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Pandas: Count the NaN values in one or more columns in DataFrame

Pandas: DataFrame Exercise-35 with Solution

Write a Pandas program to count the NaN values in one or more columns in DataFrame.

Sample data:
Original DataFrame
attempts name qualify score
0 1 Anastasia yes 12.5
1 3 Dima no 9.0
2 2 Katherine yes 16.5
3 3 James no NaN
4 2 Emily no 9.0
5 3 Michael yes 20.0
6 1 Matthew yes 14.5
7 1 Laura no NaN
8 2 Kevin no 8.0
9 1 Jonas yes 19.0
Number of NaN values in one or more columns:
2

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
        'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
        'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
df = pd.DataFrame(exam_data)
print("Original DataFrame")
print(df)
print("\nNumber of NaN values in one or more columns:")
print(df.isnull().values.sum())

Sample Output:

      Original DataFrame
   attempts       name qualify  score
0         1  Anastasia     yes   12.5
1         3       Dima      no    9.0
2         2  Katherine     yes   16.5
3         3      James      no    NaN
4         2      Emily      no    9.0
5         3    Michael     yes   20.0
6         1    Matthew     yes   14.5
7         1      Laura      no    NaN
8         2      Kevin      no    8.0
9         1      Jonas     yes   19.0

Number of NaN values in one or more columns:
2

Python Code Editor:


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Python: Tips of the Day

Creates a dictionary with the same keys as the provided dictionary and values generated by running the provided function for each value

Example:

def tips_map_values(obj, fn):
  ret = {}
  for key in obj.keys():
    ret[key] = fn(obj[key])
  return ret
users = {
  'Owen': { 'user': 'Owen', 'age': 29 },
  'Eddie': { 'user': 'Eddie', 'age': 15 }
}

print(tips_map_values(users, lambda u : u['age'])) # {'Owen': 29, 'Eddie': 15}

Output:

{'Owen': 29, 'Eddie': 15}