在 Pandas DataFrame 中计算 NaN 或缺失值

在本文中，我们将了解如何使用 DataFrame 的isnull()和sum()方法计算 Pandas DataFrame 中的 NaN 或缺失值。

Dataframe.isnull() 方法

Pandas isnull()函数检测给定对象中的缺失值。它返回一个布尔值相同大小的对象，指示值是否为 NA。缺失值映射为 True，非缺失值映射为 False。

Syntax: DataFrame.isnull()

Parameters: None

Return Type: Dataframe of Boolean values which are True for NaN values otherwise False.

dataframe.sum() 方法

Pandas sum()函数返回请求轴的值的总和。如果输入是索引轴，则它将列中的所有值相加，并对所有列重复相同的操作，并返回一个包含每列中所有值之和的序列。它还支持在计算时跳过缺失值。

Syntax: DataFrame.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Parameters :

• axis : {index (0), columns (1)}
• skipna : Exclude NA/null values when computing the result.
• level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
• numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
• min_count : The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns : sum of Series or DataFrame (if level specified).

让我们创建一个熊猫数据框。

# import numpy library as np
import numpy as np
  
# import pandas library as pd
import pandas as pd
  
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
           ('Ankita', np.NaN, 'Delhi', np.NaN),
           ('Rahul', 16, 'Tokyo', 'Abes'),
           ('Simran', 41, 'Delhi', 'Gehu'),
           ('Shaurya', np.NaN, 'Delhi', 'Geu'),
           ('Shivangi', 35, 'Mumbai', np.NaN ),
           ('Swapnil', 35, np.NaN, 'Geu'),
           (np.NaN, 35, 'Uk', 'Geu'),
           ('Jeet', 35, 'Guj', 'Gehu'),
           (np.NaN, np.NaN, np.NaN, np.NaN)
            ]
  
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age', 
                                           'Place', 'College'],
                        index =['a', 'b', 'c', 'd', 'e', 
                                'f', 'g', 'i', 'j', 'k'])
  
details

输出：

示例 1：计算 DataFrame 中每一列的总 NaN。

# import numpy library as np
import numpy as np
  
# import pandas library as pd
import pandas as pd
  
  
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
           ('Ankita', np.NaN, 'Delhi', np.NaN),
           ('Rahul', 16, 'Tokyo', 'Abes'),
           ('Simran', 41, 'Delhi', 'Gehu'),
           ('Shaurya', np.NaN, 'Delhi', 'Geu'),
           ('Shivangi', 35, 'Mumbai', np.NaN ),
           ('Swapnil', 35, np.NaN, 'Geu'),
           (np.NaN, 35, 'Uk', 'Geu'),
           ('Jeet', 35, 'Guj', 'Gehu'),
           (np.NaN, np.NaN, np.NaN, np.NaN)
            ]
  
# Create a DataFrame object from list of tuples 
# with columns and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
                                           'Place', 'College'],
                        index =['a', 'b', 'c', 'd', 'e', 
                                'f', 'g', 'i', 'j', 'k'])
  
# show the boolean dataframe            
print(" \nshow the boolean Dataframe : \n\n", details.isnull())
  
# Count total NaN at each column in a DataFrame
print(" \nCount total NaN at each column in a DataFrame : \n\n",
      details.isnull().sum())

输出：

示例 2：计算 DataFrame 中每一行的总 NaN。

# import numpy library as np
import numpy as np
  
# import pandas library as pd
import pandas as pd
  
  
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
           ('Ankita', np.NaN, 'Delhi', np.NaN),
           ('Rahul', 16, 'Tokyo', 'Abes'),
           ('Simran', 41, 'Delhi', 'Gehu'),
           ('Shaurya', np.NaN, 'Delhi', 'Geu'),
           ('Shivangi', 35, 'Mumbai', np.NaN ),
           ('Swapnil', 35, np.NaN, 'Geu'),
           (np.NaN, 35, 'Uk', 'Geu'),
           ('Jeet', 35, 'Guj', 'Gehu'),
           (np.NaN, np.NaN, np.NaN, np.NaN)
            ]
  
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age', 
                                           'Place', 'College'],
                        index =['a', 'b', 'c', 'd', 'e',
                                'f', 'g', 'i', 'j', 'k'])
  
# show the boolean dataframe            
print(" \nshow the boolean Dataframe : \n\n", details.isnull())
  
# index attribute of a dataframe
# gives index list 
  
# Count total NaN at each row in a DataFrame
for i in range(len(details.index)) :
    print(" Total NaN in row", i + 1, ":",
          details.iloc[i].isnull().sum())

输出：

示例 3：计算 DataFrame 中的总 NaN。

# import numpy library as np
import numpy as np
  
# import pandas library as pd
import pandas as pd
  
  
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
           ('Ankita', np.NaN, 'Delhi', np.NaN),
           ('Rahul', 16, 'Tokyo', 'Abes'),
           ('Simran', 41, 'Delhi', 'Gehu'),
           ('Shaurya', np.NaN, 'Delhi', 'Geu'),
           ('Shivangi', 35, 'Mumbai', np.NaN ),
           ('Swapnil', 35, np.NaN, 'Geu'),
           (np.NaN, 35, 'Uk', 'Geu'),
           ('Jeet', 35, 'Guj', 'Gehu'),
           (np.NaN, np.NaN, np.NaN, np.NaN)
            ]
  
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age', 
                                           'Place', 'College'],
                        index =['a', 'b', 'c', 'd', 'e',
                                'f', 'g', 'i', 'j', 'k'])
  
# show the boolean dataframe            
print(" \nshow the boolean Dataframe : \n\n", details.isnull())
  
# Count total NaN in a DataFrame
print(" \nCount total NaN in a DataFrame : \n\n",
       details.isnull().sum().sum())

输出：