first commit

.venv/Lib/site-packages/plotly/data/__init__.py

@@ -0,0 +1,222 @@
+"""
+Built-in datasets for demonstration, educational and test purposes.
+"""
+
+
+def gapminder(datetimes=False, centroids=False, year=None, pretty_names=False):
+    """
+    Each row represents a country on a given year.
+
+    https://www.gapminder.org/data/
+
+    Returns:
+        A `pandas.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 = _get_dataset("gapminder")
+    if year:
+        df = df[df["year"] == year]
+    if datetimes:
+        df["year"] = (df["year"].astype(str) + "-01-01").astype("datetime64[ns]")
+    if not centroids:
+        df = df.drop(["centroid_lat", "centroid_lon"], axis=1)
+    if pretty_names:
+        df.rename(
+            mapper=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",
+            ),
+            axis="columns",
+            inplace=True,
+        )
+    return df
+
+
+def tips(pretty_names=False):
+    """
+    Each row represents a restaurant bill.
+
+    https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html
+
+    Returns:
+        A `pandas.DataFrame` with 244 rows and the following columns:
+        `['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']`."""
+
+    df = _get_dataset("tips")
+    if pretty_names:
+        df.rename(
+            mapper=dict(
+                total_bill="Total Bill",
+                tip="Tip",
+                sex="Payer Gender",
+                smoker="Smokers at Table",
+                day="Day of Week",
+                time="Meal",
+                size="Party Size",
+            ),
+            axis="columns",
+            inplace=True,
+        )
+    return df
+
+
+def iris():
+    """
+    Each row represents a flower.
+
+    https://en.wikipedia.org/wiki/Iris_flower_data_set
+
+    Returns:
+        A `pandas.DataFrame` with 150 rows and the following columns:
+        `['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species', 'species_id']`."""
+    return _get_dataset("iris")
+
+
+def wind():
+    """
+    Each row represents a level of wind intensity in a cardinal direction, and its frequency.
+
+    Returns:
+        A `pandas.DataFrame` with 128 rows and the following columns:
+        `['direction', 'strength', 'frequency']`."""
+    return _get_dataset("wind")
+
+
+def election():
+    """
+    Each row represents voting results for an electoral district in the 2013 Montreal
+    mayoral election.
+
+    Returns:
+        A `pandas.DataFrame` with 58 rows and the following columns:
+        `['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result', 'district_id']`."""
+    return _get_dataset("election")
+
+
+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():
+    """
+    Each row represents the availability of car-sharing services near the centroid of a zone
+    in Montreal over a month-long period.
+
+    Returns:
+        A `pandas.DataFrame` with 249 rows and the following columns:
+        `['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']`."""
+    return _get_dataset("carshare")
+
+
+def stocks(indexed=False, datetimes=False):
+    """
+    Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019.
+
+    Returns:
+        A `pandas.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
+        If `datetimes` is True, the 'date' column will be a datetime column
+        is named 'company'"""
+    df = _get_dataset("stocks")
+    if datetimes:
+        df["date"] = df["date"].astype("datetime64[ns]")
+    if indexed:
+        df = df.set_index("date")
+        df.columns.name = "company"
+    return df
+
+
+def experiment(indexed=False):
+    """
+    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.
+
+
+    Returns:
+        A `pandas.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" """
+    df = _get_dataset("experiment")
+    if indexed:
+        df.index.name = "participant"
+    return df
+
+
+def medals_wide(indexed=False):
+    """
+    This dataset represents the medal table for Olympic Short Track Speed Skating for the
+    top three nations as of 2020.
+
+    Returns:
+        A `pandas.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'"""
+    df = _get_dataset("medals")
+    if indexed:
+        df = df.set_index("nation")
+        df.columns.name = "medal"
+    return df
+
+
+def medals_long(indexed=False):
+    """
+    This dataset represents the medal table for Olympic Short Track Speed Skating for the
+    top three nations as of 2020.
+
+    Returns:
+        A `pandas.DataFrame` with 9 rows and the following columns:
+        `['nation', 'medal', 'count']`.
+        If `indexed` is True, the 'nation' column is used as the index."""
+    df = _get_dataset("medals").melt(
+        id_vars=["nation"], value_name="count", var_name="medal"
+    )
+    if indexed:
+        df = df.set_index("nation")
+    return df
+
+
+def _get_dataset(d):
+    import pandas
+    import os
+
+    return pandas.read_csv(
+        os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            "package_data",
+            "datasets",
+            d + ".csv.gz",
+        )
+    )