NaN stands for Not A Number and is one of the common ways to represent the missing value in the data. It is a special floating-point value and cannot be converted to any other type than float.
NaN value is one of the major problems in Data Analysis. It is very essential to deal with NaN in order to get the desired results.
Check for NaN Value in Pandas DataFrame
The ways to check for NaN in Pandas DataFrame are as follows:
- Check for NaN with isnull().values.any() method
- Count the NaN Using isnull().sum() Method
- Check for NaN Using isnull().sum().any() Method
- Count the NaN Using isnull().sum().sum() Method
Method 1: Using isnull().values.any() method
Example:
Python3
# importing librariesimport pandas as pdimport numpy as npnum = {'Integers': [10, 15, 30, 40, 55, np.nan, 75, np.nan, 90, 150, np.nan]}# Create the dataframedf = pd.DataFrame(num, columns=['Integers'])# Applying the methodcheck_nan = df['Integers'].isnull().values.any()# printing the resultprint(check_nan) |
Output:
True
It is also possible to get the exact positions where NaN values are present. We can do so by removing .values.any() from isnull().values.any() .
Python3
df['Integers'].isnull() |
Output:
0 False 1 False 2 False 3 False 4 False 5 True 6 False 7 True 8 False 9 False 10 True Name: Integers, dtype: bool
Method 2: Using isnull().sum() Method
Example:
Python3
# importing librariesimport pandas as pdimport numpy as npnum = {'Integers': [10, 15, 30, 40, 55, np.nan, 75, np.nan, 90, 150, np.nan]}# Create the dataframedf = pd.DataFrame(num, columns=['Integers'])# applying the methodcount_nan = df['Integers'].isnull().sum()# printing the number of values present# in the columnprint('Number of NaN values present: ' + str(count_nan)) |
Output:
Number of NaN values present: 3
Method 3: Using isnull().sum().any() Method
Example:
Python3
# importing librariesimport pandas as pdimport numpy as npnums = {'Integers_1': [10, 15, 30, 40, 55, np.nan, 75, np.nan, 90, 150, np.nan], 'Integers_2': [np.nan, 21, 22, 23, np.nan, 24, 25, np.nan, 26, np.nan, np.nan]}# Create the dataframedf = pd.DataFrame(nums, columns=['Integers_1', 'Integers_2'])# applying the methodnan_in_df = df.isnull().sum().any()# Print the dataframeprint(nan_in_df) |
Output:
True
To get the exact positions where NaN values are present, we can do so by removing .sum().any() from isnull().sum().any() .
Method 4: Using isnull().sum().sum() Method
Example:
Python3
# importing librariesimport pandas as pdimport numpy as npnums = {'Integers_1': [10, 15, 30, 40, 55, np.nan, 75, np.nan, 90, 150, np.nan], 'Integers_2': [np.nan, 21, 22, 23, np.nan, 24, 25, np.nan, 26, np.nan, np.nan]}# Create the dataframedf = pd.DataFrame(nums, columns=['Integers_1', 'Integers_2'])# applying the methodnan_in_df = df.isnull().sum().sum()# printing the number of values present in# the whole dataframeprint('Number of NaN values present: ' + str(nan_in_df)) |
Output:
Number of NaN values present: 8
