计算熊猫数据框中的 NaN 或缺失值

原文:https://www . geesforgeks . org/count-nan-or-missing-values-in-pandas-data frame/

在本文中，我们将看到如何使用数据框的 isnull() 和 sum() 方法计算熊猫数据框中的 NaN 或缺失值。

Dataframe.isnull()方法

熊猫isnull()功能检测给定对象中缺失的值。它返回一个相同大小的布尔对象，指示值是否为“无”。缺失值映射为真，非缺失值映射为假。

语法:data frame . isnnull()

参数:无

返回类型:布尔值的数据框，对于 NaN 值为真，否则为假。

dataframe.sum()方法

熊猫sum()函数返回所请求轴的数值总和。如果输入是索引轴，则它会将一列中的所有值相加，并对所有列重复相同的操作，然后返回一个包含每列中所有值之和的序列。它还支持在计算。

语法: DataFrame.sum(轴=无，skipna =无，级别=无，numeric _ only =无，min_count=0，**kwargs)

参数:

  • 轴:
  • skipna : 计算结果时排除 NA/null 值。
  • 级别:如果轴是一个多索引(分层)，沿特定级别计数，折叠成一个系列
  • numeric_only : 仅包括浮点、整型和布尔型列。如果没有，将尝试使用所有内容，然后只使用数字数据。不适用于系列。
  • min_count : 执行操作所需的有效值数量。如果存在少于最小计数的非数值，结果将为“不适用”。

返回:系列或数据帧的总和(如果指定了级别)。

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:

PAndas-count-nan

示例 1 : 计算数据框中每一列的总 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:

pandas-count-nan-2

示例 2 : 计算数据框中每行的总 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:

例 3 : 统计数据帧中的总 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:

pandas-count-nan-4

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