torcharrow.DataFrame.describe

DataFrame.describe(percentiles=None, include=None, exclude=None)

Generate descriptive statistics.

Parameters:

• array-like (percentiles -) – Defines which percentiles to calculate. If None, uses [25,50,75].

• None (default) – Defines which percentiles to calculate. If None, uses [25,50,75].

• (default) (exclude - array-like of dtypes or None) –

  A white list of data types to include in the result. Here are the options:

  • A list-like of dtypes : Limits the results to the provided data types.

  • None (default) : The result will include all numeric columns.

• optional –

  A white list of data types to include in the result. Here are the options:

  • A list-like of dtypes : Limits the results to the provided data types.

  • None (default) : The result will include all numeric columns.

• (default) –

  An exclusion list of data types to omit from the result. Ignored for Series. Here are the options:

  • A list-like of dtypes : Excludes the provided data types from the result.

  • None (default) : The result will exclude nothing.

• optional –

  An exclusion list of data types to omit from the result. Ignored for Series. Here are the options:

  • A list-like of dtypes : Excludes the provided data types from the result.

  • None (default) : The result will exclude nothing.

Examples

>>> import torcharrow as ta
>>> df = ta.dataframe({"a": [1, 2, 3, 4, 5],
                      "b": [6, 7, 8, 9, 10]
                      })
>>> df.describe()
index  metric          a         b
-------  --------  -------  --------
    0  count     5         5
    1  mean      3         8
    2  std       1.58114   1.58114
    3  min       1         6
    4  25%       2         7
    5  50%       3         8
    6  75%       4         9
    7  max       5        10
dtype: Struct([Field('metric', string), Field('a', float32), Field('b', float32)]), count: 8, null_count: 0