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# pandas.Series.value_counts

Series.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:
    normalize bool, default False

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

    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, the sort was unstable.

    ascending bool, default False

      Sort in ascending order.

    bins int, optional

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

    dropna bool, default True

      Don’t include counts of NaN.

  Returns:
    Series

      Series containing counts of unique values.

  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

  Categorical Dtypes

  Rows with categorical type will be counted as one group if they have same categories and order. In the example below, even though `a`, `c`, and `d` all have the same data types of `category`, only `c` and `d` will be counted as one group since `a` doesn’t have the same categories.

      >>> df = pd.DataFrame({"a": [1], "b": ["2"], "c": [3], "d": [3]})
      >>> df = df.astype({"a": "category", "c": "category", "d": "category"})
      >>> df
         a  b  c  d
      0  1  2  3  3

      >>> df.dtypes
      a    category
      b      str
      c    category
      d    category
      dtype: object

      >>> df.dtypes.value_counts()
      category    2
      category    1
      str         1
      Name: count, dtype: int64