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# pandas.DataFrame.value_counts

DataFrame.value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)

  Return a Series containing the frequency of each distinct row in the DataFrame.

  Parameters:
    subset Hashable or a sequence of the previous, optional

      Columns to use when counting unique combinations.

    normalize bool, default False

      Return proportions rather than frequencies.

    sort bool, default True

      Stable sort by frequencies when True. Preserve the order of the data when False.

      Changed in version 3.0.0: Prior to 3.0.0, `sort=False` would sort by the columns values.

      Changed in version 3.0.0: Prior to 3.0.0, the sort was unstable.

    ascending bool, default False

      Sort in ascending order.

    dropna bool, default True

      Do not include counts of rows that contain NA values.

  Returns:
    Series

      Series containing the frequency of each distinct row in the DataFrame.

  See also

  `Series.value_counts`

    Equivalent method on Series.

  Notes

  The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be sorted by frequencies in descending order so that the first element is the most frequently-occurring row.

  Examples

      >>> df = pd.DataFrame(
      ...     {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]},
      ...     index=["falcon", "dog", "cat", "ant"],
      ... )
      >>> df
              num_legs  num_wings
      falcon         2          2
      dog            4          0
      cat            4          0
      ant            6          0

      >>> df.value_counts()
      num_legs  num_wings
      4         0            2
      2         2            1
      6         0            1
      Name: count, dtype: int64

      >>> df.value_counts(sort=False)
      num_legs  num_wings
      2         2            1
      4         0            2
      6         0            1
      Name: count, dtype: int64

      >>> df.value_counts(ascending=True)
      num_legs  num_wings
      2         2            1
      6         0            1
      4         0            2
      Name: count, dtype: int64

      >>> df.value_counts(normalize=True)
      num_legs  num_wings
      4         0            0.50
      2         2            0.25
      6         0            0.25
      Name: proportion, dtype: float64

  With dropna set to False we can also count rows with NA values.

      >>> df = pd.DataFrame(
      ...     {
      ...         "first_name": ["John", "Anne", "John", "Beth"],
      ...         "middle_name": ["Smith", pd.NA, pd.NA, "Louise"],
      ...     }
      ... )
      >>> df
        first_name middle_name
      0       John       Smith
      1       Anne         NaN
      2       John         NaN
      3       Beth      Louise

      >>> df.value_counts()
      first_name  middle_name
      John        Smith          1
      Beth        Louise         1
      Name: count, dtype: int64

      >>> df.value_counts(dropna=False)
      first_name  middle_name
      John        Smith          1
      Anne        NaN            1
      John        NaN            1
      Beth        Louise         1
      Name: count, dtype: int64

      >>> df.value_counts("first_name")
      first_name
      John    2
      Anne    1
      Beth    1
      Name: count, dtype: int64