sort is None:
start_s, step_s = self._start, self._step
end_s = self._start + self._step * (len(self) - 1)
start_o, step_o = other._start, other._step
end_o = other._start + other._step * (len(other) - 1)
if self._step < 0:
start_s, step_s, end_s = end_s, -step_s, start_s
if other._step < 0:
start_o, step_o, end_o = end_o, -step_o, start_o
if len(self) == 1 and len(other) == 1:
step_s = step_o = abs(self._start - other._start)
elif len(self) == 1:
step_s = step_o
elif len(other) == 1:
step_o = step_s
start_r = min(start_s, start_o)
end_r = max(end_s, end_o)
if step_o == step_s:
if ((start_s - start_o) % step_s == 0 and
(start_s - end_o) <= step_s and
(start_o - end_s) <= step_s):
return RangeIndex(start_r, end_r + step_s, step_s)
if ((step_s % 2 == 0) and
(abs(start_s - start_o) <= step_s / 2) and
(abs(end_s - end_o) <= step_s / 2)):
return RangeIndex(start_r, end_r + step_s / 2, step_s / 2)
elif step_o % step_s == 0:
if ((start_o - start_s) % step_s == 0 and
(start_o + step_s >= start_s) and
(end_o - step_s <= end_s)):
return RangeIndex(start_r, end_r + step_s, step_s)
elif step_s % step_o == 0:
if ((start_s - start_o) % step_o == 0 and
(start_s + step_o >= start_o) and
(end_s - step_o <= end_o)):
return RangeIndex(start_r, end_r + step_o, step_o)
return self._int64index.union(other, sort=sort) |
add in numeric methods, specialized to RangeIndex | def _add_numeric_methods_binary(cls):
""" add in numeric methods, specialized to RangeIndex """
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 parms
perform the binary op
step : callable, optional, default to False
op to apply to the step parm if not None
if False, use the existing step
"""
def _evaluate_numeric_binop(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
elif isinstance(other, ABCTimedeltaIndex):
# Defer to TimedeltaIndex implementation
return NotImplemented
elif isinstance(other, (timedelta, np.timedelta64)):
# GH#19333 is_integer evaluated True on timedelta64,
# so we need to catch these explicitly
return op(self._int64index, other)
elif is_timedelta64_dtype(other):
# Must be an np.ndarray; GH#22390
return op(self._int64index, other)
other = self._validate_for_numeric_binop(other, op)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
left, right = self, other
try:
# apply if we have an override
if step:
with np.errstate(all='ignore'):
rstep = step(left._step, right)
# we don't have a representable op
# so return a base index
if not is_integer(rstep) or not rstep:
raise ValueError
else:
rstep = left._step
with np.errstate(all='ignore'):
rstart = op(left._start, right)
rstop = op(left._stop, right)
result = RangeIndex(rstart,
rstop,
rstep,
**attrs)
# for compat with numpy / Int64Index
# even if we can represent as a RangeIndex, return
# as a Float64Index if we have float-like descriptors
if not all(is_integer(x) for x in
[rstart, rstop, rstep]):
result = result.astype('float64')
return result
except (ValueError, TypeError, ZeroDivisionError):
# Defer to Int64Index implementation
return op(self._int64index, other)
# TODO: Do attrs get handled reliably?
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(_evaluate_numeric_binop, name, cls)
cls.__add__ = _make_evaluate_binop(operator.add)
cls.__radd__ = _make_evaluate_binop(ops.radd)
cls.__sub__ = _make_evaluate_binop(operator.sub)
cls.__rsub__ = _make_evaluate_binop(ops.rsub)
cls.__mul__ = _make_evaluate_binop(operator.mul, step=operator.mul)
cls.__rmul__ = _make_evaluate_binop(ops.rmul, step=ops.rmul)
cls.__truediv__ = _make_evaluate_binop(operator.truediv,
step=operator.truediv)
cls.__rtruediv__ = _make_evaluate_binop(ops.rtruediv,
step=ops.rtruediv) |
Convert the PandasArray to a :class:`numpy.ndarray`.
By default, this requires no coercion or copying of data.
Parameters
----------
dtype : numpy.dtype
The NumPy dtype to pass to :func:`numpy.asarray`.
copy : bool, default False
Whether to copy the underlying data.
Returns
-------
ndarray | def to_numpy(self, dtype=None, copy=False):
"""
Convert the PandasArray to a :class:`numpy.ndarray`.
By default, this requires no coercion or copying of data.
Parameters
----------
dtype : numpy.dtype
The NumPy dtype to pass to :func:`numpy.asarray`.
copy : bool, default False
Whether to copy the underlying data.
Returns
-------
ndarray
"""
result = np.asarray(self._ndarray, dtype=dtype)
if copy and result is self._ndarray:
result = result.copy()
return result |
Glues together two sets of strings using the amount of space requested.
The idea is to prettify.
----------
space : int
number of spaces for padding
lists : str
list of str which being joined
strlen : callable
function used to calculate the length of each str. Needed for unicode
handling.
justfunc : callable
function used to justify str. Needed for unicode handling. | def adjoin(space, *lists, **kwargs):
"""
Glues together two sets of strings using the amount of space requested.
The idea is to prettify.
----------
space : int
number of spaces for padding
lists : str
list of str which being joined
strlen : callable
function used to calculate the length of each str. Needed for unicode
handling.
justfunc : callable
function used to justify str. Needed for unicode handling.
"""
strlen = kwargs.pop('strlen', len)
justfunc = kwargs.pop('justfunc', justify)
out_lines = []
newLists = []
lengths = [max(map(strlen, x)) + space for x in lists[:-1]]
# not the last one
lengths.append(max(map(len, lists[-1])))
maxLen = max(map(len, lists))
for i, lst in enumerate(lists):
nl = justfunc(lst, lengths[i], mode='left')
nl.extend([' ' * lengths[i]] * (maxLen - len(lst)))
newLists.append(nl)
toJoin = zip(*newLists)
for lines in toJoin:
out_lines.append(_join_unicode(lines))
return _join_unicode(out_lines, sep='\n') |
Perform ljust, center, rjust against string or list-like | def justify(texts, max_len, mode='right'):
"""
Perform ljust, center, rjust against string or list-like
"""
if mode == 'left':
return [x.ljust(max_len) for x in texts]
elif mode == 'center':
return [x.center(max_len) for x in texts]
else:
return [x.rjust(max_len) for x in texts] |
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
bounds length of printed sequence, depending on options | def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds):
"""
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
bounds length of printed sequence, depending on options
"""
if isinstance(seq, set):
fmt = "{{{body}}}"
else:
fmt = "[{body}]" if hasattr(seq, '__setitem__') else "({body})"
if max_seq_items is False:
nitems = len(seq)
else:
nitems = max_seq_items or get_option("max_seq_items") or len(seq)
s = iter(seq)
# handle sets, no slicing
r = [pprint_thing(next(s),
_nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
for i in range(min(nitems, len(seq)))]
body = ", ".join(r)
if nitems < len(seq):
body += ", ..."
elif isinstance(seq, tuple) and len(seq) == 1:
body += ','
return fmt.format(body=body) |
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly. | def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds):
"""
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
"""
fmt = "{{{things}}}"
pairs = []
pfmt = "{key}: {val}"
if max_seq_items is False:
nitems = len(seq)
else:
nitems = max_seq_items or get_option("max_seq_items") or len(seq)
for k, v in list(seq.items())[:nitems]:
pairs.append(
pfmt.format(
key=pprint_thing(k, _nest_lvl + 1,
max_seq_items=max_seq_items, **kwds),
val=pprint_thing(v, _nest_lvl + 1,
max_seq_items=max_seq_items, **kwds)))
if nitems < len(seq):
return fmt.format(things=", ".join(pairs) + ", ...")
else:
return fmt.format(things=", ".join(pairs)) |
This function is the sanctioned way of converting objects
to a unicode representation.
properly handles nested sequences containing unicode strings
(unicode(object) does not)
Parameters
----------
thing : anything to be formatted
_nest_lvl : internal use only. pprint_thing() is mutually-recursive
with pprint_sequence, this argument is used to keep track of the
current nesting level, and limit it.
escape_chars : list or dict, optional
Characters to escape. If a dict is passed the values are the
replacements
default_escapes : bool, default False
Whether the input escape characters replaces or adds to the defaults
max_seq_items : False, int, default None
Pass thru to other pretty printers to limit sequence printing
Returns
-------
result - unicode str | def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False,
quote_strings=False, max_seq_items=None):
"""
This function is the sanctioned way of converting objects
to a unicode representation.
properly handles nested sequences containing unicode strings
(unicode(object) does not)
Parameters
----------
thing : anything to be formatted
_nest_lvl : internal use only. pprint_thing() is mutually-recursive
with pprint_sequence, this argument is used to keep track of the
current nesting level, and limit it.
escape_chars : list or dict, optional
Characters to escape. If a dict is passed the values are the
replacements
default_escapes : bool, default False
Whether the input escape characters replaces or adds to the defaults
max_seq_items : False, int, default None
Pass thru to other pretty printers to limit sequence printing
Returns
-------
result - unicode str
"""
def as_escaped_unicode(thing, escape_chars=escape_chars):
# Unicode is fine, else we try to decode using utf-8 and 'replace'
# if that's not it either, we have no way of knowing and the user
# should deal with it himself.
try:
result = str(thing) # we should try this first
except UnicodeDecodeError:
# either utf-8 or we replace errors
result = str(thing).decode('utf-8', "replace")
translate = {'\t': r'\t', '\n': r'\n', '\r': r'\r', }
if isinstance(escape_chars, dict):
if default_escapes:
translate.update(escape_chars)
else:
translate = escape_chars
escape_chars = list(escape_chars.keys())
else:
escape_chars = escape_chars or tuple()
for c in escape_chars:
result = result.replace(c, translate[c])
return str(result)
if hasattr(thing, '__next__'):
return str(thing)
elif (isinstance(thing, dict) and
_nest_lvl < get_option("display.pprint_nest_depth")):
result = _pprint_dict(thing, _nest_lvl, quote_strings=True,
max_seq_items=max_seq_items)
elif (is_sequence(thing) and
_nest_lvl < get_option("display.pprint_nest_depth")):
result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars,
quote_strings=quote_strings,
max_seq_items=max_seq_items)
elif isinstance(thing, str) and quote_strings:
result = "'{thing}'".format(thing=as_escaped_unicode(thing))
else:
result = as_escaped_unicode(thing)
return str(result) |
Return the formatted obj as a unicode string
Parameters
----------
obj : object
must be iterable and support __getitem__
formatter : callable
string formatter for an element
is_justify : boolean
should justify the display
name : name, optional
defaults to the class name of the obj
indent_for_name : bool, default True
Whether subsequent lines should be be indented to
align with the name.
Returns
-------
summary string | def format_object_summary(obj, formatter, is_justify=True, name=None,
indent_for_name=True):
"""
Return the formatted obj as a unicode string
Parameters
----------
obj : object
must be iterable and support __getitem__
formatter : callable
string formatter for an element
is_justify : boolean
should justify the display
name : name, optional
defaults to the class name of the obj
indent_for_name : bool, default True
Whether subsequent lines should be be indented to
align with the name.
Returns
-------
summary string
"""
from pandas.io.formats.console import get_console_size
from pandas.io.formats.format import _get_adjustment
display_width, _ = get_console_size()
if display_width is None:
display_width = get_option('display.width') or 80
if name is None:
name = obj.__class__.__name__
if indent_for_name:
name_len = len(name)
space1 = "\n%s" % (' ' * (name_len + 1))
space2 = "\n%s" % (' ' * (name_len + 2))
else:
space1 = "\n"
space2 = "\n " # space for the opening '['
n = len(obj)
sep = ','
max_seq_items = get_option('display.max_seq_items') or n
# are we a truncated display
is_truncated = n > max_seq_items
# adj can optionally handle unicode eastern asian width
adj = _get_adjustment()
def _extend_line(s, line, value, display_width, next_line_prefix):
if (adj.len(line.rstrip()) + adj.len(value.rstrip()) >=
display_width):
s += line.rstrip()
line = next_line_prefix
line += value
return s, line
def best_len(values):
if values:
return max(adj.len(x) for x in values)
else:
return 0
close = ', '
if n == 0:
summary = '[]{}'.format(close)
elif n == 1:
first = formatter(obj[0])
summary = '[{}]{}'.format(first, close)
elif n == 2:
first = formatter(obj[0])
last = formatter(obj[-1])
summary = '[{}, {}]{}'.format(first, last, close)
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in obj[:n]]
tail = [formatter(x) for x in obj[-n:]]
else:
head = []
tail = [formatter(x) for x in obj]
# adjust all values to max length if needed
if is_justify:
# however, if we are not truncated and we are only a single
# line, then don't justify
if (is_truncated or
not (len(', '.join(head)) < display_width and
len(', '.join(tail)) < display_width)):
max_len = max(best_len(head), best_len(tail))
head = [x.rjust(max_len) for x in head]
tail = [x.rjust(max_len) for x in tail]
summary = ""
line = space2
for i in range(len(head)):
word = head[i] + sep + ' '
summary, line = _extend_line(summary, line, word,
display_width, space2)
if is_truncated:
# remove trailing space of last line
summary += line.rstrip() + space2 + '...'
line = space2
for i in range(len(tail) - 1):
word = tail[i] + sep + ' '
summary, line = _extend_line(summary, line, word,
display_width, space2)
# last value: no sep added + 1 space of width used for trailing ','
summary, line = _extend_line(summary, line, tail[-1],
display_width - 2, space2)
summary += line
# right now close is either '' or ', '
# Now we want to include the ']', but not the maybe space.
close = ']' + close.rstrip(' ')
summary += close
if len(summary) > (display_width):
summary += space1
else: # one row
summary += ' '
# remove initial space
summary = '[' + summary[len(space2):]
return summary |
Return a list of tuples of the (attr, formatted_value)
for common attrs, including dtype, name, length
Parameters
----------
obj : object
must be iterable
Returns
-------
list | def format_object_attrs(obj):
"""
Return a list of tuples of the (attr, formatted_value)
for common attrs, including dtype, name, length
Parameters
----------
obj : object
must be iterable
Returns
-------
list
"""
attrs = []
if hasattr(obj, 'dtype'):
attrs.append(('dtype', "'{}'".format(obj.dtype)))
if getattr(obj, 'name', None) is not None:
attrs.append(('name', default_pprint(obj.name)))
max_seq_items = get_option('display.max_seq_items') or len(obj)
if len(obj) > max_seq_items:
attrs.append(('length', len(obj)))
return attrs |
Load data from Google BigQuery.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
query : str
SQL-Like Query to return data values.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
index_col : str, optional
Name of result column to use for index in results DataFrame.
col_order : list(str), optional
List of BigQuery column names in the desired order for results
DataFrame.
reauth : boolean, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
auth_local_webserver : boolean, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
dialect : str, default 'legacy'
Note: The default value is changing to 'standard' in a future verion.
