431 lines
13 KiB
Python
431 lines
13 KiB
Python
|
"""
|
||
|
|
Built-in datasets for demonstration, educational and test purposes.
|
||
|
|
"""
|
||
|
|
|
||
|
|
import os
|
||
|
|
from importlib import import_module
|
||
|
|
|
||
|
|
import narwhals.stable.v1 as nw
|
||
|
|
|
||
|
|
AVAILABLE_BACKENDS = {"pandas", "polars", "pyarrow", "modin", "cudf"}
|
||
|
|
BACKENDS_WITH_INDEX_SUPPORT = {"pandas", "modin", "cudf"}
|
||
|
|
|
||
|
|
|
||
|
|
def gapminder(
|
||
|
|
datetimes=False,
|
||
|
|
centroids=False,
|
||
|
|
year=None,
|
||
|
|
pretty_names=False,
|
||
|
|
return_type="pandas",
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Each row represents a country on a given year.
|
||
|
|
|
||
|
|
https://www.gapminder.org/data/
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
datetimes: bool
|
||
|
|
Whether or not 'year' column will converted to datetime type
|
||
|
|
|
||
|
|
centroids: bool
|
||
|
|
If True, ['centroid_lat', 'centroid_lon'] columns are added
|
||
|
|
|
||
|
|
year: int | None
|
||
|
|
If provided, the dataset will be filtered for that year
|
||
|
|
|
||
|
|
pretty_names: bool
|
||
|
|
If True, prettifies the column names
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 1704 rows and the following columns:
|
||
|
|
`['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
|
||
|
|
'iso_alpha', 'iso_num']`.
|
||
|
|
|
||
|
|
If `datetimes` is True, the 'year' column will be a datetime column
|
||
|
|
If `centroids` is True, two new columns are added: ['centroid_lat', 'centroid_lon']
|
||
|
|
If `year` is an integer, the dataset will be filtered for that year
|
||
|
|
"""
|
||
|
|
df = nw.from_native(
|
||
|
|
_get_dataset("gapminder", return_type=return_type), eager_only=True
|
||
|
|
)
|
||
|
|
if year:
|
||
|
|
df = df.filter(nw.col("year") == year)
|
||
|
|
if datetimes:
|
||
|
|
df = df.with_columns(
|
||
|
|
# Concatenate the year value with the literal "-01-01" so that it can be
|
||
|
|
# casted to datetime from "%Y-%m-%d" format
|
||
|
|
nw.concat_str(
|
||
|
|
[nw.col("year").cast(nw.String()), nw.lit("-01-01")]
|
||
|
|
).str.to_datetime(format="%Y-%m-%d")
|
||
|
|
)
|
||
|
|
if not centroids:
|
||
|
|
df = df.drop("centroid_lat", "centroid_lon")
|
||
|
|
if pretty_names:
|
||
|
|
df = df.rename(
|
||
|
|
dict(
|
||
|
|
country="Country",
|
||
|
|
continent="Continent",
|
||
|
|
year="Year",
|
||
|
|
lifeExp="Life Expectancy",
|
||
|
|
gdpPercap="GDP per Capita",
|
||
|
|
pop="Population",
|
||
|
|
iso_alpha="ISO Alpha Country Code",
|
||
|
|
iso_num="ISO Numeric Country Code",
|
||
|
|
centroid_lat="Centroid Latitude",
|
||
|
|
centroid_lon="Centroid Longitude",
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return df.to_native()
|
||
|
|
|
||
|
|
|
||
|
|
def tips(pretty_names=False, return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row represents a restaurant bill.
|
||
|
|
|
||
|
|
https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
pretty_names: bool
|
||
|
|
If True, prettifies the column names
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 244 rows and the following columns:
|
||
|
|
`['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']`.
|
||
|
|
"""
|
||
|
|
|
||
|
|
df = nw.from_native(_get_dataset("tips", return_type=return_type), eager_only=True)
|
||
|
|
if pretty_names:
|
||
|
|
df = df.rename(
|
||
|
|
dict(
|
||
|
|
total_bill="Total Bill",
|
||
|
|
tip="Tip",
|
||
|
|
sex="Payer Gender",
|
||
|
|
smoker="Smokers at Table",
|
||
|
|
day="Day of Week",
|
||
|
|
time="Meal",
|
||
|
|
size="Party Size",
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return df.to_native()
|
||
|
|
|
||
|
|
|
||
|
|
def iris(return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row represents a flower.
|
||
|
|
|
||
|
|
https://en.wikipedia.org/wiki/Iris_flower_data_set
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 150 rows and the following columns:
|
||
|
|
`['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species', 'species_id']`.
|
||
|
|
"""
|
||
|
|
return _get_dataset("iris", return_type=return_type)
|
||
|
|
|
||
|
|
|
||
|
|
def wind(return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row represents a level of wind intensity in a cardinal direction, and its frequency.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 128 rows and the following columns:
|
||
|
|
`['direction', 'strength', 'frequency']`.
