Python Pandas DataFrame notnull to check all missing or NaN or NA values

Pandas

Check for Not Null values and map them as True

Return the masked bool values of each element.

Examples

import pandas as pd
import numpy as np 
my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],
         'ID':[1,2,np.NaN,4,5,6],
         'MATH':[80,40,70,70,82,30],
         'ENGLISH':[81,70,40,50,np.NaN,30]}
df = pd.DataFrame(data=my_dict)
print(df.notnull())

Output : All NOT None and NOT NaN values are masked to True and others are given as False

    NAME     ID   MATH  ENGLISH
0  False  False  False    False
1  False  False  False    False
2  False   True  False    False
3   True  False  False    False
4  False  False  False     True
5   True  False  False    False

In real life situation we get data in Excel or CSV file or from any database. We will try to get data from an Excel file and using the same data we will create our DataFrame.

Read more on how to create DataFrame by reading Excel file.

Download student-isnull.xlsx file

import pandas as pd 
import numpy as np
# Check your path for excel file
my_data = pd.read_excel('D:\student-isnull.xlsx') 
print(my_data)

Output

      id         name class1  mark     sex
0    1.0     John Deo   Four  75.0  female
1    2.0     Max Ruin  Three  85.0    male
2    NaN       Arnold  Three  55.0    male
3    4.0   Krish Star   Four  60.0  female
4    NaN    John Mike   Four  60.0  female
5    6.0    Alex John   Four  55.0     NaN
6    7.0  My John Rob   Five  78.0    male
7    NaN          NaN    NaN   NaN     NaN
8    9.0      Tes Qry    Six  78.0    male
9   10.0          NaN   Four  55.0  female
10  11.0       Ronald    Six   NaN  female
11  12.0        Recky    Six  94.0  female

Checking if NaN is there or not

We can check if there is any actual data ( Not NaN) value is there or not in our DataSet.

print(my_data.notnull().values.any())

Output ( returns True if any value in DataFrame is real data by using any() )

True

We can check any column for presence of any Not NaN or Not None value.
We are checking name column only here

print(my_data['name'].notnull().values.any())

Two columns name and mark we will check for NaN or None value.

print(my_data[['name','mark']].notnull().values.any())

In above case we can check all values by using all()

Showing rows not having any NaN value ( in column )

Display where id column is not having NaN value

print(my_data[my_data['id'].notnull()])

Output ( all null values in id column are removed )

      id         name class1  mark     sex
0    1.0     John Deo   Four  75.0  female
1    2.0     Max Ruin  Three  85.0    male
3    4.0   Krish Star   Four  60.0  female
5    6.0    Alex John   Four  55.0     NaN
6    7.0  My John Rob   Five  78.0    male
8    9.0      Tes Qry    Six  78.0    male
9   10.0          NaN   Four  55.0  female
10  11.0       Ronald    Six   NaN  female
11  12.0        Recky    Six  94.0  female

Display where name column is not having NaN value

print(my_data[my_data['name'].notnull()])

Output ( two rows 7 & 9 are removed as they are having NaN data in Name column )

      id         name class1  mark     sex
0    1.0     John Deo   Four  75.0  female
1    2.0     Max Ruin  Three  85.0    male
2    NaN       Arnold  Three  55.0    male
3    4.0   Krish Star   Four  60.0  female
4    NaN    John Mike   Four  60.0  female
5    6.0    Alex John   Four  55.0     NaN
6    7.0  My John Rob   Five  78.0    male
8    9.0      Tes Qry    Six  78.0    male
10  11.0       Ronald    Six   NaN  female
11  12.0        Recky    Six  94.0  female

Similarly other column names can be used.

Counting Number of NaN elements

We will count total number of actual data ( not NaN ) data present and find out the number of real data in each columns.

Read more on sum() here.

Total number of Not NaN present inside different columns ( of our sample excel file )

print(my_data['id'].notnull().sum())     # output 9
print(my_data['name'].notnull().sum())   # output 10
print(my_data['class1'].notnull().sum()) # output 11 
print(my_data['mark'].notnull().sum())   # output 10
print(my_data['sex'].notnull().sum())    # output 10

We will count the number of not NaN for total DataFrame. First we will display the breakup of total number against each columns.

print(my_data.notnull().sum()) # output each column wise

Output

id         9
name      10
class1    11
mark      10
sex       10

For the total number of Not NaN of the DataFrame.

print(my_data.notnull().sum().sum())    # Output 50

Filling NaN values by fillna()

isnull() can identify or count the missing values in a DataFrame. We can replace these values by using fillna()
fillna() isnull()

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