pandas.DataFrame.count#

DataFrame.count(axis=0,numeric_only=False)[source]#

Count non-NA cells for each column or row.

The valuesNone,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 onlyfloat,int orboolean 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  Single0    John  24.0   False1    Myla   NaN    True2   Lewis  21.0    True3    John  33.0    True4    Myla  26.0   False

Notice the uncounted NA values:

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

Counts for eachrow:

>>>df.count(axis='columns')0    31    22    33    34    3dtype: int64