SQL syntax dialect to use. Value can be one of:
``'legacy'``
Use BigQuery's legacy SQL dialect. For more information see
`BigQuery Legacy SQL Reference
<https://cloud.google.com/bigquery/docs/reference/legacy-sql>`__.
``'standard'``
Use BigQuery's standard SQL, which is
compliant with the SQL 2011 standard. For more information
see `BigQuery Standard SQL Reference
<https://cloud.google.com/bigquery/docs/reference/standard-sql/>`__.
.. versionchanged:: 0.24.0
location : str, optional
Location where the query job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of any
datasets used in the query.
*New in version 0.5.0 of pandas-gbq*.
configuration : dict, optional
Query config parameters for job processing.
For example:
configuration = {'query': {'useQueryCache': False}}
For more information see `BigQuery REST API Reference
<https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to override
default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service Account
:class:`google.oauth2.service_account.Credentials` directly.
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
use_bqstorage_api : bool, default False
Use the `BigQuery Storage API
<https://cloud.google.com/bigquery/docs/reference/storage/>`__ to
download query results quickly, but at an increased cost. To use this
API, first `enable it in the Cloud Console
<https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`__.
You must also have the `bigquery.readsessions.create
<https://cloud.google.com/bigquery/docs/access-control#roles>`__
permission on the project you are billing queries to.
This feature requires version 0.10.0 or later of the ``pandas-gbq``
package. It also requires the ``google-cloud-bigquery-storage`` and
``fastavro`` packages.
.. versionadded:: 0.25.0
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
:func:`google.oauth2.service_account.Credentials.from_service_account_info`
or
:func:`google.oauth2.service_account.Credentials.from_service_account_file`
instead.
Service account private key in JSON format. Can be file path
or string contents. This is useful for remote server
authentication (eg. Jupyter/IPython notebook on remote host).
verbose : None, deprecated
Deprecated in pandas-gbq version 0.4.0. Use the `logging module to
adjust verbosity instead
<https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
Returns
-------
df: DataFrame
DataFrame representing results of query.
See Also
--------
pandas_gbq.read_gbq : This function in the pandas-gbq library.
DataFrame.to_gbq : Write a DataFrame to Google BigQuery. | def read_gbq(query, project_id=None, index_col=None, col_order=None,
reauth=False, auth_local_webserver=False, dialect=None,
location=None, configuration=None, credentials=None,
use_bqstorage_api=None, private_key=None, verbose=None):
"""
Load data from Google BigQuery.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
query : str
SQL-Like Query to return data values.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
index_col : str, optional
Name of result column to use for index in results DataFrame.
col_order : list(str), optional
List of BigQuery column names in the desired order for results
DataFrame.
reauth : boolean, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
auth_local_webserver : boolean, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
dialect : str, default 'legacy'
Note: The default value is changing to 'standard' in a future verion.
SQL syntax dialect to use. Value can be one of:
``'legacy'``
Use BigQuery's legacy SQL dialect. For more information see
`BigQuery Legacy SQL Reference
<https://cloud.google.com/bigquery/docs/reference/legacy-sql>`__.
``'standard'``
Use BigQuery's standard SQL, which is
compliant with the SQL 2011 standard. For more information
see `BigQuery Standard SQL Reference
<https://cloud.google.com/bigquery/docs/reference/standard-sql/>`__.
.. versionchanged:: 0.24.0
location : str, optional
Location where the query job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of any
datasets used in the query.
*New in version 0.5.0 of pandas-gbq*.
configuration : dict, optional
Query config parameters for job processing.
For example:
configuration = {'query': {'useQueryCache': False}}
For more information see `BigQuery REST API Reference
<https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to override
default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service Account
:class:`google.oauth2.service_account.Credentials` directly.
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
use_bqstorage_api : bool, default False
Use the `BigQuery Storage API
<https://cloud.google.com/bigquery/docs/reference/storage/>`__ to
download query results quickly, but at an increased cost. To use this
API, first `enable it in the Cloud Console
<https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`__.
You must also have the `bigquery.readsessions.create
<https://cloud.google.com/bigquery/docs/access-control#roles>`__
permission on the project you are billing queries to.
This feature requires version 0.10.0 or later of the ``pandas-gbq``
package. It also requires the ``google-cloud-bigquery-storage`` and
``fastavro`` packages.
.. versionadded:: 0.25.0
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
:func:`google.oauth2.service_account.Credentials.from_service_account_info`
or
:func:`google.oauth2.service_account.Credentials.from_service_account_file`
instead.
Service account private key in JSON format. Can be file path
or string contents. This is useful for remote server
authentication (eg. Jupyter/IPython notebook on remote host).
verbose : None, deprecated
Deprecated in pandas-gbq version 0.4.0. Use the `logging module to
adjust verbosity instead
<https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
Returns
-------
df: DataFrame
DataFrame representing results of query.
See Also
--------
pandas_gbq.read_gbq : This function in the pandas-gbq library.
DataFrame.to_gbq : Write a DataFrame to Google BigQuery.
"""
pandas_gbq = _try_import()
kwargs = {}
# START: new kwargs. Don't populate unless explicitly set.
if use_bqstorage_api is not None:
kwargs["use_bqstorage_api"] = use_bqstorage_api
# END: new kwargs
# START: deprecated kwargs. Don't populate unless explicitly set.
if verbose is not None:
kwargs["verbose"] = verbose
if private_key is not None:
kwargs["private_key"] = private_key
# END: deprecated kwargs
return pandas_gbq.read_gbq(
query, project_id=project_id, index_col=index_col,
col_order=col_order, reauth=reauth,
auth_local_webserver=auth_local_webserver, dialect=dialect,
location=location, configuration=configuration,
credentials=credentials, **kwargs) |
Draw a matrix of scatter plots.
Parameters
----------
frame : DataFrame
alpha : float, optional
amount of transparency applied
figsize : (float,float), optional
a tuple (width, height) in inches
ax : Matplotlib axis object, optional
grid : bool, optional
setting this to True will show the grid
diagonal : {'hist', 'kde'}
pick between 'kde' and 'hist' for
either Kernel Density Estimation or Histogram
plot in the diagonal
marker : str, optional
Matplotlib marker type, default '.'
hist_kwds : other plotting keyword arguments
To be passed to hist function
density_kwds : other plotting keyword arguments
To be passed to kernel density estimate plot
range_padding : float, optional
relative extension of axis range in x and y
with respect to (x_max - x_min) or (y_max - y_min),
default 0.05
kwds : other plotting keyword arguments
To be passed to scatter function
Examples
--------
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix(df, alpha=0.2) | def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
diagonal='hist', marker='.', density_kwds=None,
hist_kwds=None, range_padding=0.05, **kwds):
"""
Draw a matrix of scatter plots.
Parameters
----------
frame : DataFrame
alpha : float, optional
amount of transparency applied
figsize : (float,float), optional
a tuple (width, height) in inches
ax : Matplotlib axis object, optional
grid : bool, optional
setting this to True will show the grid
diagonal : {'hist', 'kde'}
pick between 'kde' and 'hist' for
either Kernel Density Estimation or Histogram
plot in the diagonal
marker : str, optional
Matplotlib marker type, default '.'
hist_kwds : other plotting keyword arguments
To be passed to hist function
density_kwds : other plotting keyword arguments
To be passed to kernel density estimate plot
range_padding : float, optional
relative extension of axis range in x and y
with respect to (x_max - x_min) or (y_max - y_min),
default 0.05
kwds : other plotting keyword arguments
To be passed to scatter function
Examples
--------
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix(df, alpha=0.2)
"""
df = frame._get_numeric_data()
n = df.columns.size
naxes = n * n
fig, axes = _subplots(naxes=naxes, figsize=figsize, ax=ax,
squeeze=False)
# no gaps between subplots
fig.subplots_adjust(wspace=0, hspace=0)
mask = notna(df)
marker = _get_marker_compat(marker)
hist_kwds = hist_kwds or {}
density_kwds = density_kwds or {}
# GH 14855
kwds.setdefault('edgecolors', 'none')
boundaries_list = []
for a in df.columns:
values = df[a].values[mask[a].values]
rmin_, rmax_ = np.min(values), np.max(values)
rdelta_ext = (rmax_ - rmin_) * range_padding / 2.
boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext))
for i, a in zip(lrange(n), df.columns):
for j, b in zip(lrange(n), df.columns):
ax = axes[i, j]
if i == j:
values = df[a].values[mask[a].values]
# Deal with the diagonal by drawing a histogram there.
if diagonal == 'hist':
ax.hist(values, **hist_kwds)
elif diagonal in ('kde', 'density'):
from scipy.stats import gaussian_kde
y = values
gkde = gaussian_kde(y)
ind = np.linspace(y.min(), y.max(), 1000)
ax.plot(ind, gkde.evaluate(ind), **density_kwds)
ax.set_xlim(boundaries_list[i])
else:
common = (mask[a] & mask[b]).values
ax.scatter(df[b][common], df[a][common],
marker=marker, alpha=alpha, **kwds)
ax.set_xlim(boundaries_list[j])
ax.set_ylim(boundaries_list[i])
ax.set_xlabel(b)
ax.set_ylabel(a)
if j != 0:
ax.yaxis.set_visible(False)
if i != n - 1:
ax.xaxis.set_visible(False)
if len(df.columns) > 1:
lim1 = boundaries_list[0]
locs = axes[0][1].yaxis.get_majorticklocs()
locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])]
adj = (locs - lim1[0]) / (lim1[1] - lim1[0])
lim0 = axes[0][0].get_ylim()
adj = adj * (lim0[1] - lim0[0]) + lim0[0]
axes[0][0].yaxis.set_ticks(adj)
if np.all(locs == locs.astype(int)):
# if all ticks are int
locs = locs.astype(int)
axes[0][0].yaxis.set_ticklabels(locs)
_set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
return axes |
Plot a multidimensional dataset in 2D.
Each Series in the DataFrame is represented as a evenly distributed
slice on a circle. Each data point is rendered in the circle according to
the value on each Series. Highly correlated `Series` in the `DataFrame`
are placed closer on the unit circle.
RadViz allow to project a N-dimensional data set into a 2D space where the
influence of each dimension can be interpreted as a balance between the
influence of all dimensions.
More info available at the `original article
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_
describing RadViz.
Parameters
----------
frame : `DataFrame`
Pandas object holding the data.
class_column : str
Column name containing the name of the data point category.
ax : :class:`matplotlib.axes.Axes`, optional
A plot instance to which to add the information.
color : list[str] or tuple[str], optional
Assign a color to each category. Example: ['blue', 'green'].
colormap : str or :class:`matplotlib.colors.Colormap`, default None
Colormap to select colors from. If string, load colormap with that
name from matplotlib.
kwds : optional
Options to pass to matplotlib scatter plotting method.
Returns
-------
class:`matplotlib.axes.Axes`
See Also
--------
plotting.andrews_curves : Plot clustering visualization.
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6,
... 6.7, 4.6],
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2,
... 3.3, 3.6],
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4,
... 5.7, 1.0],
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2,
... 2.1, 0.2],
... 'Category': ['virginica', 'virginica', 'setosa',
... 'virginica', 'virginica', 'versicolor',
... 'versicolor', 'setosa', 'virginica',
... 'setosa']
... })
>>> rad_viz = pd.plotting.radviz(df, 'Category') # doctest: +SKIP | def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
"""
Plot a multidimensional dataset in 2D.
