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Classe « DataFrame »

Méthode pandas.DataFrame.count

Signature de la méthode count

def count(self, axis: 'Axis' = 0, numeric_only: 'bool' = False) 

Description

help(DataFrame.count)

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_only : bool, 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

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