|
||
|
|
"""
|
||
|
|
return _get_dataset("wind", return_type=return_type)
|
||
|
|
|
||
|
|
|
||
|
|
def election(return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row represents voting results for an electoral district in the 2013 Montreal
|
||
|
|
mayoral election.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 58 rows and the following columns:
|
||
|
|
`['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result', 'district_id']`.
|
||
|
|
"""
|
||
|
|
return _get_dataset("election", return_type=return_type)
|
||
|
|
|
||
|
|
|
||
|
|
def election_geojson():
|
||
|
|
"""
|
||
|
|
Each feature represents an electoral district in the 2013 Montreal mayoral election.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
A GeoJSON-formatted `dict` with 58 polygon or multi-polygon features whose `id`
|
||
|
|
is an electoral district numerical ID and whose `district` property is the ID and
|
||
|
|
district name.
|
||
|
|
"""
|
||
|
|
import gzip
|
||
|
|
import json
|
||
|
|
import os
|
||
|
|
|
||
|
|
path = os.path.join(
|
||
|
|
os.path.dirname(os.path.dirname(__file__)),
|
||
|
|
"package_data",
|
||
|
|
"datasets",
|
||
|
|
"election.geojson.gz",
|
||
|
|
)
|
||
|
|
with gzip.GzipFile(path, "r") as f:
|
||
|
|
result = json.loads(f.read().decode("utf-8"))
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
def carshare(return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row represents the availability of car-sharing services near the centroid of a zone
|
||
|
|
in Montreal over a month-long period.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe` with 249 rows and the following columns:
|
||
|
|
`['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']`.
|
||
|
|
"""
|
||
|
|
return _get_dataset("carshare", return_type=return_type)
|
||
|
|
|
||
|
|
|
||
|
|
def stocks(indexed=False, datetimes=False, return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
indexed: bool
|
||
|
|
Whether or not the 'date' column is used as the index and the column index
|
||
|
|
is named 'company'. Applicable only if `return_type='pandas'`
|
||
|
|
|
||
|
|
datetimes: bool
|
||
|
|
Whether or not the 'date' column will be of datetime type
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 100 rows and the following columns:
|
||
|
|
`['date', 'GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT']`.
|
||
|
|
If `indexed` is True, the 'date' column is used as the index and the column index
|
||
|
|
is named 'company'
|
||
|
|
If `datetimes` is True, the 'date' column will be a datetime column
|
||
|
|
"""
|
||
|
|
if indexed and return_type not in BACKENDS_WITH_INDEX_SUPPORT:
|
||
|
|
msg = f"Backend '{return_type}' does not support setting index"
|
||
|
|
raise NotImplementedError(msg)
|
||
|
|
|
||
|
|
df = nw.from_native(
|
||
|
|
_get_dataset("stocks", return_type=return_type), eager_only=True
|
||
|
|
).with_columns(nw.col("date").cast(nw.String()))
|
||
|
|
|
||
|
|
if datetimes:
|
||
|
|
df = df.with_columns(nw.col("date").str.to_datetime())
|
||
|
|
|
||
|
|
if indexed: # then it must be pandas
|
||
|
|
df = df.to_native().set_index("date")
|
||
|
|
df.columns.name = "company"
|
||
|
|
return df
|
||
|
|
|
||
|
|
return df.to_native()
|
||
|
|
|
||
|
|
|
||
|
|
def experiment(indexed=False, return_type="pandas"):
|
||
|
|
"""
|
||
|
|
Each row in this wide dataset represents the results of 100 simulated participants
|
||
|
|
on three hypothetical experiments, along with their gender and control/treatment group.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
indexed: bool
|
||
|
|
If True, then the index is named "participant".
|
||
|
|
Applicable only if `return_type='pandas'`
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 100 rows and the following columns:
|
||
|
|
`['experiment_1', 'experiment_2', 'experiment_3', 'gender', 'group']`.
|
||
|
|
If `indexed` is True, the data frame index is named "participant"
|
||
|
|
"""
|
||
|
|
|
||
|
|
if indexed and return_type not in BACKENDS_WITH_INDEX_SUPPORT:
|
||
|
|
msg = f"Backend '{return_type}' does not support setting index"
|
||
|
|
raise NotImplementedError(msg)
|
||
|
|
|
||
|
|
df = nw.from_native(
|
||
|
|
_get_dataset("experiment", return_type=return_type), eager_only=True
|
||
|
|
)
|
||
|
|
if indexed: # then it must be pandas
|
||
|
|
df = df.to_native()
|
||
|
|
df.index.name = "participant"
|
||
|
|
return df
|
||
|
|
return df.to_native()
|
||
|
|
|
||
|
|
|
||
|
|
def medals_wide(indexed=False, return_type="pandas"):
|
||
|
|
"""
|
||
|
|
This dataset represents the medal table for Olympic Short Track Speed Skating for the
|
||
|
|
top three nations as of 2020.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
indexed: bool
|
||
|
|
Whether or not the 'nation' column is used as the index and the column index
|
||
|
|
is named 'medal'. Applicable only if `return_type='pandas'`
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 3 rows and the following columns:
|
||
|
|
`['nation', 'gold', 'silver', 'bronze']`.
