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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:
    subsetlabel or list of labels, optional

      Columns to use when counting unique combinations.

    normalizebool, default False

      Return proportions rather than frequencies.

    sortbool, default True

      Sort by frequencies when True. Sort by DataFrame column values when False.

    ascendingbool, default False

      Sort in ascending order.

    dropnabool, default True

      Don’t include counts of rows that contain NA values.

      Added in version 1.3.0.

  Returns:
    Series

  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 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        <NA>
      2       John        <NA>
      3       Beth      Louise

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

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

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