Each Series in the DataFrame is represented as a evenly distributed
slice on a circle. Each data point is rendered in the circle according to
the value on each Series. Highly correlated `Series` in the `DataFrame`
are placed closer on the unit circle.
RadViz allow to project a N-dimensional data set into a 2D space where the
influence of each dimension can be interpreted as a balance between the
influence of all dimensions.
More info available at the `original article
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_
describing RadViz.
Parameters
----------
frame : `DataFrame`
Pandas object holding the data.
class_column : str
Column name containing the name of the data point category.
ax : :class:`matplotlib.axes.Axes`, optional
A plot instance to which to add the information.
color : list[str] or tuple[str], optional
Assign a color to each category. Example: ['blue', 'green'].
colormap : str or :class:`matplotlib.colors.Colormap`, default None
Colormap to select colors from. If string, load colormap with that
name from matplotlib.
kwds : optional
Options to pass to matplotlib scatter plotting method.
Returns
-------
class:`matplotlib.axes.Axes`
See Also
--------
plotting.andrews_curves : Plot clustering visualization.
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6,
... 6.7, 4.6],
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2,
... 3.3, 3.6],
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4,
... 5.7, 1.0],
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2,
... 2.1, 0.2],
... 'Category': ['virginica', 'virginica', 'setosa',
... 'virginica', 'virginica', 'versicolor',
... 'versicolor', 'setosa', 'virginica',
... 'setosa']
... })
>>> rad_viz = pd.plotting.radviz(df, 'Category') # doctest: +SKIP
"""
import matplotlib.pyplot as plt
import matplotlib.patches as patches
def normalize(series):
a = min(series)
b = max(series)
return (series - a) / (b - a)
n = len(frame)
classes = frame[class_column].drop_duplicates()
class_col = frame[class_column]
df = frame.drop(class_column, axis=1).apply(normalize)
if ax is None:
ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1])
to_plot = {}
colors = _get_standard_colors(num_colors=len(classes), colormap=colormap,
color_type='random', color=color)
for kls in classes:
to_plot[kls] = [[], []]
m = len(frame.columns) - 1
s = np.array([(np.cos(t), np.sin(t))
for t in [2.0 * np.pi * (i / float(m))
for i in range(m)]])
for i in range(n):
row = df.iloc[i].values
row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)
y = (s * row_).sum(axis=0) / row.sum()
kls = class_col.iat[i]
to_plot[kls][0].append(y[0])
to_plot[kls][1].append(y[1])
for i, kls in enumerate(classes):
ax.scatter(to_plot[kls][0], to_plot[kls][1], color=colors[i],
label=pprint_thing(kls), **kwds)
ax.legend()
ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none'))
for xy, name in zip(s, df.columns):
ax.add_patch(patches.Circle(xy, radius=0.025, facecolor='gray'))
if xy[0] < 0.0 and xy[1] < 0.0:
ax.text(xy[0] - 0.025, xy[1] - 0.025, name,
ha='right', va='top', size='small')
elif xy[0] < 0.0 and xy[1] >= 0.0:
ax.text(xy[0] - 0.025, xy[1] + 0.025, name,
ha='right', va='bottom', size='small')
elif xy[0] >= 0.0 and xy[1] < 0.0:
ax.text(xy[0] + 0.025, xy[1] - 0.025, name,
ha='left', va='top', size='small')
elif xy[0] >= 0.0 and xy[1] >= 0.0:
ax.text(xy[0] + 0.025, xy[1] + 0.025, name,
ha='left', va='bottom', size='small')
ax.axis('equal')
return ax |
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) +
x_4 sin(2t) + x_5 cos(2t) + ...
Where x coefficients correspond to the values of each dimension and t is
linearly spaced between -pi and +pi. Each row of frame then corresponds to
a single curve.
Parameters
----------
frame : DataFrame
Data to be plotted, preferably normalized to (0.0, 1.0)
class_column : Name of the column containing class names
ax : matplotlib axes object, default None
samples : Number of points to plot in each curve
color : list or tuple, optional
Colors to use for the different classes
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that name
from matplotlib.
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlip.axis.Axes` | def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
colormap=None, **kwds):
"""
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) +
x_4 sin(2t) + x_5 cos(2t) + ...
Where x coefficients correspond to the values of each dimension and t is
linearly spaced between -pi and +pi. Each row of frame then corresponds to
a single curve.
Parameters
----------
frame : DataFrame
Data to be plotted, preferably normalized to (0.0, 1.0)
class_column : Name of the column containing class names
ax : matplotlib axes object, default None
samples : Number of points to plot in each curve
color : list or tuple, optional
Colors to use for the different classes
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that name
from matplotlib.
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlip.axis.Axes`
"""
from math import sqrt, pi
import matplotlib.pyplot as plt
def function(amplitudes):
def f(t):
x1 = amplitudes[0]
result = x1 / sqrt(2.0)
# Take the rest of the coefficients and resize them
# appropriately. Take a copy of amplitudes as otherwise numpy
# deletes the element from amplitudes itself.
coeffs = np.delete(np.copy(amplitudes), 0)
coeffs.resize(int((coeffs.size + 1) / 2), 2)
# Generate the harmonics and arguments for the sin and cos
# functions.
harmonics = np.arange(0, coeffs.shape[0]) + 1
trig_args = np.outer(harmonics, t)
result += np.sum(coeffs[:, 0, np.newaxis] * np.sin(trig_args) +
coeffs[:, 1, np.newaxis] * np.cos(trig_args),
axis=0)
return result
return f
n = len(frame)
class_col = frame[class_column]
classes = frame[class_column].drop_duplicates()
df = frame.drop(class_column, axis=1)
t = np.linspace(-pi, pi, samples)
used_legends = set()
color_values = _get_standard_colors(num_colors=len(classes),
colormap=colormap, color_type='random',
color=color)
colors = dict(zip(classes, color_values))
if ax is None:
ax = plt.gca(xlim=(-pi, pi))
for i in range(n):
row = df.iloc[i].values
f = function(row)
y = f(t)
kls = class_col.iat[i]
label = pprint_thing(kls)
if label not in used_legends:
used_legends.add(label)
ax.plot(t, y, color=colors[kls], label=label, **kwds)
else:
ax.plot(t, y, color=colors[kls], **kwds)
ax.legend(loc='upper right')
ax.grid()
return ax |
Bootstrap plot on mean, median and mid-range statistics.
The bootstrap plot is used to estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
generate bootstrapping plots for mean, median and mid-range statistics
for the given number of samples of the given size.
.. [1] "Bootstrapping (statistics)" in \
https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
Parameters
----------
series : pandas.Series
Pandas Series from where to get the samplings for the bootstrapping.
fig : matplotlib.figure.Figure, default None
If given, it will use the `fig` reference for plotting instead of
creating a new one with default parameters.
size : int, default 50
Number of data points to consider during each sampling. It must be
greater or equal than the length of the `series`.
samples : int, default 500
Number of times the bootstrap procedure is performed.
**kwds :
Options to pass to matplotlib plotting method.
Returns
-------
matplotlib.figure.Figure
Matplotlib figure.
See Also
--------
DataFrame.plot : Basic plotting for DataFrame objects.
Series.plot : Basic plotting for Series objects.
Examples
--------
.. plot::
:context: close-figs
>>> s = pd.Series(np.random.uniform(size=100))
>>> fig = pd.plotting.bootstrap_plot(s) # doctest: +SKIP | def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
"""
Bootstrap plot on mean, median and mid-range statistics.
The bootstrap plot is used to estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
generate bootstrapping plots for mean, median and mid-range statistics
for the given number of samples of the given size.
.. [1] "Bootstrapping (statistics)" in \
https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
Parameters
----------
series : pandas.Series
Pandas Series from where to get the samplings for the bootstrapping.
fig : matplotlib.figure.Figure, default None
If given, it will use the `fig` reference for plotting instead of
creating a new one with default parameters.
size : int, default 50
Number of data points to consider during each sampling. It must be
greater or equal than the length of the `series`.
samples : int, default 500
Number of times the bootstrap procedure is performed.
**kwds :
Options to pass to matplotlib plotting method.
Returns
-------
matplotlib.figure.Figure
Matplotlib figure.
See Also
--------
DataFrame.plot : Basic plotting for DataFrame objects.
Series.plot : Basic plotting for Series objects.
Examples
--------
.. plot::
:context: close-figs
>>> s = pd.Series(np.random.uniform(size=100))
>>> fig = pd.plotting.bootstrap_plot(s) # doctest: +SKIP
"""
import random
import matplotlib.pyplot as plt
# random.sample(ndarray, int) fails on python 3.3, sigh
data = list(series.values)
samplings = [random.sample(data, size) for _ in range(samples)]
means = np.array([np.mean(sampling) for sampling in samplings])
medians = np.array([np.median(sampling) for sampling in samplings])
midranges = np.array([(min(sampling) + max(sampling)) * 0.5
for sampling in samplings])
if fig is None:
fig = plt.figure()
x = lrange(samples)
axes = []
ax1 = fig.add_subplot(2, 3, 1)
ax1.set_xlabel("Sample")
axes.append(ax1)
ax1.plot(x, means, **kwds)
ax2 = fig.add_subplot(2, 3, 2)
ax2.set_xlabel("Sample")
axes.append(ax2)
ax2.plot(x, medians, **kwds)
ax3 = fig.add_subplot(2, 3, 3)
ax3.set_xlabel("Sample")
axes.append(ax3)
ax3.plot(x, midranges, **kwds)
ax4 = fig.add_subplot(2, 3, 4)
ax4.set_xlabel("Mean")
axes.append(ax4)
ax4.hist(means, **kwds)
ax5 = fig.add_subplot(2, 3, 5)
ax5.set_xlabel("Median")
axes.append(ax5)
ax5.hist(medians, **kwds)
ax6 = fig.add_subplot(2, 3, 6)
ax6.set_xlabel("Midrange")
axes.append(ax6)
ax6.hist(midranges, **kwds)
for axis in axes:
plt.setp(axis.get_xticklabels(), fontsize=8)
plt.setp(axis.get_yticklabels(), fontsize=8)
return fig |
Parallel coordinates plotting.
Parameters
----------
frame : DataFrame
class_column : str
Column name containing class names
cols : list, optional
A list of column names to use
ax : matplotlib.axis, optional
matplotlib axis object
color : list or tuple, optional
Colors to use for the different classes
use_columns : bool, optional
If true, columns will be used as xticks
xticks : list or tuple, optional
A list of values to use for xticks
colormap : str or matplotlib colormap, default None
Colormap to use for line colors.
axvlines : bool, optional
If true, vertical lines will be added at each xtick
axvlines_kwds : keywords, optional
Options to be passed to axvline method for vertical lines
sort_labels : bool, False
Sort class_column labels, useful when assigning colors
.. versionadded:: 0.20.0
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
>>> from matplotlib import pyplot as plt
>>> df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
'/pandas/tests/data/iris.csv')
>>> pd.plotting.parallel_coordinates(
df, 'Name',
color=('#556270', '#4ECDC4', '#C7F464'))
>>> plt.show() | def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
use_columns=False, xticks=None, colormap=None,
axvlines=True, axvlines_kwds=None, sort_labels=False,
**kwds):
"""Parallel coordinates plotting.
Parameters
----------
frame : DataFrame
class_column : str
Column name containing class names
cols : list, optional
A list of column names to use
ax : matplotlib.axis, optional
matplotlib axis object
color : list or tuple, optional
Colors to use for the different classes
use_columns : bool, optional
If true, columns will be used as xticks
xticks : list or tuple, optional
A list of values to use for xticks
colormap : str or matplotlib colormap, default None
Colormap to use for line colors.
axvlines : bool, optional
If true, vertical lines will be added at each xtick
axvlines_kwds : keywords, optional
Options to be passed to axvline method for vertical lines
sort_labels : bool, False
Sort class_column labels, useful when assigning colors
.. versionadded:: 0.20.0
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
>>> from matplotlib import pyplot as plt
>>> df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
'/pandas/tests/data/iris.csv')
>>> pd.plotting.parallel_coordinates(
df, 'Name',
color=('#556270', '#4ECDC4', '#C7F464'))
>>> plt.show()
"""
if axvlines_kwds is None:
axvlines_kwds = {'linewidth': 1, 'color': 'black'}
import matplotlib.pyplot as plt
n = len(frame)
classes = frame[class_column].drop_duplicates()
class_col = frame[class_column]
if cols is None:
df = frame.drop(class_column, axis=1)
else:
df = frame[cols]
used_legends = set()
ncols = len(df.columns)
# determine values to use for xticks
if use_columns is True:
if not np.all(np.isreal(list(df.columns))):
raise ValueError('Columns must be numeric to be used as xticks')
x = df.columns
elif xticks is not None:
if not np.all(np.isreal(xticks)):
raise ValueError('xticks specified must be numeric')
elif len(xticks) != ncols:
raise ValueError('Length of xticks must match number of columns')
x = xticks
else:
x = lrange(ncols)
if ax is None:
ax = plt.gca()
color_values = _get_standard_colors(num_colors=len(classes),
colormap=colormap, color_type='random',
color=color)
if sort_labels:
classes = sorted(classes)
color_values = sorted(color_values)
colors = dict(zip(classes, color_values))
for i in range(n):
y = df.iloc[i].values
kls = class_col.iat[i]
label = pprint_thing(kls)
if label not in used_legends:
used_legends.add(label)
ax.plot(x, y, color=colors[kls], label=label, **kwds)
else:
ax.plot(x, y, color=colors[kls], **kwds)
if axvlines:
for i in x:
ax.axvline(i, **axvlines_kwds)
ax.set_xticks(x)
ax.set_xticklabels(df.columns)
ax.set_xlim(x[0], x[-1])
ax.legend(loc='upper right')
ax.grid()
return ax |
Lag plot for time series.
