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# pandas.Series.describe

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

  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:
    percentiles list-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 None

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

    exclude None

      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