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# pandas.DataFrame.quantile

DataFrame.quantile(q=0.5, axis=0, numeric_only=False, interpolation='linear', method='single')

  Return values at the given quantile over requested axis.

  Parameters:
    qfloat or array-like, default 0.5 (50% quantile)

      Value between 0 <= q <= 1, the quantile(s) to compute.

    axis{0 or ‘index’, 1 or ‘columns’}, default 0

      Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

    numeric_onlybool, default False

      Include only float, int or boolean data.

      Changed in version 2.0.0: The default value of `numeric_only` is now `False`.

    interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

      This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:

      - linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.

      - lower: i.

      - higher: j.

      - nearest: i or j whichever is nearest.

      - midpoint: (i + j) / 2.

    method{‘single’, ‘table’}, default ‘single’

      Whether to compute quantiles per-column (‘single’) or over all columns (‘table’). When ‘table’, the only allowed interpolation methods are ‘nearest’, ‘lower’, and ‘higher’.

  Returns:
    Series or DataFrame
      If `q` is an array, a DataFrame will be returned where the

        index is `q`, the columns are the columns of self, and the values are the quantiles.

      If `q` is a float, a Series will be returned where the

        index is the columns of self and the values are the quantiles.

  See also

  `core.window.rolling.Rolling.quantile`

    Rolling quantile.

  `numpy.percentile`

    Numpy function to compute the percentile.

  Examples

      >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
      ...                   columns=['a', 'b'])
      >>> df.quantile(.1)
      a    1.3
      b    3.7
      Name: 0.1, dtype: float64
      >>> df.quantile([.1, .5])
             a     b
      0.1  1.3   3.7
      0.5  2.5  55.0

  Specifying method=’table’ will compute the quantile over all columns.

      >>> df.quantile(.1, method="table", interpolation="nearest")
      a    1
      b    1
      Name: 0.1, dtype: int64
      >>> df.quantile([.1, .5], method="table", interpolation="nearest")
           a    b
      0.1  1    1
      0.5  3  100

  Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

      >>> df = pd.DataFrame({'A': [1, 2],
      ...                    'B': [pd.Timestamp('2010'),
      ...                          pd.Timestamp('2011')],
      ...                    'C': [pd.Timedelta('1 days'),
      ...                          pd.Timedelta('2 days')]})
      >>> df.quantile(0.5, numeric_only=False)
      A                    1.5
      B    2010-07-02 12:00:00
      C        1 days 12:00:00
      Name: 0.5, dtype: object