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from typing import Dict, Any
from phi.tools import Toolkit
from phi.utils.log import logger
try:
import pandas as pd
except ImportError:
raise ImportError("`pandas` not installed. Please install using `pip install pandas`.")
class PandasTools(Toolkit):
def __init__(self):
super().__init__(name="pandas_tools")
self.dataframes: Dict[str, pd.DataFrame] = {}
self.register(self.create_pandas_dataframe)
self.register(self.run_dataframe_operation)
def create_pandas_dataframe(
self, dataframe_name: str, create_using_function: str, function_parameters: Dict[str, Any]
) -> str:
"""Creates a pandas dataframe named `dataframe_name` by running a function `create_using_function` with the parameters `function_parameters`.
Returns the created dataframe name as a string if successful, otherwise returns an error message.
For Example:
- To create a dataframe `csv_data` by reading a CSV file, use: {"dataframe_name": "csv_data", "create_using_function": "read_csv", "function_parameters": {"filepath_or_buffer": "data.csv"}}
- To create a dataframe `csv_data` by reading a JSON file, use: {"dataframe_name": "json_data", "create_using_function": "read_json", "function_parameters": {"path_or_buf": "data.json"}}
:param dataframe_name: The name of the dataframe to create.
:param create_using_function: The function to use to create the dataframe.
:param function_parameters: The parameters to pass to the function.
:return: The name of the created dataframe if successful, otherwise an error message.
"""
try:
logger.debug(f"Creating dataframe: {dataframe_name}")
logger.debug(f"Using function: {create_using_function}")
logger.debug(f"With parameters: {function_parameters}")
if dataframe_name in self.dataframes:
return f"Dataframe already exists: {dataframe_name}"
# Create the dataframe
dataframe = getattr(pd, create_using_function)(**function_parameters)
if dataframe is None:
return f"Error creating dataframe: {dataframe_name}"
if not isinstance(dataframe, pd.DataFrame):
return f"Error creating dataframe: {dataframe_name}"
if dataframe.empty:
return f"Dataframe is empty: {dataframe_name}"
self.dataframes[dataframe_name] = dataframe
logger.debug(f"Created dataframe: {dataframe_name}")
return dataframe_name
except Exception as e:
logger.error(f"Error creating dataframe: {e}")
return f"Error creating dataframe: {e}"
def run_dataframe_operation(self, dataframe_name: str, operation: str, operation_parameters: Dict[str, Any]) -> str:
"""Runs an operation `operation` on a dataframe `dataframe_name` with the parameters `operation_parameters`.
Returns the result of the operation as a string if successful, otherwise returns an error message.
For Example:
- To get the first 5 rows of a dataframe `csv_data`, use: {"dataframe_name": "csv_data", "operation": "head", "operation_parameters": {"n": 5}}
- To get the last 5 rows of a dataframe `csv_data`, use: {"dataframe_name": "csv_data", "operation": "tail", "operation_parameters": {"n": 5}}
:param dataframe_name: The name of the dataframe to run the operation on.
:param operation: The operation to run on the dataframe.
:param operation_parameters: The parameters to pass to the operation.
:return: The result of the operation if successful, otherwise an error message.
"""
try:
logger.debug(f"Running operation: {operation}")
logger.debug(f"On dataframe: {dataframe_name}")
logger.debug(f"With parameters: {operation_parameters}")
# Get the dataframe
dataframe = self.dataframes.get(dataframe_name)
# Run the operation
result = getattr(dataframe, operation)(**operation_parameters)
logger.debug(f"Ran operation: {operation}")
try:
try:
return result.to_string()
except AttributeError:
return str(result)
except Exception:
return "Operation ran successfully"
except Exception as e:
logger.error(f"Error running operation: {e}")
return f"Error running operation: {e}"
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