Skip to main content
`Ctrl`+`K`

# pandas.Index.value_counts

Index.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

  Return a Series containing counts of unique values.

  The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.

  Parameters:
    normalizebool, default False

      If True then the object returned will contain the relative frequencies of the unique values.

    sortbool, default True

      Sort by frequencies when True. Preserve the order of the data when False.

    ascendingbool, default False

      Sort in ascending order.

    binsint, optional

      Rather than count values, group them into half-open bins, a convenience for `pd.cut`, only works with numeric data.

    dropnabool, default True

      Don’t include counts of NaN.

  Returns:
    Series

  See also

  `Series.count`

    Number of non-NA elements in a Series.

  `DataFrame.count`

    Number of non-NA elements in a DataFrame.

  `DataFrame.value_counts`

    Equivalent method on DataFrames.

  Examples

      >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
      >>> index.value_counts()
      3.0    2
      1.0    1
      2.0    1
      4.0    1
      Name: count, dtype: int64

  With normalize set to True, returns the relative frequency by dividing all values by the sum of values.

      >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
      >>> s.value_counts(normalize=True)
      3.0    0.4
      1.0    0.2
      2.0    0.2
      4.0    0.2
      Name: proportion, dtype: float64

  bins

  Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins.

      >>> s.value_counts(bins=3)
      (0.996, 2.0]    2
      (2.0, 3.0]      2
      (3.0, 4.0]      1
      Name: count, dtype: int64

  dropna

  With dropna set to False we can also see NaN index values.

      >>> s.value_counts(dropna=False)
      3.0    2
      1.0    1
      2.0    1
      4.0    1
      NaN    1
      Name: count, dtype: int64