pandas.Series.describe#

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

Generate descriptive statistics.

Summarize the central tendency, dispersion, and shape of the Series’s distribution, excluding NaN values. The set of statistics returned depends on the Series’s dtype; see Notes.

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.
includeNone: Has no effect on Series. Deprecated and will be removed in a future version.
excludeNone: Has no effect on Series. Deprecated and will be removed in a future version.

Returns:

Series: Summary statistics of the Series.

See also

DataFrame.describe: Generate descriptive statistics of a DataFrame.
Series.count: Count of non-NA observations.
Series.max: Maximum of the values.
Series.min: Minimum of the values.
Series.mean: Mean of the values.
Series.std: Standard deviation of the observations.

Notes

For numeric dtypes, 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 dtypes (e.g. strings), 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.

For datetime dtypes, the result also includes mean and the requested percentiles, computed on the underlying timestamps.

Examples

A numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

An object Series.

>>> s = pd.Series(["a", "a", "b", "c"])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

A datetime Series.

>>> s = pd.Series(
...     [
...         np.datetime64("2000-01-01"),
...         np.datetime64("2010-01-01"),
...         np.datetime64("2010-01-01"),
...     ]
... )
>>> s.describe()
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object