Parameters
----------
series : Time series
lag : lag of the scatter plot, default 1
ax : Matplotlib axis object, optional
kwds : Matplotlib scatter method keyword arguments, optional
Returns
-------
class:`matplotlib.axis.Axes` | def lag_plot(series, lag=1, ax=None, **kwds):
"""Lag plot for time series.
Parameters
----------
series : Time series
lag : lag of the scatter plot, default 1
ax : Matplotlib axis object, optional
kwds : Matplotlib scatter method keyword arguments, optional
Returns
-------
class:`matplotlib.axis.Axes`
"""
import matplotlib.pyplot as plt
# workaround because `c='b'` is hardcoded in matplotlibs scatter method
kwds.setdefault('c', plt.rcParams['patch.facecolor'])
data = series.values
y1 = data[:-lag]
y2 = data[lag:]
if ax is None:
ax = plt.gca()
ax.set_xlabel("y(t)")
ax.set_ylabel("y(t + {lag})".format(lag=lag))
ax.scatter(y1, y2, **kwds)
return ax |
Autocorrelation plot for time series.
Parameters:
-----------
series: Time series
ax: Matplotlib axis object, optional
kwds : keywords
Options to pass to matplotlib plotting method
Returns:
-----------
class:`matplotlib.axis.Axes` | def autocorrelation_plot(series, ax=None, **kwds):
"""
Autocorrelation plot for time series.
Parameters:
-----------
series: Time series
ax: Matplotlib axis object, optional
kwds : keywords
Options to pass to matplotlib plotting method
Returns:
-----------
class:`matplotlib.axis.Axes`
"""
import matplotlib.pyplot as plt
n = len(series)
data = np.asarray(series)
if ax is None:
ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0))
mean = np.mean(data)
c0 = np.sum((data - mean) ** 2) / float(n)
def r(h):
return ((data[:n - h] - mean) *
(data[h:] - mean)).sum() / float(n) / c0
x = np.arange(n) + 1
y = lmap(r, x)
z95 = 1.959963984540054
z99 = 2.5758293035489004
ax.axhline(y=z99 / np.sqrt(n), linestyle='--', color='grey')
ax.axhline(y=z95 / np.sqrt(n), color='grey')
ax.axhline(y=0.0, color='black')
ax.axhline(y=-z95 / np.sqrt(n), color='grey')
ax.axhline(y=-z99 / np.sqrt(n), linestyle='--', color='grey')
ax.set_xlabel("Lag")
ax.set_ylabel("Autocorrelation")
ax.plot(x, y, **kwds)
if 'label' in kwds:
ax.legend()
ax.grid()
return ax |
Check a sequence of terms for instances of PandasObject. | def _any_pandas_objects(terms):
"""Check a sequence of terms for instances of PandasObject."""
return any(isinstance(term.value, pd.core.generic.PandasObject)
for term in terms) |
Align a set of terms | def _align(terms):
"""Align a set of terms"""
try:
# flatten the parse tree (a nested list, really)
terms = list(com.flatten(terms))
except TypeError:
# can't iterate so it must just be a constant or single variable
if isinstance(terms.value, pd.core.generic.NDFrame):
typ = type(terms.value)
return typ, _zip_axes_from_type(typ, terms.value.axes)
return np.result_type(terms.type), None
# if all resolved variables are numeric scalars
if all(term.is_scalar for term in terms):
return _result_type_many(*(term.value for term in terms)).type, None
# perform the main alignment
typ, axes = _align_core(terms)
return typ, axes |
Reconstruct an object given its type, raw value, and possibly empty
(None) axes.
Parameters
----------
typ : object
A type
obj : object
The value to use in the type constructor
axes : dict
The axes to use to construct the resulting pandas object
Returns
-------
ret : typ
An object of type ``typ`` with the value `obj` and possible axes
`axes`. | def _reconstruct_object(typ, obj, axes, dtype):
"""Reconstruct an object given its type, raw value, and possibly empty
(None) axes.
Parameters
----------
typ : object
A type
obj : object
The value to use in the type constructor
axes : dict
The axes to use to construct the resulting pandas object
Returns
-------
ret : typ
An object of type ``typ`` with the value `obj` and possible axes
`axes`.
"""
try:
typ = typ.type
except AttributeError:
pass
res_t = np.result_type(obj.dtype, dtype)
if (not isinstance(typ, partial) and
issubclass(typ, pd.core.generic.PandasObject)):
return typ(obj, dtype=res_t, **axes)
# special case for pathological things like ~True/~False
if hasattr(res_t, 'type') and typ == np.bool_ and res_t != np.bool_:
ret_value = res_t.type(obj)
else:
ret_value = typ(obj).astype(res_t)
# The condition is to distinguish 0-dim array (returned in case of
# scalar) and 1 element array
# e.g. np.array(0) and np.array([0])
if len(obj.shape) == 1 and len(obj) == 1:
if not isinstance(ret_value, np.ndarray):
ret_value = np.array([ret_value]).astype(res_t)
return ret_value |
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
.. deprecated:: 0.23.0
Use Series.plot() instead | def tsplot(series, plotf, ax=None, **kwargs):
import warnings
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
.. deprecated:: 0.23.0
Use Series.plot() instead
"""
warnings.warn("'tsplot' is deprecated and will be removed in a "
"future version. Please use Series.plot() instead.",
FutureWarning, stacklevel=2)
# Used inferred freq is possible, need a test case for inferred
if ax is None:
import matplotlib.pyplot as plt
ax = plt.gca()
freq, series = _maybe_resample(series, ax, kwargs)
# Set ax with freq info
_decorate_axes(ax, freq, kwargs)
ax._plot_data.append((series, plotf, kwargs))
lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs)
# set date formatter, locators and rescale limits
format_dateaxis(ax, ax.freq, series.index)
return lines |
Initialize axes for time-series plotting | def _decorate_axes(ax, freq, kwargs):
"""Initialize axes for time-series plotting"""
if not hasattr(ax, '_plot_data'):
ax._plot_data = []
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
if not hasattr(ax, 'legendlabels'):
ax.legendlabels = [kwargs.get('label', None)]
else:
ax.legendlabels.append(kwargs.get('label', None))
ax.view_interval = None
ax.date_axis_info = None |
Get the freq attribute of the ax object if set.
Also checks shared axes (eg when using secondary yaxis, sharex=True
or twinx) | def _get_ax_freq(ax):
"""
Get the freq attribute of the ax object if set.
Also checks shared axes (eg when using secondary yaxis, sharex=True
or twinx)
"""
ax_freq = getattr(ax, 'freq', None)
if ax_freq is None:
# check for left/right ax in case of secondary yaxis
if hasattr(ax, 'left_ax'):
ax_freq = getattr(ax.left_ax, 'freq', None)
elif hasattr(ax, 'right_ax'):
ax_freq = getattr(ax.right_ax, 'freq', None)
if ax_freq is None:
# check if a shared ax (sharex/twinx) has already freq set
shared_axes = ax.get_shared_x_axes().get_siblings(ax)
if len(shared_axes) > 1:
for shared_ax in shared_axes:
ax_freq = getattr(shared_ax, 'freq', None)
if ax_freq is not None:
break
return ax_freq |
Convert seconds to 'D days HH:MM:SS.F' | def format_timedelta_ticks(x, pos, n_decimals):
"""
Convert seconds to 'D days HH:MM:SS.F'
"""
s, ns = divmod(x, 1e9)
m, s = divmod(s, 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
decimals = int(ns * 10**(n_decimals - 9))
s = r'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s))
if n_decimals > 0:
s += '.{{:0{:0d}d}}'.format(n_decimals).format(decimals)
if d != 0:
s = '{:d} days '.format(int(d)) + s
return s |
Pretty-formats the date axis (x-axis).
Major and minor ticks are automatically set for the frequency of the
current underlying series. As the dynamic mode is activated by
default, changing the limits of the x axis will intelligently change
the positions of the ticks. | def format_dateaxis(subplot, freq, index):
"""
Pretty-formats the date axis (x-axis).
Major and minor ticks are automatically set for the frequency of the
current underlying series. As the dynamic mode is activated by
default, changing the limits of the x axis will intelligently change
the positions of the ticks.
"""
# handle index specific formatting
# Note: DatetimeIndex does not use this
# interface. DatetimeIndex uses matplotlib.date directly
if isinstance(index, ABCPeriodIndex):
majlocator = TimeSeries_DateLocator(freq, dynamic_mode=True,
minor_locator=False,
plot_obj=subplot)
minlocator = TimeSeries_DateLocator(freq, dynamic_mode=True,
minor_locator=True,
plot_obj=subplot)
subplot.xaxis.set_major_locator(majlocator)
subplot.xaxis.set_minor_locator(minlocator)
majformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True,
minor_locator=False,
plot_obj=subplot)
minformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True,
minor_locator=True,
plot_obj=subplot)
subplot.xaxis.set_major_formatter(majformatter)
subplot.xaxis.set_minor_formatter(minformatter)
# x and y coord info
subplot.format_coord = functools.partial(_format_coord, freq)
elif isinstance(index, ABCTimedeltaIndex):
subplot.xaxis.set_major_formatter(
TimeSeries_TimedeltaFormatter())
else:
raise TypeError('index type not supported')
pylab.draw_if_interactive() |
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
False
Items with the same type but different sizes are considered
different types.
>>> DataFrame({
... "A": np.array([1, 2], dtype=np.int32),
... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
False | def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
False
Items with the same type but different sizes are considered
different types.
>>> DataFrame({
... "A": np.array([1, 2], dtype=np.int32),
... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
False
"""
if self._data.any_extension_types:
return len({block.dtype for block in self._data.blocks}) == 1
else:
return not self._data.is_mixed_type |
Return a html representation for a particular DataFrame.
Mainly for IPython notebook. | def _repr_html_(self):
"""
Return a html representation for a particular DataFrame.
Mainly for IPython notebook.
"""
if self._info_repr():
buf = StringIO("")
self.info(buf=buf)
# need to escape the <class>, should be the first line.
val = buf.getvalue().replace('<', r'<', 1)
val = val.replace('>', r'>', 1)
return '<pre>' + val + '</pre>'
if get_option("display.notebook_repr_html"):
max_rows = get_option("display.max_rows")
max_cols = get_option("display.max_columns")
show_dimensions = get_option("display.show_dimensions")
return self.to_html(max_rows=max_rows, max_cols=max_cols,
show_dimensions=show_dimensions, notebook=True)
else:
return None |
Render a DataFrame to a console-friendly tabular output.
%(shared_params)s
line_width : int, optional
Width to wrap a line in characters.
%(returns)s
See Also
--------
to_html : Convert DataFrame to HTML.
Examples
--------
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6 | def to_string(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None,
max_rows=None, max_cols=None, show_dimensions=False,
decimal='.', line_width=None):
"""
Render a DataFrame to a console-friendly tabular output.
%(shared_params)s
line_width : int, optional
Width to wrap a line in characters.
%(returns)s
See Also
--------
to_html : Convert DataFrame to HTML.
Examples
--------
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6
"""
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal,
line_width=line_width)
formatter.to_string()
if buf is None:
result = formatter.buf.getvalue()
return result |
r"""
Iterator over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Yields
------
label : object
The column names for the DataFrame being iterated over.
content : Series
The column entries belonging to each label, as a Series.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as
(index, Series) pairs.
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
of the values.
Examples
--------
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.iteritems():
... print('label:', label)
... print('content:', content, sep='\n')
...
label: species
content:
panda bear
polar bear
koala marsupial
Name: species, dtype: object
label: population
content:
panda 1864
polar 22000
koala 80000
Name: population, dtype: int64 | def iteritems(self):
r"""
Iterator over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Yields
------
label : object
The column names for the DataFrame being iterated over.
content : Series
The column entries belonging to each label, as a Series.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as
(index, Series) pairs.
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
of the values.
Examples
--------
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.iteritems():
... print('label:', label)
... print('content:', content, sep='\n')
...
label: species
content:
panda bear
polar bear
koala marsupial
Name: species, dtype: object
label: population
content:
panda 1864
polar 22000
koala 80000
Name: population, dtype: int64
"""
if self.columns.is_unique and hasattr(self, '_item_cache'):
for k in self.columns:
yield k, self._get_item_cache(k)
else:
for i, k in enumerate(self.columns):
yield k, self._ixs(i, axis=1) |
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
it : generator
A generator that iterates over the rows of the frame.
