Df isna sum python

Count NaN Values in Pandas DataFrame

We can count the NaN values in Pandas DataFrame using the isna() function and with the sum() function. NaN stands for Not A Number and is one of the common ways to represent the missing value in the data. In pandas handling missing data is very important before you process it.

None/NaN values are one of the major problems in Data Analysis hence before we process either you need to remove columns that have NaN values or replace NaN with empty for String or replace NaN with zero for numeric columns based on your need. In this article, I will explain how to count the NaN values of a specific column/row of DataFrame or the whole DataFrame using the isna() function with the sum() function.

1. Quick Examples of Count NaN Values in Pandas DataFrame

Now, let’s create a DataFrame with a few rows and columns using Python Dictionary. Our DataFrame contains the column names Courses , Fee , Duration , and Discount and has some NaN values on a string and integer columns.

 df = pd.DataFrame(technologies, index = ['r1', 'r2', 'r3', 'r4', 'r5']) print(df) 

Yields below output. Note that in Pandas nan can be defined by using NumPy np.nan .

2. Pandas Count NaN in a Column

In Pandas DataFrame.isna() function is used to check the missing values and sum() is used to count the NaN values in a column. In this example, I will count the NaN values of a single column from DataFrame using the below syntax. Let’s apply these functions and count the NaN vales. For example,

3. Count NaN Value in All Columns of Pandas DataFrame

You can also get or find the count of NaN values of all columns in a Pandas DataFrame using the isna() function with sum() function. df.isna().sum() this syntax returns the number of NaN values in all columns of a pandas DataFrame in Python.

4. Count NaN Value in the Whole Pandas DataFrame

If we want to count the total number of NaN values in the whole DataFrame, we can use df.isna().sum().sum() , it will return the total number of NaN values in the entire DataFrame.

5. Pandas Count NaN Values in Single Row

So far, we have learned how to count the NaN values in a single/all columns of DataFrame and the whole DataFrame using isna() function with sum(). Now, we will learn how to count the NaN values in a single row of DataFrame.

In order to count NaN values in a single row first, we select the particular row by using Pandas.DataFrame.loc[] attribute and then apply isna() and the sum() functions.

6. Pandas Count NaN Values in All Rows

Using the above functions we can also count the NaN values of all rows. By default sum() function adds all column values whereas to get rows count we have to pass the axis param as ‘1’ into the sum() function, and it will add all row values.

If you want drop rows with NaN values in a DataFrame, you can drop using drop() function.

7. Conclusion

In this article, I have explained how to count the NaN values of a specific column/row of Pandas DataFrame or the entire DataFrame using the isna() function with the sum() function with several examples.

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References

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Как подсчитать NaN-вступления в столбце в Pandas Dataframe

1. метод isna() для подсчета NaN в одной или нескольких колонках
2. Вычитаем общую длину из счета NaN для подсчета NaN вхождений
3. метод df.isnull().sum() для подсчета NaN вхождений
4. Посчитайте NaN происшествия во всем Pandas DataFrame

Мы познакомим вас с методами подсчета NaN вхождений в колонку в Pandas DataFrame . У нас есть много вариантов, в том числе метод isna() для одной или нескольких колонок, путем вычитания общей длины из числа NaN вхождений, с помощью метода value_counts и с помощью метода df.isnull().sum() .

Также мы введем метод для вычисления общего числа NaN вхождений во всем Pandas DataFrame .

метод isna() для подсчета NaN в одной или нескольких колонках

Мы можем использовать метод insna() (pandas versions > 0.21.0), а затем суммировать для подсчета NaN вхождений. Для одного столбца мы сделаем следующее:

import pandas as pd s = pd.Series([ 1,2,3, np.nan, np.nan]) s.isna().sum() # or s.isnull().sum() for older pandas versions 

Для нескольких столбцов это также работает:

import pandas as pd df = pd.DataFrame(  'a':[1,2,np.nan],  'b':[np.nan,1,np.nan]>) df.isna().sum() 

Вычитаем общую длину из счета NaN для подсчета NaN вхождений

Мы можем получить количество NaN вхождений в каждом столбце, вычитая количество non-Nan вхождений из длины DataFrame :

import pandas as pd df = pd.DataFrame([  (1,2,None),  (None,4,None),  (5,None,7),  (5,None,None)],  columns=['a','b','d'],  index = ['A', 'B','C','D']) print(df) print(len(df)-df.count()) 

 a b d A 1.0 2.0 NaN B NaN 4.0 NaN C 5.0 NaN 7.0 D 5.0 NaN NaN a 1 b 2 d 3 dtype: int64 

