README.md

metadata

license: mit

all_in_one.zarr.zip

<xarray.Dataset> Size: 25GB
Dimensions:      (Timestamp: 245376, station: 537)
Coordinates:


Timestamp    (Timestamp) datetime64[ns] 2MB 2017-01-01 ... 2023-12-31T23:...
address      (station) <U187 402kB ...
city         (station) <U18 39kB ...
latitude     (station) float64 4kB ...
longitude    (station) float64 4kB ...
state        (station) <U17 37kB 'Chhattisgarh' ... 'West Bengal'
station      (station) <U64 137kB '32Bungalows, Bhilai - CECB' ... 'Ward-...
Data variables: (12/24)
AT           (Timestamp, station) float64 1GB ...
BP           (Timestamp, station) float64 1GB ...
Benzene      (Timestamp, station) float64 1GB ...
CO           (Timestamp, station) float64 1GB ...
Eth-Benzene  (Timestamp, station) float64 1GB ...
MP-Xylene    (Timestamp, station) float64 1GB ...
...           ...
TOT-RF       (Timestamp, station) float64 1GB ...
Toluene      (Timestamp, station) float64 1GB ...
VWS          (Timestamp, station) float64 1GB ...
WD           (Timestamp, station) float64 1GB ...
WS           (Timestamp, station) float64 1GB ...
Xylene       (Timestamp, station) float64 1GB ...

xarray.Dataset

• Dimensions:
  • Timestamp: 245376
  • station: 537
• Coordinates: (7)
  • Timestamp
    (Timestamp)
    datetime64[ns]
    2017-01-01 ... 2023-12-31T23:45:00

    array(['2017-01-01T00:00:00.000000000', '2017-01-01T00:15:00.000000000',
       '2017-01-01T00:30:00.000000000', ..., '2023-12-31T23:15:00.000000000',
       '2023-12-31T23:30:00.000000000', '2023-12-31T23:45:00.000000000'],
      dtype='datetime64[ns]')

  • address
    (station)
    <U187
    ...

    [537 values with dtype=<U187]

  • city
    (station)
    <U18
    ...

    [537 values with dtype=<U18]

  • latitude
    (station)
    float64
    ...

    [537 values with dtype=float64]

  • longitude
    (station)
    float64
    ...

    [537 values with dtype=float64]

  • state
    (station)
    <U17
    'Chhattisgarh' ... 'West Bengal'

    array(['Chhattisgarh', 'Chhattisgarh', 'Chhattisgarh', ..., 'West Bengal',
       'West Bengal', 'West Bengal'], dtype='<U17')

  • station
    (station)
    <U64
    '32Bungalows, Bhilai - CECB' ......

    array(['32Bungalows, Bhilai - CECB', 'AIIMS, Raipur - CECB',
       'Bhatagaon New ISBT, Raipur - CECB', ...,
       'Sidhu Kanhu Indoor Stadium, Durgapur - WBPCB',
       'Victoria, Kolkata - WBPCB', 'Ward-32 Bapupara, Siliguri - WBPCB'],
      dtype='<U64')

• Data variables: (24)
  • AT
    (Timestamp, station)
    float64
    ...

    unit :

    °C

    [131766912 values with dtype=float64]

  • BP
    (Timestamp, station)
    float64
    ...

    unit :

    mmHg

    [131766912 values with dtype=float64]

  • Benzene
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • CO
    (Timestamp, station)
    float64
    ...

    unit :

    mg/m³

    [131766912 values with dtype=float64]

  • Eth-Benzene
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • MP-Xylene
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • NH3
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • NO
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • NO2
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • NOx
    (Timestamp, station)
    float64
    ...

    unit :

    ppb

    [131766912 values with dtype=float64]

  • O Xylene
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • Ozone
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • PM10
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • PM2.5
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • RF
    (Timestamp, station)
    float64
    ...

    unit :

    mm

    [131766912 values with dtype=float64]

