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df describe

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Athul Mathew answered on April 16, 2022 Popularity 9/10 Helpfulness 4/10

answer df describe

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df describe

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df.describe(include='all')

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Source: pandas.pydata.org

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Contributed on Aug 18 2022

Athul Mathew

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pandas df describe()

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df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                   })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Popularity 10/10 Helpfulness 3/10 Language python

Source: pandas.pydata.org

Tags: pandas pa

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Contributed on Apr 16 2022

Piotr Adaszewski

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pandas describe

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df.describe() #when df is a Pandas dataframe

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Source: Grepper

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Contributed on Apr 25 2022

Tired Trout

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pandas describe

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pandas describe without outliers

def drop_series_outliers(serie):
    """
    Drop outliers from a pandas series.

    Args:
        serie (pd.Series): Series to drop outliers from.

    Returns:
        pd.Series: Series without outliers in it
    """
    q_1 = serie.quantile(0.25)
    q_3 = serie.quantile(0.75)
    iqr = q_3 - q_1  # Interquartile range
    low = q_1 - 1.5 * iqr
    high = q_3 + 1.5 * iqr

    return serie.loc[(serie > low) & (serie < high)]


def describe(data, *args, drop_outliers=True, **kwargs):
    """
    Describe a pd.DataFrame with some useful information.

    Args:
        data (pd.DataFrame): DataFrame to describe.
        *args: Arguments to pass to pd.DataFrame.describe.
        **kwargs: Keyword arguments to pass to pd.DataFrame.describe.

    Returns:
        pd.DataFrame: DataFrame with the description.
    """
    desc = data.describe(*args, **kwargs).T
    cols = [
        "count",
        "nulls",
        "nulls%",
        "unique",
        "unique%",
        "mean",
        "std",
        "min",
        "25%",
        "50%",
        "75%",
        "max",
        "dtype",
        "memory",
        "memory%",
    ]

    # if data should be cleaned from outliers
    if drop_outliers:
        wo_outliers = pd.DataFrame(
            {
                name: values.pipe(drop_series_outliers).describe(*args, **kwargs).values
                for name, values in data.iteritems()
            },
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        ).T
        desc["outliers"] = desc["count"] - wo_outliers["count"]
        desc["outliers%"] = desc.outliers / desc["count"]
        desc["ovr_min"] = desc["min"]
        desc["ovr_max"] = desc["max"]
        desc["mean"] = wo_outliers["mean"]
        desc["std"] = wo_outliers["std"]
        desc["min"] = wo_outliers["min"]
        desc["25%"] = wo_outliers["25%"]
        desc["50%"] = wo_outliers["50%"]
        desc["75%"] = wo_outliers["75%"]
        desc["max"] = wo_outliers["max"]
        cols = [
            "count",
            "nulls",
            "nulls%",
            "outliers",
            "outliers%",
            "unique",
            "unique%",
            "mean",
            "std",
            "ovr_min",
            "min",
            "25%",
            "50%",
            "75%",
            "ovr_max",
            "max",
            "dtype",
            "memory",
            "memory%",
        ]

    desc["nulls"] = data.isnull().sum()
    desc["nulls%"] = desc["nulls"] / len(data)
    desc["unique"] = data.nunique()
    desc["unique%"] = desc["unique"] / len(data)
    desc["dtype"] = data.dtypes
    desc["memory"] = data.memory_usage(deep=True)
    desc["memory%"] = desc["memory"] / desc["memory"].sum()

    return desc[cols]
  
df.pipe(describe)

An enhanced describe function without outliers for pandas

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Contributed on Jan 06 2023

Homely Herring

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