Skip to main content
`Ctrl`+`K`

# pandas.DataFrame.count

DataFrame.count(axis=0, numeric_only=False)

  Count non-NA cells for each column or row.

  The values None, NaN, NaT, `pandas.NA` are considered NA.

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

      If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.

    numeric_onlybool, default False

      Include only float, int or boolean data.

  Returns:
    Series

      For each column/row the number of non-NA/null entries.

  See also

  `Series.count`

    Number of non-NA elements in a Series.

  `DataFrame.value_counts`

    Count unique combinations of columns.

  `DataFrame.shape`

    Number of DataFrame rows and columns (including NA elements).

  `DataFrame.isna`

    Boolean same-sized DataFrame showing places of NA elements.

  Examples

  Constructing DataFrame from a dictionary:

      >>> df = pd.DataFrame({"Person":
      ...                    ["John", "Myla", "Lewis", "John", "Myla"],
      ...                    "Age": [24., np.nan, 21., 33, 26],
      ...                    "Single": [False, True, True, True, False]})
      >>> df
         Person   Age  Single
      0    John  24.0   False
      1    Myla   NaN    True
      2   Lewis  21.0    True
      3    John  33.0    True
      4    Myla  26.0   False

  Notice the uncounted NA values:

      >>> df.count()
      Person    5
      Age       4
      Single    5
      dtype: int64

  Counts for each row:

      >>> df.count(axis='columns')
      0    3
      1    2
      2    3
      3    3
      4    3
      dtype: int64