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# pandas.DataFrame.mode

DataFrame.mode(axis=0, numeric_only=False, dropna=True)

  Get the mode(s) of each element along the selected axis.

  The mode of a set of values is the value that appears most often. It can be multiple values.

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

      The axis to iterate over while searching for the mode:

      - 0 or ‘index’ : get mode of each column

      - 1 or ‘columns’ : get mode of each row.

    numeric_only bool, default False

      If True, only apply to numeric columns.

    dropna bool, default True

      Don’t consider counts of NaN/NaT.

  Returns:
    DataFrame

      The modes of each column or row.

  See also

  `Series.mode`

    Return the highest frequency value in a Series.

  `Series.value_counts`

    Return the counts of values in a Series.

  Examples

      >>> df = pd.DataFrame(
      ...     [
      ...         ("bird", 2, 2),
      ...         ("mammal", 4, np.nan),
      ...         ("arthropod", 8, 0),
      ...         ("bird", 2, np.nan),
      ...     ],
      ...     index=("falcon", "horse", "spider", "ostrich"),
      ...     columns=("species", "legs", "wings"),
      ... )
      >>> df
                 species  legs  wings
      falcon        bird     2    2.0
      horse       mammal     4    NaN
      spider   arthropod     8    0.0
      ostrich       bird     2    NaN

  By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of `species` and `legs` contains `NaN`.

      >>> df.mode()
        species  legs  wings
      0    bird   2.0    0.0
      1     NaN   NaN    2.0

  Setting `dropna=False` `NaN` values are considered and they can be the mode (like for wings).

      >>> df.mode(dropna=False)
        species  legs  wings
      0    bird     2    NaN

  Setting `numeric_only=True`, only the mode of numeric columns is computed, and columns of other types are ignored.

      >>> df.mode(numeric_only=True)
         legs  wings
      0   2.0    0.0
      1   NaN    2.0

  To compute the mode over columns and not rows, use the axis parameter:

      >>> df.mode(axis="columns", numeric_only=True)
                 0    1
      falcon   2.0  NaN
      horse    4.0  NaN
      spider   0.0  8.0
      ostrich  2.0  NaN