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Pandas DataFrame | count method

Pandas
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.count(~) method counts the number of non-missing values for each row or column of the DataFrame.

Parameters

1. axislink | string or int | optional

Whether to check each column or row:

Axis

Description

Count each column.

0 or "index"

Count each row.

1 or "columns"

By default, axis=0.

2. level | int or string | optional

The level to check. This is only relevant if the source DataFrame has MultiIndex.

3. numeric_onlylink | boolean | optional

  • If True, then the method will perform the count on columns/rows of type number or boolean.

  • If False, then all columns/rows will be counted.

By default, numeric_only=False.

Return Value

A Series of int that indicates the number of missing values for each row/column of the source DataFrame.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[pd.np.nan,pd.np.nan], "B":[3,4]})
df
A B
0 NaN 3
1 NaN 4

Counting non-missing values column-wise

To count the number of non-missing values for each column:

df.count() # axis=0
A 0
B 2
dtype: int64

Here, we have 0 non-NaN values in column A, and 2 non-NaN values in B.

Counting non-missing values row-wise

To count the number of non-missing values for each row, set axis=1:

df.count(axis=1)
0 1
1 1
dtype: int64

Here, we have 1 non-missing value in both row 0 and row 1.

Counting only numeric and boolean columns/rows

Consider the following DataFrame:

df = pd.DataFrame({"A":["a","b"], "B":[3,4]})
df
A B
0 a 3
1 b 4

To count only numeric and boolean columns, set numeric_only=True:

df.count(numeric_only=True)
B 2
dtype: int64

Notice how column A is ignored since it is a non-numeric type.

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Published by Isshin Inada
Edited by 0 others
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