метод df.isnull().sum() для подсчета NaN вхождений

Получить количество NaN вхождений в каждую колонку можно с помощью метода df.isnull().sum() . Если мы передали axis=0 в методе sum , то получим количество NaN вхождений в каждом столбце. Если нам нужны NaN вхождения в каждой строке, то установим axis=1 .

import pandas as pd  df = pd.DataFrame(  [(1,2,None),  (None,4,None),  (5,None,7),  (5,None,None)],  columns=['a','b','d'],  index = ['A', 'B','C','D'])  print('NaN occurrences in Columns:') print(df.isnull().sum(axis = 0)) print('NaN occurrences in Rows:') print(df.isnull().sum(axis = 1)) 

NaN occurrences in Columns: a 1 b 2 d 3 dtype: int64 NaN occurrences in Rows: A 1 B 2 C 1 D 2 dtype: int64 

Посчитайте NaN происшествия во всем Pandas DataFrame

Чтобы получить общее количество всех случаев NaN в DataFrame , мы связываем два метода .sum() вместе:

import pandas as pd  df = pd.DataFrame(  [(1,2,None),  (None,4,None),  (5,None,7),  (5,None,None)],  columns=['a','b','d'],  index = ['A', 'B','C','D'])  print('NaN occurrences in DataFrame:') print(df.isnull().sum().sum()) 

NaN occurrences in DataFrame: 6 

Сопутствующая статья — Pandas DataFrame

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How to Count Nan Values in Pandas Dataframe? – Definitive Guide

Pandas dataframe stores values in a row and column format, and some data may be missing in the dataset.

You can count NaN values in Pandas dataframe using the df.isna() method.

Basic Example

This tutorial teaches you how to count NaN values in the Pandas dataframe.

Sample Dataframe

To demonstrate the counting of NaN values, first, create a dataframe with the NaN values.

There are three columns, and each column contains a few NaN values.

import pandas as pd import numpy as np data = df = pd.DataFrame(data,columns=['Column 1','Column 2','Column 3']) df

Dataframe Will Look Like

Column 1	Column 2	Column 3
0	1.0	1.0	1.0
1	2.0	2.0	2.0
2	NaN	NaN	NaN
3	4.0	4.0	4.0
4	5.0	NaN	5.0
5	NaN	NaN	NaN
6	NaN	NaN	NaN

Now, you’ll use this dataframe and count the NaN values.

Count Nan Values in Column

The isna() method returns the same sized boolean object indicating if the item is missing value or not.

Count Nan Values in Multiple Columns

In this section, you’ll count the NaN values in a Multiple columns using the isna() method.

• Pass the columns as a list to the isna() method.
• It returns the same sized boolean object indicating if the item is missing value or not.
• Sum the object using the sum() method to get the total number of missing values

df[['Column 1', 'Column 2']].isna().sum()

 Column 1 3 Column 2 4 dtype: int64

Count NaN Values in Every Column Of Dataframe

In this section, you’ll count the NaN values in each column the isna() method.

• Call the isna() method in the dataframe object.
• It returns the same sized boolean object indicating if the item is missing value or not.
• Sum the object using the sum() function to get the total number of missing values

 Column 1 3 Column 2 4 Column 3 3 dtype: int64

Count NaN values in Entire Dataframe

In this section, you’ll count the NaN values in entire dataframe using the isna() method.

• Call the isna() method in the dataframe object.
• It returns the same sized boolean object indicating if the item is missing value or not.
• Sum the object to get the total number of missing values in each column and again invoke the sum() function to count the total number of missing values

Count Nan Value in a specific row

In this section, you’ll learn how to count the NaN values in a specific row of the dataframe.

• Select the desired row of the dataframe using the loc attribute
• use the isna() method and sum() to count the missing values.
• It’ll return the missing values in each column.
• Again invoke the sum() function to calculate the total NaN values in the complete row.

Count Rows with Nan Values

In this section, you’ll learn how to count the number of rows with NaN values.

• Use the isna() method to check if the value is missing
• Use the any(axis=1) method to check if any of the values are missing on axis 1. Axis 1 denotes the row axis
• Use the sum() function to calculate the total number of rows with NaN values

You’ll see output 4 as four rows in the dataframe contains missing values.

Additional Resources

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