See Also
--------
itertuples : Iterate over DataFrame rows as namedtuples of the values.
iteritems : Iterate over (column name, Series) pairs.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect. | def iterrows(self):
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
it : generator
A generator that iterates over the rows of the frame.
See Also
--------
itertuples : Iterate over DataFrame rows as namedtuples of the values.
iteritems : Iterate over (column name, Series) pairs.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.
"""
columns = self.columns
klass = self._constructor_sliced
for k, v in zip(self.index, self.values):
s = klass(v, index=columns, name=k)
yield k, s |
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The name of the returned namedtuples or None to return regular
tuples.
Yields
-------
collections.namedtuple
Yields a namedtuple for each row in the DataFrame with the first
field possibly being the index and following fields being the
column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.iteritems : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
With a large number of columns (>255), regular tuples are returned.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2) | def itertuples(self, index=True, name="Pandas"):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The name of the returned namedtuples or None to return regular
tuples.
Yields
-------
collections.namedtuple
Yields a namedtuple for each row in the DataFrame with the first
field possibly being the index and following fields being the
column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.iteritems : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
With a large number of columns (>255), regular tuples are returned.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
"""
arrays = []
fields = list(self.columns)
if index:
arrays.append(self.index)
fields.insert(0, "Index")
# use integer indexing because of possible duplicate column names
arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
# Python 3 supports at most 255 arguments to constructor
if name is not None and len(self.columns) + index < 256:
itertuple = collections.namedtuple(name, fields, rename=True)
return map(itertuple._make, zip(*arrays))
# fallback to regular tuples
return zip(*arrays) |
Compute the matrix mutiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Serie. If other is a DataFrame or a numpy.array, return
the matrix product of self and other in a DataFrame of a np.array.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2 | def dot(self, other):
"""
Compute the matrix mutiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Serie. If other is a DataFrame or a numpy.array, return
the matrix product of self and other in a DataFrame of a np.array.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
"""
if isinstance(other, (Series, DataFrame)):
common = self.columns.union(other.index)
if (len(common) > len(self.columns) or
len(common) > len(other.index)):
raise ValueError('matrices are not aligned')
left = self.reindex(columns=common, copy=False)
right = other.reindex(index=common, copy=False)
lvals = left.values
rvals = right.values
else:
left = self
lvals = self.values
rvals = np.asarray(other)
if lvals.shape[1] != rvals.shape[0]:
raise ValueError('Dot product shape mismatch, '
'{s} vs {r}'.format(s=lvals.shape,
r=rvals.shape))
if isinstance(other, DataFrame):
return self._constructor(np.dot(lvals, rvals), index=left.index,
columns=other.columns)
elif isinstance(other, Series):
return Series(np.dot(lvals, rvals), index=left.index)
elif isinstance(rvals, (np.ndarray, Index)):
result = np.dot(lvals, rvals)
if result.ndim == 2:
return self._constructor(result, index=left.index)
else:
return Series(result, index=left.index)
else: # pragma: no cover
raise TypeError('unsupported type: {oth}'.format(oth=type(other))) |
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the passed dict
should be the columns of the resulting DataFrame, pass 'columns'
(default). Otherwise if the keys should be rows, pass 'index'.
dtype : dtype, default None
Data type to force, otherwise infer.
columns : list, default None
Column labels to use when ``orient='index'``. Raises a ValueError
if used with ``orient='columns'``.
.. versionadded:: 0.23.0
Returns
-------
DataFrame
See Also
--------
DataFrame.from_records : DataFrame from ndarray (structured
dtype), list of tuples, dict, or DataFrame.
DataFrame : DataFrame object creation using constructor.
Examples
--------
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Specify ``orient='index'`` to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
0 1 2 3
row_1 3 2 1 0
row_2 a b c d
When using the 'index' orientation, the column names can be
specified manually:
>>> pd.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D'])
A B C D
row_1 3 2 1 0
row_2 a b c d | def from_dict(cls, data, orient='columns', dtype=None, columns=None):
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the passed dict
should be the columns of the resulting DataFrame, pass 'columns'
(default). Otherwise if the keys should be rows, pass 'index'.
dtype : dtype, default None
Data type to force, otherwise infer.
columns : list, default None
Column labels to use when ``orient='index'``. Raises a ValueError
if used with ``orient='columns'``.
.. versionadded:: 0.23.0
Returns
-------
DataFrame
See Also
--------
DataFrame.from_records : DataFrame from ndarray (structured
dtype), list of tuples, dict, or DataFrame.
DataFrame : DataFrame object creation using constructor.
Examples
--------
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Specify ``orient='index'`` to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
0 1 2 3
row_1 3 2 1 0
row_2 a b c d
When using the 'index' orientation, the column names can be
specified manually:
>>> pd.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D'])
A B C D
row_1 3 2 1 0
row_2 a b c d
"""
index = None
orient = orient.lower()
if orient == 'index':
if len(data) > 0:
# TODO speed up Series case
if isinstance(list(data.values())[0], (Series, dict)):
data = _from_nested_dict(data)
else:
data, index = list(data.values()), list(data.keys())
elif orient == 'columns':
if columns is not None:
raise ValueError("cannot use columns parameter with "
"orient='columns'")
else: # pragma: no cover
raise ValueError('only recognize index or columns for orient')
return cls(data, index=index, columns=columns, dtype=dtype) |
Convert the DataFrame to a NumPy array.
.. versionadded:: 0.24.0
By default, the dtype of the returned array will be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
``float16`` and ``float32``, the results dtype will be ``float32``.
This may require copying data and coercing values, which may be
expensive.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
Returns
-------
numpy.ndarray
See Also
--------
Series.to_numpy : Similar method for Series.
Examples
--------
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
[2, 4]])
With heterogenous data, the lowest common type will have to
be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
[2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will
have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
[2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object) | def to_numpy(self, dtype=None, copy=False):
"""
Convert the DataFrame to a NumPy array.
.. versionadded:: 0.24.0
By default, the dtype of the returned array will be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
``float16`` and ``float32``, the results dtype will be ``float32``.
This may require copying data and coercing values, which may be
expensive.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
Returns
-------
numpy.ndarray
See Also
--------
Series.to_numpy : Similar method for Series.
Examples
--------
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
[2, 4]])
With heterogenous data, the lowest common type will have to
be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
[2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will
have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
[2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
"""
result = np.array(self.values, dtype=dtype, copy=copy)
return result |
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
Determines the type of the values of the dictionary.
- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
Abbreviations are allowed. `s` indicates `series` and `sp`
indicates `split`.
into : class, default dict
The collections.abc.Mapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
.. versionadded:: 0.21.0
Returns
-------
dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame.
The resulting transformation depends on the `orient` parameter.
See Also
--------
DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
{'col1': row1 1
row2 2
Name: col1, dtype: int64,
'col2': row1 0.50
row2 0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})] | def to_dict(self, orient='dict', into=dict):
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
Determines the type of the values of the dictionary.
- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
Abbreviations are allowed. `s` indicates `series` and `sp`
indicates `split`.
into : class, default dict
The collections.abc.Mapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
.. versionadded:: 0.21.0
Returns
-------
dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame.
The resulting transformation depends on the `orient` parameter.
See Also
--------
DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
{'col1': row1 1
row2 2
Name: col1, dtype: int64,
'col2': row1 0.50
row2 0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
"""
if not self.columns.is_unique:
warnings.warn("DataFrame columns are not unique, some "
"columns will be omitted.", UserWarning,
stacklevel=2)
# GH16122
into_c = com.standardize_mapping(into)
if orient.lower().startswith('d'):
return into_c(
(k, v.to_dict(into)) for k, v in self.items())
elif orient.lower().startswith('l'):
return into_c((k, v.tolist()) for k, v in self.items())
elif orient.lower().startswith('sp'):
return into_c((('index', self.index.tolist()),
('columns', self.columns.tolist()),
('data', [
list(map(com.maybe_box_datetimelike, t))
for t in self.itertuples(index=False, name=None)
])))
elif orient.lower().startswith('s'):
return into_c((k, com.maybe_box_datetimelike(v))
for k, v in self.items())
elif orient.lower().startswith('r'):
columns = self.columns.tolist()
rows = (dict(zip(columns, row))
for row in self.itertuples(index=False, name=None))
return [
into_c((k, com.maybe_box_datetimelike(v))
for k, v in row.items())
for row in rows]
elif orient.lower().startswith('i'):
if not self.index.is_unique:
raise ValueError(
"DataFrame index must be unique for orient='index'."
)
return into_c((t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples(name=None))
else:
raise ValueError("orient '{o}' not understood".format(o=orient)) |
Write a DataFrame to a Google BigQuery table.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
destination_table : str
Name of table to be written, in the form ``dataset.tablename``.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
chunksize : int, optional
Number of rows to be inserted in each chunk from the dataframe.
Set to ``None`` to load the whole dataframe at once.
reauth : bool, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
if_exists : str, default 'fail'
Behavior when the destination table exists. Value can be one of:
``'fail'``
If table exists, do nothing.
``'replace'``
If table exists, drop it, recreate it, and insert data.
``'append'``
If table exists, insert data. Create if does not exist.
auth_local_webserver : bool, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
table_schema : list of dicts, optional
List of BigQuery table fields to which according DataFrame
columns conform to, e.g. ``[{'name': 'col1', 'type':
'STRING'},...]``. If schema is not provided, it will be
generated according to dtypes of DataFrame columns. See
BigQuery API documentation on available names of a field.
*New in version 0.3.1 of pandas-gbq*.
location : str, optional
Location where the load job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of the
target dataset.
*New in version 0.5.0 of pandas-gbq*.
progress_bar : bool, default True
Use the library `tqdm` to show the progress bar for the upload,
chunk by chunk.
*New in version 0.5.0 of pandas-gbq*.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to
override default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service
Account :class:`google.oauth2.service_account.Credentials`
directly.
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
verbose : bool, deprecated
Deprecated in pandas-gbq version 0.4.0. Use the `logging module
to adjust verbosity instead
<https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
:func:`google.oauth2.service_account.Credentials.from_service_account_info`
or
:func:`google.oauth2.service_account.Credentials.from_service_account_file`
instead.
Service account private key in JSON format. Can be file path
or string contents. This is useful for remote server
authentication (eg. Jupyter/IPython notebook on remote host).
See Also
--------
pandas_gbq.to_gbq : This function in the pandas-gbq library.
read_gbq : Read a DataFrame from Google BigQuery. | def to_gbq(self, destination_table, project_id=None, chunksize=None,
reauth=False, if_exists='fail', auth_local_webserver=False,
table_schema=None, location=None, progress_bar=True,
credentials=None, verbose=None, private_key=None):
"""
Write a DataFrame to a Google BigQuery table.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
destination_table : str
Name of table to be written, in the form ``dataset.tablename``.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
chunksize : int, optional
Number of rows to be inserted in each chunk from the dataframe.
Set to ``None`` to load the whole dataframe at once.
reauth : bool, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
if_exists : str, default 'fail'
Behavior when the destination table exists. Value can be one of:
``'fail'``
If table exists, do nothing.
``'replace'``
If table exists, drop it, recreate it, and insert data.
``'append'``
If table exists, insert data. Create if does not exist.
auth_local_webserver : bool, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
table_schema : list of dicts, optional
List of BigQuery table fields to which according DataFrame
columns conform to, e.g. ``[{'name': 'col1', 'type':
'STRING'},...]``. If schema is not provided, it will be
generated according to dtypes of DataFrame columns. See
BigQuery API documentation on available names of a field.
*New in version 0.3.1 of pandas-gbq*.
location : str, optional
Location where the load job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of the
target dataset.
*New in version 0.5.0 of pandas-gbq*.
progress_bar : bool, default True
Use the library `tqdm` to show the progress bar for the upload,
chunk by chunk.
*New in version 0.5.0 of pandas-gbq*.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to
override default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service
Account :class:`google.oauth2.service_account.Credentials`
directly.
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
verbose : bool, deprecated
Deprecated in pandas-gbq version 0.4.0. Use the `logging module
to adjust verbosity instead
<https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
:func:`google.oauth2.service_account.Credentials.from_service_account_info`
or
:func:`google.oauth2.service_account.Credentials.from_service_account_file`
instead.
Service account private key in JSON format. Can be file path
or string contents. This is useful for remote server
authentication (eg. Jupyter/IPython notebook on remote host).
See Also
--------
pandas_gbq.to_gbq : This function in the pandas-gbq library.
read_gbq : Read a DataFrame from Google BigQuery.
"""
from pandas.io import gbq
return gbq.to_gbq(
self, destination_table, project_id=project_id,
chunksize=chunksize, reauth=reauth, if_exists=if_exists,
auth_local_webserver=auth_local_webserver,
table_schema=table_schema, location=location,
progress_bar=progress_bar, credentials=credentials,
verbose=verbose, private_key=private_key) |
Convert structured or record ndarray to DataFrame.
Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
index : string, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use
exclude : sequence, default None
Columns or fields to exclude
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns)
coerce_float : boolean, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
nrows : int, default None
Number of rows to read if data is an iterator
Returns
-------
DataFrame | def from_records(cls, data, index=None, exclude=None, columns=None,
coerce_float=False, nrows=None):
"""
Convert structured or record ndarray to DataFrame.
Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
index : string, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use
exclude : sequence, default None
Columns or fields to exclude
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns)
coerce_float : boolean, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
nrows : int, default None
Number of rows to read if data is an iterator
Returns
-------
DataFrame
"""
# Make a copy of the input columns so we can modify it
if columns is not None:
columns = ensure_index(columns)
if is_iterator(data):
if nrows == 0:
return cls()
try:
first_row = next(data)
except StopIteration:
return cls(index=index, columns=columns)
dtype = None
if hasattr(first_row, 'dtype') and first_row.dtype.names:
dtype = first_row.dtype
values = [first_row]
if nrows is None:
values += data
else:
values.extend(itertools.islice(data, nrows - 1))
if dtype is not None:
data = np.array(values, dtype=dtype)
else:
data = values
if isinstance(data, dict):
if columns is None:
columns = arr_columns = ensure_index(sorted(data))
arrays = [data[k] for k in columns]
else:
arrays = []
arr_columns = []
for k, v in data.items():
if k in columns:
arr_columns.append(k)
arrays.append(v)
arrays, arr_columns = reorder_arrays(arrays, arr_columns,
columns)
elif isinstance(data, (np.ndarray, DataFrame)):
arrays, columns = to_arrays(data, columns)
if columns is not None:
columns = ensure_index(columns)
arr_columns = columns
else:
arrays, arr_columns = to_arrays(data, columns,
coerce_float=coerce_float)
arr_columns = ensure_index(arr_columns)
if columns is not None:
columns = ensure_index(columns)
else:
columns = arr_columns
if exclude is None:
exclude = set()
else:
exclude = set(exclude)
result_index = None
if index is not None:
if (isinstance(index, str) or
not hasattr(index, "__iter__")):
i = columns.get_loc(index)
exclude.add(index)
if len(arrays) > 0:
result_index = Index(arrays[i], name=index)
else:
result_index = Index([], name=index)
else:
try:
index_data = [arrays[arr_columns.get_loc(field)]
for field in index]
result_index = ensure_index_from_sequences(index_data,
names=index)
exclude.update(index)
except Exception:
result_index = index
if any(exclude):
arr_exclude = [x for x in exclude if x in arr_columns]
to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
arr_columns = arr_columns.drop(arr_exclude)
columns = columns.drop(exclude)
mgr = arrays_to_mgr(arrays, arr_columns, result_index, columns)
return cls(mgr) |
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
Parameters
----------
index : bool, default True
Include index in resulting record array, stored in 'index'
field or using the index label, if set.
convert_datetime64 : bool, default None
.. deprecated:: 0.23.0
Whether to convert the index to datetime.datetime if it is a
DatetimeIndex.
column_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all columns. If
a dictionary, a mapping of column names and indices (zero-indexed)
to specific data types.
index_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all index levels. If
a dictionary, a mapping of index level names and indices
(zero-indexed) to specific data types.
This mapping is applied only if `index=True`.
Returns
-------
numpy.recarray
NumPy ndarray with the DataFrame labels as fields and each row
of the DataFrame as entries.
See Also
--------
DataFrame.from_records: Convert structured or record ndarray
to DataFrame.
numpy.recarray: An ndarray that allows field access using
attributes, analogous to typed columns in a
spreadsheet.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
If the DataFrame index has no label then the recarray field name
is set to 'index'. If the index has a label then this is used as the
field name:
>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
As well as for the index:
>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = "<S{}".format(df.index.str.len().max())
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')]) | def to_records(self, index=True, convert_datetime64=None,
column_dtypes=None, index_dtypes=None):
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
Parameters
----------
index : bool, default True
Include index in resulting record array, stored in 'index'
field or using the index label, if set.
convert_datetime64 : bool, default None
.. deprecated:: 0.23.0
Whether to convert the index to datetime.datetime if it is a
DatetimeIndex.
column_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all columns. If
a dictionary, a mapping of column names and indices (zero-indexed)
to specific data types.
index_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all index levels. If
a dictionary, a mapping of index level names and indices
(zero-indexed) to specific data types.
This mapping is applied only if `index=True`.
Returns
-------
numpy.recarray
NumPy ndarray with the DataFrame labels as fields and each row
of the DataFrame as entries.
See Also
--------
DataFrame.from_records: Convert structured or record ndarray
to DataFrame.
numpy.recarray: An ndarray that allows field access using
attributes, analogous to typed columns in a
spreadsheet.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
If the DataFrame index has no label then the recarray field name
is set to 'index'. If the index has a label then this is used as the
field name:
>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
As well as for the index:
>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = "<S{}".format(df.index.str.len().max())
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
"""
if convert_datetime64 is not None:
warnings.warn("The 'convert_datetime64' parameter is "
"deprecated and will be removed in a future "
"version",
FutureWarning, stacklevel=2)
if index:
if is_datetime64_any_dtype(self.index) and convert_datetime64:
ix_vals = [self.index.to_pydatetime()]
else:
if isinstance(self.index, MultiIndex):
# array of tuples to numpy cols. copy copy copy
ix_vals = lmap(np.array, zip(*self.index.values))
else:
ix_vals = [self.index.values]
arrays = ix_vals + [self[c].get_values() for c in self.columns]
count = 0
index_names = list(self.index.names)
if isinstance(self.index, MultiIndex):
for i, n in enumerate(index_names):
if n is None:
index_names[i] = 'level_%d' % count
count += 1
elif index_names[0] is None:
index_names = ['index']
names = lmap(str, index_names) + lmap(str, self.columns)
else:
arrays = [self[c].get_values() for c in self.columns]
names = lmap(str, self.columns)
index_names = []
index_len = len(index_names)
formats = []
for i, v in enumerate(arrays):
index = i
# When the names and arrays are collected, we
# first collect those in the DataFrame's index,
# followed by those in its columns.
#
# Thus, the total length of the array is:
# len(index_names) + len(DataFrame.columns).
#
# This check allows us to see whether we are
# handling a name / array in the index or column.
if index < index_len:
dtype_mapping = index_dtypes
name = index_names[index]
else:
index -= index_len
dtype_mapping = column_dtypes
name = self.columns[index]
# We have a dictionary, so we get the data type
# associated with the index or column (which can
# be denoted by its name in the DataFrame or its
# position in DataFrame's array of indices or
# columns, whichever is applicable.
if is_dict_like(dtype_mapping):
if name in dtype_mapping:
dtype_mapping = dtype_mapping[name]
elif index in dtype_mapping:
dtype_mapping = dtype_mapping[index]
else:
dtype_mapping = None
# If no mapping can be found, use the array's
# dtype attribute for formatting.
#
# A valid dtype must either be a type or
# string naming a type.
if dtype_mapping is None:
formats.append(v.dtype)
elif isinstance(dtype_mapping, (type, np.dtype, str)):
formats.append(dtype_mapping)
else:
element = "row" if i < index_len else "column"
msg = ("Invalid dtype {dtype} specified for "
"{element} {name}").format(dtype=dtype_mapping,
element=element, name=name)
raise ValueError(msg)
return np.rec.fromarrays(
arrays,
dtype={'names': names, 'formats': formats}
) |
Construct a DataFrame from a list of tuples.
.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
instead.
:meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`
may be used to preserve the key order.
Convert (key, value) pairs to DataFrame. The keys will be the axis
index (usually the columns, but depends on the specified
orientation). The values should be arrays or Series.
Parameters
----------
items : sequence of (key, value) pairs
Values should be arrays or Series.
columns : sequence of column labels, optional
Must be passed if orient='index'.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the
input correspond to column labels, pass 'columns'
(default). Otherwise if the keys correspond to the index,
pass 'index'.
Returns
-------
DataFrame | def from_items(cls, items, columns=None, orient='columns'):
"""
Construct a DataFrame from a list of tuples.
.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
instead.
:meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`
may be used to preserve the key order.
Convert (key, value) pairs to DataFrame. The keys will be the axis
index (usually the columns, but depends on the specified
orientation). The values should be arrays or Series.
Parameters
----------
items : sequence of (key, value) pairs
Values should be arrays or Series.
columns : sequence of column labels, optional
Must be passed if orient='index'.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the
input correspond to column labels, pass 'columns'
(default). Otherwise if the keys correspond to the index,
pass 'index'.
Returns
-------
DataFrame
"""
warnings.warn("from_items is deprecated. Please use "
"DataFrame.from_dict(dict(items), ...) instead. "
"DataFrame.from_dict(OrderedDict(items)) may be used to "
"preserve the key order.",
FutureWarning, stacklevel=2)
keys, values = lzip(*items)
if orient == 'columns':
if columns is not None:
columns = ensure_index(columns)
idict = dict(items)
if len(idict) < len(items):
if not columns.equals(ensure_index(keys)):
raise ValueError('With non-unique item names, passed '
'columns must be identical')
arrays = values
else:
arrays = [idict[k] for k in columns if k in idict]
else:
columns = ensure_index(keys)
arrays = values
# GH 17312
# Provide more informative error msg when scalar values passed
try:
return cls._from_arrays(arrays, columns, None)
except ValueError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
elif orient == 'index':
if columns is None:
raise TypeError("Must pass columns with orient='index'")
keys = ensure_index(keys)
# GH 17312
# Provide more informative error msg when scalar values passed
try:
arr = np.array(values, dtype=object).T
data = [lib.maybe_convert_objects(v) for v in arr]
return cls._from_arrays(data, columns, keys)
except TypeError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
else: # pragma: no cover
raise ValueError("'orient' must be either 'columns' or 'index'") |
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`read_csv` instead.
It is preferable to use the more powerful :func:`read_csv`
for most general purposes, but ``from_csv`` makes for an easy
roundtrip to and from a file (the exact counterpart of
``to_csv``), especially with a DataFrame of time series data.
This method only differs from the preferred :func:`read_csv`
in some defaults:
- `index_col` is ``0`` instead of ``None`` (take first column as index
by default)
- `parse_dates` is ``True`` instead of ``False`` (try parsing the index
as datetime by default)
So a ``pd.DataFrame.from_csv(path)`` can be replaced by
``pd.read_csv(path, index_col=0, parse_dates=True)``.
Parameters
----------
path : string file path or file handle / StringIO
header : int, default 0
Row to use as header (skip prior rows)
sep : string, default ','
Field delimiter
index_col : int or sequence, default 0
Column to use for index. If a sequence is given, a MultiIndex
is used. Different default from read_table
parse_dates : boolean, default True
Parse dates. Different default from read_table
tupleize_cols : boolean, default False
write multi_index columns as a list of tuples (if True)
or new (expanded format) if False)
infer_datetime_format : boolean, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
Returns
-------
DataFrame
See Also
--------
read_csv | def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True,
encoding=None, tupleize_cols=None,
infer_datetime_format=False):
"""
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`read_csv` instead.
It is preferable to use the more powerful :func:`read_csv`
for most general purposes, but ``from_csv`` makes for an easy
roundtrip to and from a file (the exact counterpart of
``to_csv``), especially with a DataFrame of time series data.
This method only differs from the preferred :func:`read_csv`
in some defaults:
- `index_col` is ``0`` instead of ``None`` (take first column as index
by default)
- `parse_dates` is ``True`` instead of ``False`` (try parsing the index
as datetime by default)
So a ``pd.DataFrame.from_csv(path)`` can be replaced by
``pd.read_csv(path, index_col=0, parse_dates=True)``.
Parameters
----------
path : string file path or file handle / StringIO
header : int, default 0
Row to use as header (skip prior rows)
sep : string, default ','
Field delimiter
index_col : int or sequence, default 0
Column to use for index. If a sequence is given, a MultiIndex
is used. Different default from read_table
parse_dates : boolean, default True
Parse dates. Different default from read_table
tupleize_cols : boolean, default False
write multi_index columns as a list of tuples (if True)
or new (expanded format) if False)
infer_datetime_format : boolean, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
Returns
-------
DataFrame
See Also
--------
read_csv
"""
warnings.warn("from_csv is deprecated. Please use read_csv(...) "
"instead. Note that some of the default arguments are "
"different, so please refer to the documentation "
"for from_csv when changing your function calls",
FutureWarning, stacklevel=2)
from pandas.io.parsers import read_csv
return read_csv(path, header=header, sep=sep,
parse_dates=parse_dates, index_col=index_col,
encoding=encoding, tupleize_cols=tupleize_cols,
infer_datetime_format=infer_datetime_format) |
Convert to SparseDataFrame.
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
Parameters
----------
fill_value : float, default None
The specific value that should be omitted in the representation.
kind : {'block', 'integer'}, default 'block'
The kind of the SparseIndex tracking where data is not equal to
the fill value:
- 'block' tracks only the locations and sizes of blocks of data.
- 'integer' keeps an array with all the locations of the data.
In most cases 'block' is recommended, since it's more memory
efficient.
Returns
-------
SparseDataFrame
The sparse representation of the DataFrame.
See Also
--------
DataFrame.to_dense :
Converts the DataFrame back to the its dense form.
