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NaNStands for Not a Number- Not a Number, which indicates missing values ​​in Pandas. To detect NaN values ​​in Python Pandas, we can use the isnull()and isna()methods on the DataFrame object.

pandas.DataFrame.isnull() method

We can use pandas.DataFrame.isnull() to check for NaN values ​​in a DataFrame. DataFrameThe method returns a boolean value of the DataFrame element if the corresponding element in the DataFrame to be checked has a NaN value True, else it is False.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "Student": ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"],
        "Height": [1.63, 1.5, np.nan, np.nan, 1.4],
        "Weight": [np.nan, 56, 73, np.nan, 44],
    }
)

df_check = df.isnull()

print(df_check)

Output:

   Student  Height  Weight
0    False   False    True
1    False   False   False
2    False    True   False
3    False    True    True
4    False   False   False

Here, Falsethe values ​​in the output represent dfthe elements in the DataFrame NaNand Truethe values ​​represent the elements dfin the DataFrame NaN.

If we want to know whether there are NaN values ​​in the DataFrame, we can use isnull().values.any()the method, which returns True if there are any NaN values ​​in the DataFrame and False if there is not even a single NaN element in the DataFrame.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "Student": ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"],
        "Height": [1.63, 1.5, np.nan, np.nan, 1.4],
        "Weight": [np.nan, 56, 73, np.nan, 44],
    }
)

check_for_nan = df.isnull().values.any()
print(check_for_nan)

Output:

True

df.isnull().valuesnumpy.any()Returns a NumPy representation of the DataFrame. Returns True if any element evaluates to True .

Therefore, if any exists in the DataFrame NaN, then df.isnull().values.any()is True.

df.isnull().any().any()Check if there are any NaNs

df.any()Returns whether any element is True. dfIt returns one when is a DataFrame pd.Seriesand a boolean when dfis .pd.Series

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "Student": ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"],
        "Height": [1.63, 1.5, np.nan, np.nan, 1.4],
        "Weight": [np.nan, 56, 73, np.nan, 44],
    }
)

check_for_nan = df.isnull().any().any()

print(check_for_nan)

Output:

True

The two concatenated methods after NaNin the above example will return if any element in the DataFrame is .isnull()any()True

isnull().sum().sum()Checking for the presence of NaN

If we want to count the total number of NaN values ​​in a particular DataFrame, then df.isnull().sum().sum()the method is the right solution. This method returns the total number of values ​​in the entire DataFrame NaN.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "Student": ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"],
        "Height": [1.63, 1.5, np.nan, np.nan, 1.4],
        "Weight": [np.nan, 56, 73, np.nan, 44],
    }
)

total_nan_values = df.isnull().sum().sum()
print(total_nan_values)

Output:

4

If the result is greater than 0, it means it exists in the DataFrame NaN.

pandas.DataFrame.isna()method

The pandas.DataFrame.isna() method is similar to
pandas.DataFrame.isnull(). There is no difference between the working of both the methods. They are just named differently.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "Student": ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"],
        "Height": [1.63, 1.5, np.nan, np.nan, 1.4],
        "Weight": [np.nan, 56, 73, np.nan, 44],
    }
)

df_check = df.isna()

check_for_any_nan = df.isna().values.any()
# Or
check_for_any_nan = df.isna().any().any()
total_nan_values = df.isna().sum().sum()
print(df_check)
print("NaN Presence:" + str(check_for_any_nan))
print("Total Number of NaN values:" + str(total_nan_values))

Output:

   Student  Height  Weight
0    False   False    True
1    False   False   False
2    False    True   False
3    False    True    True
4    False   False   False
NaN Presence:True
Total Number of NaN values:4

Here, the method df.isna()returns a DataFrame whose elements contain Boolean values ​​indicating the presence of NaN values ​​in df. Similarly, df.isna().values.any(), df.isna().any().any()and df.isna().sum().sum()return the number of NaN values ​​present in the entire df and the number of NaN elements in df.