How to Check if NaN Exisits in Pandas DataFrame

  1. pandas.DataFrame.isnull() Method
  2. pandas.DataFrame.isna() Method

NaN stands for Not a Number that represents missing values in Pandas. To detect NaN values in Python Pandas we can use isnull() andisna() methods for DataFrame objects.

pandas.DataFrame.isnull() Method

We can check for NaN values in DataFrame using pandas.DataFrame.isnull() method. The method returns DataFrame of bool values whose elements are True if the corresponding elements in DataFrame to be checked have NaN value, and the elements are False otherwise.

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, the False values in output represent the entries in DataFrame df are not NaN and True values represent NaN entries in the DataFrame df.

If we want to know if there is any NaN value in the DataFrame, we can use the isnull().values.any() method that returns True if there is any NaN value in the DataFrame and returns False if there is not even a single NaN entry 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().values returns the Numpy representation of the dataframe. numpy.any() returns True if any of the elements is evaluated to be True.

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

df.isnull().any().any() to Check if Any NaN Exists

df.any() returns whether any of the elements is True. It returns a pd.Series when df is a dataframe, and a boolean value when df is 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

Two cascaded any() methods after isnull() in the above example returns True if any element is NaN in the dataframe.

Isnull().sum().sum() to Check if Any NaN Exists

If we wish to count total number of NaN values in the particular DataFrame, df.isnull().sum().sum() method is the right solution. The method returns total number of NaN values in the entire DataFrame.

import pandas as pd
import numpy as np 

df=pd.DataFrame({'Student':['Hisila', 'Shristi','Zeppy','Alina','Jerry'],
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 that NaN exists in the dataframe.

pandas.DataFrame.isna() Method

pandas.DataFrame.isna() method is similar to pandas.DataFrame.isnull(). There isn’t any difference between the working of two methods. They differ in name only.

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 DataFrame whose entries contain boolean values denoting presence of NaN values in df . Similarly, df.isna().values.any(), df.isna().any().any() and df.isna().sum().sum() return presence of NaN value in the entire df and number of NaN entries in the df.

Related Article - Pandas NaN

  • How to Replace All the NaN Values With Zeros in a Column of a Pandas DataFrame
  • comments powered by Disqus