Examples
--------
>>> df = pd.DataFrame([(np.nan, np.nan),
... (1., np.nan),
... (np.nan, 1.)])
>>> df
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
>>> sdf = df.to_sparse()
>>> sdf
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(sdf)
<class 'pandas.core.sparse.frame.SparseDataFrame'> | def to_sparse(self, fill_value=None, kind='block'):
"""
Convert to SparseDataFrame.
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
Parameters
----------
fill_value : float, default None
The specific value that should be omitted in the representation.
kind : {'block', 'integer'}, default 'block'
The kind of the SparseIndex tracking where data is not equal to
the fill value:
- 'block' tracks only the locations and sizes of blocks of data.
- 'integer' keeps an array with all the locations of the data.
In most cases 'block' is recommended, since it's more memory
efficient.
Returns
-------
SparseDataFrame
The sparse representation of the DataFrame.
See Also
--------
DataFrame.to_dense :
Converts the DataFrame back to the its dense form.
Examples
--------
>>> df = pd.DataFrame([(np.nan, np.nan),
... (1., np.nan),
... (np.nan, 1.)])
>>> df
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
>>> sdf = df.to_sparse()
>>> sdf
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(sdf)
<class 'pandas.core.sparse.frame.SparseDataFrame'>
"""
from pandas.core.sparse.api import SparseDataFrame
return SparseDataFrame(self._series, index=self.index,
columns=self.columns, default_kind=kind,
default_fill_value=fill_value) |
Write out the binary feather-format for DataFrames.
.. versionadded:: 0.20.0
Parameters
----------
fname : str
string file path | def to_feather(self, fname):
"""
Write out the binary feather-format for DataFrames.
.. versionadded:: 0.20.0
Parameters
----------
fname : str
string file path
"""
from pandas.io.feather_format import to_feather
to_feather(self, fname) |
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
This function writes the dataframe as a `parquet file
<https://parquet.apache.org/>`_. You can choose different parquet
backends, and have the option of compression. See
:ref:`the user guide <io.parquet>` for more details.
Parameters
----------
fname : str
File path or Root Directory path. Will be used as Root Directory
path while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file. If ``None``,
the behavior depends on the chosen engine.
.. versionadded:: 0.24.0
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
.. versionadded:: 0.24.0
**kwargs
Additional arguments passed to the parquet library. See
:ref:`pandas io <io.parquet>` for more details.
See Also
--------
read_parquet : Read a parquet file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.
Notes
-----
This function requires either the `fastparquet
<https://pypi.org/project/fastparquet>`_ or `pyarrow
<https://arrow.apache.org/docs/python/>`_ library.
Examples
--------
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
... compression='gzip') # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
col1 col2
0 1 3
1 2 4 | def to_parquet(self, fname, engine='auto', compression='snappy',
index=None, partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
This function writes the dataframe as a `parquet file
<https://parquet.apache.org/>`_. You can choose different parquet
backends, and have the option of compression. See
:ref:`the user guide <io.parquet>` for more details.
Parameters
----------
fname : str
File path or Root Directory path. Will be used as Root Directory
path while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file. If ``None``,
the behavior depends on the chosen engine.
.. versionadded:: 0.24.0
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
.. versionadded:: 0.24.0
**kwargs
Additional arguments passed to the parquet library. See
:ref:`pandas io <io.parquet>` for more details.
See Also
--------
read_parquet : Read a parquet file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.
Notes
-----
This function requires either the `fastparquet
<https://pypi.org/project/fastparquet>`_ or `pyarrow
<https://arrow.apache.org/docs/python/>`_ library.
Examples
--------
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
... compression='gzip') # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
col1 col2
0 1 3
1 2 4
"""
from pandas.io.parquet import to_parquet
to_parquet(self, fname, engine,
compression=compression, index=index,
partition_cols=partition_cols, **kwargs) |
Render a DataFrame as an HTML table.
%(shared_params)s
bold_rows : bool, default True
Make the row labels bold in the output.
classes : str or list or tuple, default None
CSS class(es) to apply to the resulting html table.
escape : bool, default True
Convert the characters <, >, and & to HTML-safe sequences.
notebook : {True, False}, default False
Whether the generated HTML is for IPython Notebook.
border : int
A ``border=border`` attribute is included in the opening
`<table>` tag. Default ``pd.options.html.border``.
.. versionadded:: 0.19.0
table_id : str, optional
A css id is included in the opening `<table>` tag if specified.
.. versionadded:: 0.23.0
render_links : bool, default False
Convert URLs to HTML links.
.. versionadded:: 0.24.0
%(returns)s
See Also
--------
to_string : Convert DataFrame to a string. | def to_html(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None, max_rows=None,
max_cols=None, show_dimensions=False, decimal='.',
bold_rows=True, classes=None, escape=True, notebook=False,
border=None, table_id=None, render_links=False):
"""
Render a DataFrame as an HTML table.
%(shared_params)s
bold_rows : bool, default True
Make the row labels bold in the output.
classes : str or list or tuple, default None
CSS class(es) to apply to the resulting html table.
escape : bool, default True
Convert the characters <, >, and & to HTML-safe sequences.
notebook : {True, False}, default False
Whether the generated HTML is for IPython Notebook.
border : int
A ``border=border`` attribute is included in the opening
`<table>` tag. Default ``pd.options.html.border``.
.. versionadded:: 0.19.0
table_id : str, optional
A css id is included in the opening `<table>` tag if specified.
.. versionadded:: 0.23.0
render_links : bool, default False
Convert URLs to HTML links.
.. versionadded:: 0.24.0
%(returns)s
See Also
--------
to_string : Convert DataFrame to a string.
"""
if (justify is not None and
justify not in fmt._VALID_JUSTIFY_PARAMETERS):
raise ValueError("Invalid value for justify parameter")
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
bold_rows=bold_rows, escape=escape,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal, table_id=table_id,
render_links=render_links)
# TODO: a generic formatter wld b in DataFrameFormatter
formatter.to_html(classes=classes, notebook=notebook, border=border)
if buf is None:
return formatter.buf.getvalue() |
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
max_cols : int, optional
When to switch from the verbose to the truncated output. If the
DataFrame has more than `max_cols` columns, the truncated output
is used. By default, the setting in
``pandas.options.display.max_info_columns`` is used.
memory_usage : bool, str, optional
Specifies whether total memory usage of the DataFrame
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the frame is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a DataFrame and returns None.
See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.
Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
... "float_col": float_values})
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
int_col 5 non-null int64
text_col 5 non-null object
float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes
Prints a summary of columns count and its dtypes but not per column
information:
>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
... encoding="utf-8") as f: # doctest: +SKIP
... f.write(s)
260
The `memory_usage` parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 188.8 MB | def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
null_counts=None):
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
max_cols : int, optional
When to switch from the verbose to the truncated output. If the
DataFrame has more than `max_cols` columns, the truncated output
is used. By default, the setting in
``pandas.options.display.max_info_columns`` is used.
memory_usage : bool, str, optional
Specifies whether total memory usage of the DataFrame
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the frame is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a DataFrame and returns None.
See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.
Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
... "float_col": float_values})
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
int_col 5 non-null int64
text_col 5 non-null object
float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes
Prints a summary of columns count and its dtypes but not per column
information:
>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
... encoding="utf-8") as f: # doctest: +SKIP
... f.write(s)
260
The `memory_usage` parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 188.8 MB
"""
if buf is None: # pragma: no cover
buf = sys.stdout
lines = []
lines.append(str(type(self)))
lines.append(self.index._summary())
if len(self.columns) == 0:
lines.append('Empty {name}'.format(name=type(self).__name__))
fmt.buffer_put_lines(buf, lines)
return
cols = self.columns
# hack
if max_cols is None:
max_cols = get_option('display.max_info_columns',
len(self.columns) + 1)
max_rows = get_option('display.max_info_rows', len(self) + 1)
if null_counts is None:
show_counts = ((len(self.columns) <= max_cols) and
(len(self) < max_rows))
else:
show_counts = null_counts
exceeds_info_cols = len(self.columns) > max_cols
def _verbose_repr():
lines.append('Data columns (total %d columns):' %
len(self.columns))
space = max(len(pprint_thing(k)) for k in self.columns) + 4
counts = None
tmpl = "{count}{dtype}"
if show_counts:
counts = self.count()
if len(cols) != len(counts): # pragma: no cover
raise AssertionError(
'Columns must equal counts '
'({cols:d} != {counts:d})'.format(
cols=len(cols), counts=len(counts)))
tmpl = "{count} non-null {dtype}"
dtypes = self.dtypes
for i, col in enumerate(self.columns):
dtype = dtypes.iloc[i]
col = pprint_thing(col)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(_put_str(col, space) + tmpl.format(count=count,
dtype=dtype))
def _non_verbose_repr():
lines.append(self.columns._summary(name='Columns'))
def _sizeof_fmt(num, size_qualifier):
# returns size in human readable format
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if num < 1024.0:
return ("{num:3.1f}{size_q} "
"{x}".format(num=num, size_q=size_qualifier, x=x))
num /= 1024.0
return "{num:3.1f}{size_q} {pb}".format(num=num,
size_q=size_qualifier,
pb='PB')
if verbose:
_verbose_repr()
elif verbose is False: # specifically set to False, not nesc None
_non_verbose_repr()
else:
if exceeds_info_cols:
_non_verbose_repr()
else:
_verbose_repr()
counts = self.get_dtype_counts()
dtypes = ['{k}({kk:d})'.format(k=k[0], kk=k[1]) for k
in sorted(counts.items())]
lines.append('dtypes: {types}'.format(types=', '.join(dtypes)))
if memory_usage is None:
memory_usage = get_option('display.memory_usage')
if memory_usage:
# append memory usage of df to display
size_qualifier = ''
if memory_usage == 'deep':
deep = True
else:
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
deep = False
if ('object' in counts or
self.index._is_memory_usage_qualified()):
size_qualifier = '+'
mem_usage = self.memory_usage(index=True, deep=deep).sum()
lines.append("memory usage: {mem}\n".format(
mem=_sizeof_fmt(mem_usage, size_qualifier)))
fmt.buffer_put_lines(buf, lines) |
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
copy : bool, default False
If True, the underlying data is copied. Otherwise (default), no
copy is made if possible.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
The transposed DataFrame.
See Also
--------
numpy.transpose : Permute the dimensions of a given array.
Notes
-----
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the `object` dtype. In such a case, a copy of the data
is always made.
Examples
--------
**Square DataFrame with homogeneous dtype**
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object
**Non-square DataFrame with mixed dtypes**
>>> d2 = {'name': ['Alice', 'Bob'],
... 'score': [9.5, 8],
... 'employed': [False, True],
... 'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
0 1
name Alice Bob
score 9.5 8
employed False True
kids 0 0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the `object` dtype:
>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object | def transpose(self, *args, **kwargs):
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
copy : bool, default False
If True, the underlying data is copied. Otherwise (default), no
copy is made if possible.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
The transposed DataFrame.
See Also
--------
numpy.transpose : Permute the dimensions of a given array.
Notes
-----
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the `object` dtype. In such a case, a copy of the data
is always made.
Examples
--------
**Square DataFrame with homogeneous dtype**
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object
**Non-square DataFrame with mixed dtypes**
>>> d2 = {'name': ['Alice', 'Bob'],
... 'score': [9.5, 8],
... 'employed': [False, True],
... 'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
0 1
name Alice Bob
score 9.5 8
employed False True
kids 0 0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the `object` dtype:
>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object
"""
nv.validate_transpose(args, dict())
return super().transpose(1, 0, **kwargs) |
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
suppressed by setting ``pandas.options.display.memory_usage`` to False.
Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the DataFrame's
index in returned Series. If ``index=True``, the memory usage of
the index is the first item in the output.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned values.
Returns
-------
Series
A Series whose index is the original column names and whose values
is the memory usage of each column in bytes.
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of an
ndarray.
Series.memory_usage : Bytes consumed by a Series.
Categorical : Memory-efficient array for string values with
many repeated values.
DataFrame.info : Concise summary of a DataFrame.
Examples
--------
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000).astype(t))
... for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
int64 float64 complex128 object bool
0 1 1.0 1.0+0.0j 1 True
1 1 1.0 1.0+0.0j 1 True
2 1 1.0 1.0+0.0j 1 True
3 1 1.0 1.0+0.0j 1 True
4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage()
Index 80
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
>>> df.memory_usage(index=False)
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
The memory footprint of `object` dtype columns is ignored by default:
>>> df.memory_usage(deep=True)
Index 80
int64 40000
float64 40000
complex128 80000
object 160000
bool 5000
dtype: int64
Use a Categorical for efficient storage of an object-dtype column with
many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True)
5168 | def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
suppressed by setting ``pandas.options.display.memory_usage`` to False.
Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the DataFrame's
index in returned Series. If ``index=True``, the memory usage of
the index is the first item in the output.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned values.
Returns
-------
Series
A Series whose index is the original column names and whose values
is the memory usage of each column in bytes.
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of an
ndarray.
Series.memory_usage : Bytes consumed by a Series.
Categorical : Memory-efficient array for string values with
many repeated values.
DataFrame.info : Concise summary of a DataFrame.
