add code for analysis of data

1 files changed, 430 insertions, 0 deletions

diff --git a/venv/lib/python3.8/site-packages/plotly/data/__init__.py b/venv/lib/python3.8/site-packages/plotly/data/__init__.py
new file mode 100644
index 0000000..3bba389
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/plotly/data/__init__.py
@@ -0,0 +1,430 @@
+"""
+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__)