|
||
|
|
If `indexed` is True, the 'nation' column is used as the index and the column index
|
||
|
|
is named 'medal'
|
||
|
|
"""
|
||
|
|
|
||
|
|
if indexed and return_type not in BACKENDS_WITH_INDEX_SUPPORT:
|
||
|
|
msg = f"Backend '{return_type}' does not support setting index"
|
||
|
|
raise NotImplementedError(msg)
|
||
|
|
|
||
|
|
df = nw.from_native(
|
||
|
|
_get_dataset("medals", return_type=return_type), eager_only=True
|
||
|
|
)
|
||
|
|
if indexed: # then it must be pandas
|
||
|
|
df = df.to_native().set_index("nation")
|
||
|
|
df.columns.name = "medal"
|
||
|
|
return df
|
||
|
|
return df.to_native()
|
||
|
|
|
||
|
|
|
||
|
|
def medals_long(indexed=False, return_type="pandas"):
|
||
|
|
"""
|
||
|
|
This dataset represents the medal table for Olympic Short Track Speed Skating for the
|
||
|
|
top three nations as of 2020.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
indexed: bool
|
||
|
|
Whether or not the 'nation' column is used as the index.
|
||
|
|
Applicable only if `return_type='pandas'`
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
Dataframe with 9 rows and the following columns: `['nation', 'medal', 'count']`.
|
||
|
|
If `indexed` is True, the 'nation' column is used as the index.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if indexed and return_type not in BACKENDS_WITH_INDEX_SUPPORT:
|
||
|
|
msg = f"Backend '{return_type}' does not support setting index"
|
||
|
|
raise NotImplementedError(msg)
|
||
|
|
|
||
|
|
df = nw.from_native(
|
||
|
|
_get_dataset("medals", return_type=return_type), eager_only=True
|
||
|
|
).unpivot(
|
||
|
|
index=["nation"],
|
||
|
|
value_name="count",
|
||
|
|
variable_name="medal",
|
||
|
|
)
|
||
|
|
if indexed:
|
||
|
|
df = nw.maybe_set_index(df, "nation")
|
||
|
|
return df.to_native()
|
||
|
|
|
||
|
|
|
||
|
|
def _get_dataset(d, return_type):
|
||
|
|
"""
|
||
|
|
Loads the dataset using the specified backend.
|
||
|
|
|
||
|
|
Notice that the available backends are 'pandas', 'polars', 'pyarrow' and they all have
|
||
|
|
a `read_csv` function (pyarrow has it via pyarrow.csv). Therefore we can dynamically
|
||
|
|
load the library using `importlib.import_module` and then call
|
||
|
|
`backend.read_csv(filepath)`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
d: str
|
||
|
|
Name of the dataset to load.
|
||
|
|
|
||
|
|
return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
|
||
|
|
Type of the resulting dataframe
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Dataframe of `return_type` type
|
||
|
|
"""
|
||
|
|
filepath = os.path.join(
|
||
|
|
os.path.dirname(os.path.dirname(__file__)),
|
||
|
|
"package_data",
|
||
|
|
"datasets",
|
||
|
|
d + ".csv.gz",
|
||
|
|
)
|
||
|
|
|
||
|
|
if return_type not in AVAILABLE_BACKENDS:
|
||
|
|
msg = (
|
||
|
|
f"Unsupported return_type. Found {return_type}, expected one "
|
||
|
|
f"of {AVAILABLE_BACKENDS}"
|
||
|
|
)
|
||
|
|
raise NotImplementedError(msg)
|
||
|
|
|
||
|
|
try:
|
||
|
|
if return_type == "pyarrow":
|
||
|
|
module_to_load = "pyarrow.csv"
|
||
|
|
elif return_type == "modin":
|
||
|
|
module_to_load = "modin.pandas"
|
||
|
|
else:
|
||
|
|
module_to_load = return_type
|
||
|
|
backend = import_module(module_to_load)
|
||
|
|
except ModuleNotFoundError:
|
||
|
|
msg = f"return_type={return_type}, but {return_type} is not installed"
|
||
|
|
raise ModuleNotFoundError(msg)
|
||
|
|
|
||
|
|
try:
|
||
|
|
return backend.read_csv(filepath)
|
||
|
|
except Exception as e:
|
||
|
|
msg = f"Unable to read '{d}' dataset due to: {e}"
|
||
|
|
raise Exception(msg).with_traceback(e.__traceback__)
|