Examples
--------
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000).astype(t))
... for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
int64 float64 complex128 object bool
0 1 1.0 1.0+0.0j 1 True
1 1 1.0 1.0+0.0j 1 True
2 1 1.0 1.0+0.0j 1 True
3 1 1.0 1.0+0.0j 1 True
4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage()
Index 80
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
>>> df.memory_usage(index=False)
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
The memory footprint of `object` dtype columns is ignored by default:
>>> df.memory_usage(deep=True)
Index 80
int64 40000
float64 40000
complex128 80000
object 160000
bool 5000
dtype: int64
Use a Categorical for efficient storage of an object-dtype column with
many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True)
5168
"""
result = Series([c.memory_usage(index=False, deep=deep)
for col, c in self.iteritems()], index=self.columns)
if index:
result = Series(self.index.memory_usage(deep=deep),
index=['Index']).append(result)
return result |
Quickly retrieve single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable : interpret the index/col as indexers, default False
Returns
-------
scalar | def get_value(self, index, col, takeable=False):
"""
Quickly retrieve single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable : interpret the index/col as indexers, default False
Returns
-------
scalar
"""
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(index, col, takeable=takeable) |
Put single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
value : scalar
takeable : interpret the index/col as indexers, default False
Returns
-------
DataFrame
If label pair is contained, will be reference to calling DataFrame,
otherwise a new object. | def set_value(self, index, col, value, takeable=False):
"""
Put single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
value : scalar
takeable : interpret the index/col as indexers, default False
Returns
-------
DataFrame
If label pair is contained, will be reference to calling DataFrame,
otherwise a new object.
"""
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(index, col, value, takeable=takeable) |
Parameters
----------
i : int, slice, or sequence of integers
axis : int
Notes
-----
If slice passed, the resulting data will be a view. | def _ixs(self, i, axis=0):
"""
Parameters
----------
i : int, slice, or sequence of integers
axis : int
Notes
-----
If slice passed, the resulting data will be a view.
"""
# irow
if axis == 0:
if isinstance(i, slice):
return self[i]
else:
label = self.index[i]
if isinstance(label, Index):
# a location index by definition
result = self.take(i, axis=axis)
copy = True
else:
new_values = self._data.fast_xs(i)
if is_scalar(new_values):
return new_values
# if we are a copy, mark as such
copy = (isinstance(new_values, np.ndarray) and
new_values.base is None)
result = self._constructor_sliced(new_values,
index=self.columns,
name=self.index[i],
dtype=new_values.dtype)
result._set_is_copy(self, copy=copy)
return result
# icol
else:
label = self.columns[i]
if isinstance(i, slice):
# need to return view
lab_slice = slice(label[0], label[-1])
return self.loc[:, lab_slice]
else:
if isinstance(label, Index):
return self._take(i, axis=1)
index_len = len(self.index)
# if the values returned are not the same length
# as the index (iow a not found value), iget returns
# a 0-len ndarray. This is effectively catching
# a numpy error (as numpy should really raise)
values = self._data.iget(i)
if index_len and not len(values):
values = np.array([np.nan] * index_len, dtype=object)
result = self._box_col_values(values, label)
# this is a cached value, mark it so
result._set_as_cached(label, self)
return result |
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate. You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.
.. versionadded:: 0.25.0
You can refer to column names that contain spaces by surrounding
them in backticks.
For example, if one of your columns is called ``a a`` and you want
to sum it with ``b``, your query should be ```a a` + b``.
inplace : bool
Whether the query should modify the data in place or return
a modified copy.
**kwargs
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by :meth:`DataFrame.query`.
.. versionadded:: 0.18.0
Returns
-------
DataFrame
DataFrame resulting from the provided query expression.
See Also
--------
eval : Evaluate a string describing operations on
DataFrame columns.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
The result of the evaluation of this expression is first passed to
:attr:`DataFrame.loc` and if that fails because of a
multidimensional key (e.g., a DataFrame) then the result will be passed
to :meth:`DataFrame.__getitem__`.
This method uses the top-level :func:`eval` function to
evaluate the passed query.
The :meth:`~pandas.DataFrame.query` method uses a slightly
modified Python syntax by default. For example, the ``&`` and ``|``
(bitwise) operators have the precedence of their boolean cousins,
:keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
however the semantics are different.
You can change the semantics of the expression by passing the keyword
argument ``parser='python'``. This enforces the same semantics as
evaluation in Python space. Likewise, you can pass ``engine='python'``
to evaluate an expression using Python itself as a backend. This is not
recommended as it is inefficient compared to using ``numexpr`` as the
engine.
The :attr:`DataFrame.index` and
:attr:`DataFrame.columns` attributes of the
:class:`~pandas.DataFrame` instance are placed in the query namespace
by default, which allows you to treat both the index and columns of the
frame as a column in the frame.
The identifier ``index`` is used for the frame index; you can also
use the name of the index to identify it in a query. Please note that
Python keywords may not be used as identifiers.
For further details and examples see the ``query`` documentation in
:ref:`indexing <indexing.query>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6),
... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10 | def query(self, expr, inplace=False, **kwargs):
"""
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate. You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.
.. versionadded:: 0.25.0
You can refer to column names that contain spaces by surrounding
them in backticks.
For example, if one of your columns is called ``a a`` and you want
to sum it with ``b``, your query should be ```a a` + b``.
inplace : bool
Whether the query should modify the data in place or return
a modified copy.
**kwargs
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by :meth:`DataFrame.query`.
.. versionadded:: 0.18.0
Returns
-------
DataFrame
DataFrame resulting from the provided query expression.
See Also
--------
eval : Evaluate a string describing operations on
DataFrame columns.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
The result of the evaluation of this expression is first passed to
:attr:`DataFrame.loc` and if that fails because of a
multidimensional key (e.g., a DataFrame) then the result will be passed
to :meth:`DataFrame.__getitem__`.
This method uses the top-level :func:`eval` function to
evaluate the passed query.
The :meth:`~pandas.DataFrame.query` method uses a slightly
modified Python syntax by default. For example, the ``&`` and ``|``
(bitwise) operators have the precedence of their boolean cousins,
:keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
however the semantics are different.
You can change the semantics of the expression by passing the keyword
argument ``parser='python'``. This enforces the same semantics as
evaluation in Python space. Likewise, you can pass ``engine='python'``
to evaluate an expression using Python itself as a backend. This is not
recommended as it is inefficient compared to using ``numexpr`` as the
engine.
The :attr:`DataFrame.index` and
:attr:`DataFrame.columns` attributes of the
:class:`~pandas.DataFrame` instance are placed in the query namespace
by default, which allows you to treat both the index and columns of the
frame as a column in the frame.
The identifier ``index`` is used for the frame index; you can also
use the name of the index to identify it in a query. Please note that
Python keywords may not be used as identifiers.
For further details and examples see the ``query`` documentation in
:ref:`indexing <indexing.query>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6),
... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(expr, str):
msg = "expr must be a string to be evaluated, {0} given"
raise ValueError(msg.format(type(expr)))
kwargs['level'] = kwargs.pop('level', 0) + 1
kwargs['target'] = None
res = self.eval(expr, **kwargs)
try:
new_data = self.loc[res]
except ValueError:
# when res is multi-dimensional loc raises, but this is sometimes a
# valid query
new_data = self[res]
if inplace:
self._update_inplace(new_data)
else:
return new_data |
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evaluate.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
a new DataFrame is returned.
.. versionadded:: 0.18.0.
kwargs : dict
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by
:meth:`~pandas.DataFrame.query`.
Returns
-------
ndarray, scalar, or pandas object
The result of the evaluation.
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.assign : Can evaluate an expression or function to create new
values for a column.
eval : Evaluate a Python expression as a string using various
backends.
Notes
-----
For more details see the API documentation for :func:`~eval`.
For detailed examples see :ref:`enhancing performance with eval
<enhancingperf.eval>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not
modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Use ``inplace=True`` to modify the original DataFrame.
>>> df.eval('C = A + B', inplace=True)
>>> df
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7 | def eval(self, expr, inplace=False, **kwargs):
"""
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evaluate.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
a new DataFrame is returned.
.. versionadded:: 0.18.0.
kwargs : dict
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by
:meth:`~pandas.DataFrame.query`.
Returns
-------
ndarray, scalar, or pandas object
The result of the evaluation.
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.assign : Can evaluate an expression or function to create new
values for a column.
eval : Evaluate a Python expression as a string using various
backends.
Notes
-----
For more details see the API documentation for :func:`~eval`.
For detailed examples see :ref:`enhancing performance with eval
<enhancingperf.eval>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not
modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Use ``inplace=True`` to modify the original DataFrame.
>>> df.eval('C = A + B', inplace=True)
>>> df
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
"""
from pandas.core.computation.eval import eval as _eval
inplace = validate_bool_kwarg(inplace, 'inplace')
resolvers = kwargs.pop('resolvers', None)
kwargs['level'] = kwargs.pop('level', 0) + 1
if resolvers is None:
index_resolvers = self._get_index_resolvers()
column_resolvers = \
self._get_space_character_free_column_resolvers()
resolvers = column_resolvers, index_resolvers
if 'target' not in kwargs:
kwargs['target'] = self
kwargs['resolvers'] = kwargs.get('resolvers', ()) + tuple(resolvers)
return _eval(expr, inplace=inplace, **kwargs) |
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied.
Returns
-------
DataFrame
The subset of the frame including the dtypes in ``include`` and
excluding the dtypes in ``exclude``.
Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
* If ``include`` and ``exclude`` have overlapping elements
* If any kind of string dtype is passed in.
Notes
-----
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
this will return *all* object dtype columns
* See the `numpy dtype hierarchy
<http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
0.20.0) or ``'datetime64[ns, tz]'``
Examples
--------
>>> df = pd.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['int'])
b c
0 True 1.0
1 False 2.0
2 True 1.0
3 False 2.0
4 True 1.0
5 False 2.0 | def select_dtypes(self, include=None, exclude=None):
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied.
Returns
-------
DataFrame
The subset of the frame including the dtypes in ``include`` and
excluding the dtypes in ``exclude``.
Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
* If ``include`` and ``exclude`` have overlapping elements
* If any kind of string dtype is passed in.
Notes
-----
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
this will return *all* object dtype columns
* See the `numpy dtype hierarchy
<http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
0.20.0) or ``'datetime64[ns, tz]'``
Examples
--------
>>> df = pd.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['int'])
b c
0 True 1.0
1 False 2.0
2 True 1.0
3 False 2.0
4 True 1.0
5 False 2.0
"""
def _get_info_slice(obj, indexer):
"""Slice the info axis of `obj` with `indexer`."""
if not hasattr(obj, '_info_axis_number'):
msg = 'object of type {typ!r} has no info axis'
raise TypeError(msg.format(typ=type(obj).__name__))
slices = [slice(None)] * obj.ndim
slices[obj._info_axis_number] = indexer
return tuple(slices)
if not is_list_like(include):
include = (include,) if include is not None else ()
if not is_list_like(exclude):
exclude = (exclude,) if exclude is not None else ()
selection = tuple(map(frozenset, (include, exclude)))
if not any(selection):
raise ValueError('at least one of include or exclude must be '
'nonempty')
# convert the myriad valid dtypes object to a single representation
include, exclude = map(
lambda x: frozenset(map(infer_dtype_from_object, x)), selection)
for dtypes in (include, exclude):
invalidate_string_dtypes(dtypes)
# can't both include AND exclude!
if not include.isdisjoint(exclude):
raise ValueError('include and exclude overlap on {inc_ex}'.format(
inc_ex=(include & exclude)))
# empty include/exclude -> defaults to True
# three cases (we've already raised if both are empty)
# case 1: empty include, nonempty exclude
# we have True, True, ... True for include, same for exclude
# in the loop below we get the excluded
# and when we call '&' below we get only the excluded
# case 2: nonempty include, empty exclude
# same as case 1, but with include
# case 3: both nonempty
# the "union" of the logic of case 1 and case 2:
# we get the included and excluded, and return their logical and
include_these = Series(not bool(include), index=self.columns)
exclude_these = Series(not bool(exclude), index=self.columns)
def is_dtype_instance_mapper(idx, dtype):
return idx, functools.partial(issubclass, dtype.type)
for idx, f in itertools.starmap(is_dtype_instance_mapper,
enumerate(self.dtypes)):
if include: # checks for the case of empty include or exclude
include_these.iloc[idx] = any(map(f, include))
if exclude:
exclude_these.iloc[idx] = not any(map(f, exclude))
dtype_indexer = include_these & exclude_these
return self.loc[_get_info_slice(self, dtype_indexer)] |
Provide boxed values for a column. | def _box_col_values(self, values, items):
"""
Provide boxed values for a column.
"""
klass = self._constructor_sliced
return klass(values, index=self.index, name=items, fastpath=True) |
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrames index to
ensure homogeneity. | def _set_item(self, key, value):
"""
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrames index to
ensure homogeneity.
"""
self._ensure_valid_index(value)
value = self._sanitize_column(key, value)
NDFrame._set_item(self, key, value)
# check if we are modifying a copy
# try to set first as we want an invalid
# value exception to occur first
if len(self):
self._check_setitem_copy() |
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