Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

I've been working on the Two Sigma competition and I tried to add Kurtosis to some aggregations I was doing, but it threw an error since it's apparently not available on GroupBy-ed DataFrames? See this StackOverflow answer and how it's not listed in the Pandas' documentation.

Return unbiased kurtosis within groups.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. ... >>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b'] >>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst) >>> ser.groupby(level=0).kurt() a -6.0 b -1.963223 dtype: Float64

September 16, 2020 - Skewness essentially measures the symmetry of the distribution, while kurtosis determines the heaviness of the distribution tails.

>>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b'] >>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst) >>> ser.groupby(level=0).kurtosis() a -6.0 b -1.963223 dtype: Float64 · Parameters: numeric_only (bool, default False) – Include only float, int or boolean data. Returns: Variance of values within each group. Return type: bigframes.pandas.DataFrame or bigframes.pandas.Series ·

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. Examples: >>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b'] >>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst) >>> ser.groupby(level=0).kurt() a -6.0 b -1.963223 dtype: Float64 ·

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. ... Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. ... Exclude NA/null values when computing the result. ... If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. ... Axis for the function to be applied on. ... Exclude NA/null values when computing the result. Changed in version 3.4.0: Supported including NA/null values. ... Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

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Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

>>> arrays = [['falcon', 'parrot', 'cockatoo', 'kiwi', ... 'lion', 'monkey', 'rabbit'], ... ['bird', 'bird', 'bird', 'bird', ... 'mammal', 'mammal', 'mammal']] >>> index = pd.MultiIndex.from_arrays(arrays, names=('name', 'class')) >>> df = pd.DataFrame({'max_speed': [389.0, 24.0, 70.0, np.nan, ... 80.5, 21.5, 15.0]}, ... index=index) >>> df max_speed name class falcon bird 389.0 parrot bird 24.0 cockatoo bird 70.0 kiwi bird NaN lion mammal 80.5 monkey mammal 21.5 rabbit mammal 15.0 >>> gb = df.groupby(["class"]) >>> gb.skew() max_speed class bird 1.628296 mammal 1.669046 >>> gb.skew(skipna=False) max_speed class bird NaN mammal 1.669046

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. ... Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. ... Exclude NA/null values when computing the result. ... If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

DataFrame.kurtosis(axis: Union[int, str] = None, numeric_only: bool = None) → Union[int, float, str, bytes, decimal.Decimal, datetime.date, None, Series]¶

March 19, 2021 - Python pandas DataFrame.kurtosis() method. This method returns unbiased kurtosis over the requested axis. Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).