pandas.DataFrame.describe#

DataFrame.describe(percentiles=None, include=None, exclude=None)[source]#

Generate descriptive statistics.

Summarize the central tendency, dispersion, and shape of each analyzed column’s distribution, excluding NaN values. By default only numeric columns are analyzed; pass include to also analyze non-numeric columns (or exclude to omit columns by dtype).

Parameters:
percentileslist-like of numbers, optional

The percentiles to include in the output. All should fall between 0 and 1. The default, None, returns the 25th, 50th, and 75th percentiles.

include‘all’, list-like of dtypes or None (default), optional

Which column dtypes to include. Options:

  • 'all' : Include all columns, including non-numeric ones.

  • list-like of dtypes : Limit the result to columns of the given dtypes, in the style of DataFrame.select_dtypes() (e.g. include=[np.number] or include=["category"]).

  • None (default) : Include only numeric columns, falling back to object and categorical columns if there are no numeric columns.

excludelist-like of dtypes or None (default), optional

Column dtypes to omit from the result, in the style of DataFrame.select_dtypes(). None (default) excludes nothing.

Returns:
DataFrame

Summary statistics of the DataFrame’s columns.

See also

Series.describe

Generate descriptive statistics of a Series.

DataFrame.count

Count of non-NA observations per column.

DataFrame.max

Maximum of the values in each column.

DataFrame.min

Minimum of the values in each column.

DataFrame.mean

Mean of the values.

DataFrame.std

Standard deviation of the observations.

DataFrame.select_dtypes

Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric columns, the result’s index includes count, mean, std, min, max, and the requested percentiles. By default the lower percentile is 25 and the upper is 75; the 50 percentile is the same as the median.

For object columns, the result’s index includes count, unique, top, and freq. The top is the most common value and freq is its count. If multiple values tie for the highest count, top is chosen arbitrarily from among them.

With include='all', the result’s index is the union of the per-dtype indices, with NaN for statistics that do not apply to a given column’s dtype.

Examples

By default, only numeric columns are analyzed.

>>> 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

All columns regardless of dtype.

>>> df.describe(include="all")
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Restrict the result to a specific dtype.

>>> df.describe(include=["category"])
       categorical
count            3
unique           3
top              d
freq             1

Exclude a specific dtype.

>>> df.describe(exclude=[np.number])
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1
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