Как подсчитать количество значений NaN в пандах?

Опубликовано: 27 Марта, 2022

Нам может потребоваться подсчитать количество значений NaN для каждой функции в наборе данных, чтобы мы могли решить, как с ней бороться. Например, если количество пропущенных значений довольно мало, мы можем отказаться от этих наблюдений; или может быть столбец, в котором отсутствует много записей, поэтому мы можем решить, включать ли вообще эту переменную.

Метод 1. Использование description ()

We can use the describe() method which returns a table containing details about the dataset. The count property directly gives the count of non-NaN values in each column. So, we can get the count of NaN values, if we know the total number of observations.

import pandas as pd 
import numpy as np
    
# dictionary of lists 
dict = { "A":[1, 4, 6, 9], 
        "B":[np.NaN, 5, 8, np.NaN], 
        "C":[7, 3, np.NaN, 2],
        "D":[1, np.NaN, np.NaN, np.NaN] } 
  
# creating dataframe from the
# dictionary 
data = pd.DataFrame(dict) 
    
data.describe()

Output :

Method 2: Using sum()
The isnull() function returns a dataset containing True and False values. Since, True is treated as a 1 and False as 0, calling the sum() method on the isnull() series returns the count of True values which actually corresponds to the number of NaN values.

Counting NaN in a column :

We can simply find the null values in the desired column, then get the sum.

import pandas as pd
import numpy as np
    
# dictionary of lists 
dict = { "A":[1, 4, 6, 9], 
        "B":[np.NaN, 5, 8, np.NaN], 
        "C":[7, 3, np.NaN, 2],
        "D":[1, np.NaN, np.NaN, np.NaN] } 
    
# creating dataframe from the
# dictionary 
data = pd.DataFrame(dict) 
  
# total NaN values in column "B"
print(data["B"].isnull().sum())

Output :

Counting NaN in a row :

The row can be selected using loc or iloc. Then we find the sum as before.

import pandas as pd 
import numpy as np
    
# dictionary of lists 
dict = { "A":[1, 4, 6, 9],
        "B":[np.NaN, 5, 8, np.NaN], 
        "C":[7, 3, np.NaN, 2],
        "D":[1, np.NaN, np.NaN, np.NaN] }   
    
# creating dataframe from the 
# dictionary 
data = pd.DataFrame(dict) 
  
# total NaN values in row index 1
print(data.loc[1, :].isnull().sum())

Output :

Counting NaN in the entire DataFrame :
To count NaN in the entire dataset, we just need to call the sum() function twice – once for getting the count in each column and again for finding the total sum of all the columns.

import pandas as pd 
import numpy as np
    
# dictionary of lists 
dict = {"A":[1, 4, 6, 9],
        "B":[np.NaN, 5, 8, np.NaN],
        "C":[7, 3, np.NaN, 2],
        "D":[1, np.NaN, np.NaN, np.NaN]} 
    
# creating dataframe from the
# dictionary 
data = pd.DataFrame(dict) 
  
# total count of NaN values
print(data.isnull().sum().sum())

Output :

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Как подсчитать количество значений NaN в пандах?

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