  • RH
    (Timestamp, station)
    float64
    ...

    unit :

    %

    [131766912 values with dtype=float64]

  • SO2
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • SR
    (Timestamp, station)
    float64
    ...

    unit :

    W/mt2

    [131766912 values with dtype=float64]

  • TOT-RF
    (Timestamp, station)
    float64
    ...

    unit :

    mm

    [131766912 values with dtype=float64]

  • Toluene
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

  • VWS
    (Timestamp, station)
    float64
    ...

    unit :

    m/s

    [131766912 values with dtype=float64]

  • WD
    (Timestamp, station)
    float64
    ...

    unit :

    deg

    [131766912 values with dtype=float64]

  • WS
    (Timestamp, station)
    float64
    ...

    unit :

    m/s

    [131766912 values with dtype=float64]

  • Xylene
    (Timestamp, station)
    float64
    ...

    unit :

    µg/m³

    [131766912 values with dtype=float64]

• Indexes: (2)
  • Timestamp
    PandasIndex

    PandasIndex(DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 00:15:00',
           '2017-01-01 00:30:00', '2017-01-01 00:45:00',
           '2017-01-01 01:00:00', '2017-01-01 01:15:00',
           '2017-01-01 01:30:00', '2017-01-01 01:45:00',
           '2017-01-01 02:00:00', '2017-01-01 02:15:00',
           ...
           '2023-12-31 21:30:00', '2023-12-31 21:45:00',
           '2023-12-31 22:00:00', '2023-12-31 22:15:00',
           '2023-12-31 22:30:00', '2023-12-31 22:45:00',
           '2023-12-31 23:00:00', '2023-12-31 23:15:00',
           '2023-12-31 23:30:00', '2023-12-31 23:45:00'],
          dtype='datetime64[ns]', name='Timestamp', length=245376, freq=None))

  • station
    PandasIndex

    PandasIndex(Index(['32Bungalows, Bhilai - CECB', 'AIIMS, Raipur - CECB',
       'Bhatagaon New ISBT, Raipur - CECB',
       'Civic Center, Bhilai - Bhilai Steel Plant',
       'Govt. Higher Secondary School, Milupara - CECB',
       'Hathkhoj, Bhilai - CECB', 'Krishak Nagar, Raipur - CECB',
       'Mangala, Bilaspur - NTPC', 'Nawapara SECL Colony, Chhal - CECB',
       'OP Jindal Industrial Park, Tumidih - CECB',
       ...
       'Ghusuri, Howrah - WBPCB', 'Jadavpur, Kolkata - WBPCB',
       'Padmapukur, Howrah - WBPCB',
       'Priyambada Housing Estate, Haldia - WBPCB',
       'Rabindra Bharati University, Kolkata - WBPCB',
       'Rabindra Sarobar, Kolkata - WBPCB',
       'SVSPA Campus, Barrackpore - WBPCB',
       'Sidhu Kanhu Indoor Stadium, Durgapur - WBPCB',
       'Victoria, Kolkata - WBPCB', 'Ward-32 Bapupara, Siliguri - WBPCB'],
      dtype='object', name='station', length=537))

• Attributes: (0)

Reading data

First install zarr and xarray libraries

pip install zarr xarray

Lazy load the dataset

import xarray as xr
ds = xr.open_zarr("zip:///::https://huggingface.co/datasets/Zeel/P1/resolve/main/all_in_one.zarr.zip")

Full dataset size is ~8 GB. ds variable contains the lazily loaded dataset, which means, it will only download a part of the data when a computation is needed on it. For example, If we need to calculate mean of PM2.5 in Delhi for 2022, it will only download PM2.5 data of all stations in Delhi for 2022.

delhi_2022_ds = ds.sel(station=ds.station.state=="Delhi", Timestamp="2022")["PM2.5"]
mean_val = delhi_2022_ds.mean().item()
print(mean_val)
# 99.5650831365871