Pandas GroupBy 计算列中的出现次数

使用pandas.DataFrame.groupby()的size()或count()方法将生成数据框架中某一列数据出现的次数的计数。然而，这个操作也可以使用pandas.Series.value_counts()和pandas.Index.value_counts()进行。

步骤

• Import module
• 创建或导入数据框架
• Apply groupby
• 使用两种方法中的任何一种
• Display result

方法1：使用pandas.groupyby().si ze()

使用这种方法的基本方法是在groupby()方法中指定列名作为参数，然后使用size()。下面是各种例子，描述了如何为不同的数据集计算一列中的出现次数。

示例 1:

在这个例子中，我们分别计算数据集中所有列的出现次数。

# import module
import pandas as pd
 
 
# assign data
data = pd.DataFrame({'Section': ['A', 'A', 'A', 'B', 'B',
                                 'B', 'C', 'C', 'C'],
                     'Teacher': ['Kakeshi', 'Kakeshi', 'Iruka',
                                 'Kakeshi', 'Kakeshi', 'Kakeshi',
                                 'Iruka', 'Iruka', 'Guy']})
 
# display dataframe
print('Data:')
display(data)
 
 
print('Occurrence counts of particular columns:')
 
# count occurrences a particular column
occur = data.groupby(['Section']).size()
 
# display occurrences of a particular column
display(occur)
 
# count occurrences a particular column
occur = data.groupby(['Teacher']).size()
 
# display occurrences of a particular column
display(occur)

输出:

示例 2:

在下面的程序中，我们从上一个程序中使用的同一数据集中计算所有列的出现次数。

# import module
import pandas as pd
 
 
# assign data
data = pd.DataFrame({'Section': ['A', 'A', 'A', 'B', 'B', 'B',
                                 'C', 'C', 'C'],
                     'Teacher': ['Kakeshi', 'Kakeshi', 'Iruka',
                                 'Kakeshi', 'Kakeshi', 'Kakeshi',
                                 'Iruka', 'Iruka', 'Guy']})
 
# display dataframe
print('Data:')
display(data)
 
 
print('Occurrence counts of combined columns:')
 
# count occurrences of combined columns
occur = data.groupby(['Section', 'Teacher']).size()
 
# display occurrences of combined columns
display(occur)

输出:

示例 3:

在这里，我们将CSV文件中的分类列的出现次数和合并出现次数分开计算。

# import module
import pandas as pd
 
 
# assign data
data = pd.read_csv('diamonds.csv')
 
# display dataframe
print('Data:')
display(data.sample(10))
 
print('Occurrence counts of particular column:')
 
# count occurrences a particular column
occur = data.groupby(['cut']).size()
 
# display occurrences of a particular column
display(occur)
 
print('Occurrence counts of combined columns:')
 
# count occurrences of combined columns
occur = data.groupby(['clarity', 'color', 'cut']).size()
 
# display occurrences of combined columns
display(occur)

输出:

方法2：使用pandas.groupyby().count()

使用这种方法的基本方法是在groupby()方法中指定列名作为参数，然后使用count()。下面是各种例子，描述了如何为不同的数据集计算一列中的出现次数。

示例 1:

在这个例子中，我们分别计算数据集中所有列的出现次数。

# import module
import pandas as pd
 
 
# assign data
data = pd.DataFrame({'Section': ['A', 'A', 'A', 'B', 'B', 'B',
                                 'C', 'C', 'C'],
                     'Teacher': ['Kakeshi', 'Kakeshi', 'Iruka',
                                 'Kakeshi', 'Kakeshi', 'Kakeshi',
                                 'Iruka', 'Iruka', 'Guy']})
 
# display dataframe
print('Data:')
display(data)
 
print('Occurrence counts of particular columns:')
 
# count occurrences a particular column
occur = data.groupby(['Section']).size()
 
# display occurrences of a particular column
display(occur)
 
# count occurrences a particular column
occur = data.groupby(['Teacher']).size()
 
# display occurrences of a particular column
display(occur)

输出:

示例 2:

在下面的程序中，我们从上一个程序中使用的同一数据集中计算所有列的出现次数。

# import module
import pandas as pd
 
 
# assign data
data = pd.DataFrame({'Section': ['A', 'A', 'A', 'B', 'B', 'B',
                                 'C', 'C', 'C'],
                     'Teacher': ['Kakeshi', 'Kakeshi', 'Iruka',
                                 'Kakeshi', 'Kakeshi', 'Kakeshi',
                                 'Iruka', 'Iruka', 'Guy']})
 
# display dataframe
print('Data:')
display(data)
 
print('Occurrence counts of combined columns:')
 
# count occurrences of combined columns
occur = data.groupby(['Section', 'Teacher']).size()
 
# display occurrences of combined columns
display(occur)

输出:

示例 3:

在这里，我们将CSV文件中的分类列的出现次数和合并出现次数分开计算。

# import module
import pandas as pd
 
 
# assign data
data = pd.read_csv('diamonds.csv')
 
# display dataframe
print('Data:')
display(data.sample(10))
 
 
print('Occurrence counts of particular column:')
 
# count occurrences a particular column
occur = data.groupby(['cut']).size()
 
# display occurrences of a particular column
display(occur)
 
 
print('Occurrence counts of combined columns:')
 
# count occurrences of combined columns
occur = data.groupby(['clarity', 'color', 'cut']).size()
 
# display occurrences of combined columns
display(occur)

输出: