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Counting non-missing values in Pandas

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Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
Tags

To count non-missing values in rows or columns of a Pandas DataFrame use the count(~) method.

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

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.

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.

Numeric and boolean columns/rows only

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 Arthur Yanagisawa
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