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How to count the NaN values in a column in a Python Pandas DataFrame?
To count the NaN values in a column in a Pandas DataFrame, we can use the isna() method with sum.
Steps
Create a series, s, one-dimensional ndarray with axis labels (including time series).
Print the series, s.
Count the number of NaN present in the series.
Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.
Print the input DataFrame.
Find NaN count column wise.
Print the count DataFrame.
Example
import pandas as pd import numpy as np s = pd.Series([1, np.nan, 3, np.nan, 3, np.nan, 7, np.nan, 3]) print "Input series is:
", s count = s.isna().sum() print "NAN count in series: ", count df = pd.DataFrame( { "x": [5, np.nan, 1, np.nan], "y": [np.nan, 1, np.nan, 10], "z": [np.nan, 1, np.nan, np.nan] } ) print "
Input DataFrame is:
", df count = df.isna().sum() print "
NAN count in DataFrame:
", count
Output
Input series is: 0 1.0 1 NaN 2 3.0 3 NaN 4 3.0 5 NaN 6 7.0 7 NaN 8 3.0 dtype: float64 NAN count in series: 4 Input DataFrame is: x y z 0 5.0 NaN NaN 1 NaN 1.0 1.0 2 1.0 NaN NaN 3 NaN 10.0 NaN NAN count in DataFrame: x 2 y 2 z 3 dtype: int64
Updated on: 30-Aug-2021
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