按列值拆分 Pandas 数据框

有时为了更准确地分析 Dataframe，我们需要将其拆分为 2 个或更多部分。 Pandas 提供了根据列索引、行索引和列值等拆分 Dataframe 的功能。

让我们看看如何在Python中按列值拆分 Pandas 数据框？

现在，让我们创建一个数据框：

维利尔斯

Python3

# importing pandas library
import pandas as pd
 
# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]
 
# creating a pandas dataframe
df = pd.DataFrame(player_list,
                  columns = ['Name', 'Age',
                             'Weight', 'Salary'])
 
# show the dataframe
df

输出：

方法一：使用布尔掩码 方法。

此方法仅用于打印我们传递布尔值 True 的那部分数据帧。

示例 1：

Python3

# importing pandas library
import pandas as pd
 
# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]
 
# creating a pandas dataframe
df = pd.DataFrame(player_list,
                  columns = ['Name', 'Age',
                             'Weight', 'Salary'])
 
# splitting the dataframe into 2 parts
# on basis of 'Age' column values
# using Relational operator
df1 = df[df['Age'] >= 37]
 
# printing df1
df1

输出：

Python3

df2 = df[df['Age'] < 37]
 
# printing df2
df2

输出：

在上面的示例中，数据框“df”根据列“ Age ”的值分为 2 个部分“df1”和“df2”。

示例 2：

Python3

# importing pandas library
import pandas as pd
 
# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]
 
# creating a pandas dataframe
df = pd.DataFrame(player_list,
                  columns = ['Name', 'Age',
                             'Weight', 'Salary'])
 
# splitting the dataframe into 2 parts
# on basis of 'Weight' column values
mask = df['Weight'] >= 80
 
df1 = df[mask]
 
# invert the boolean values
df2 = df[~mask]
 
# printing df1
df1

输出：

Python3

# printing df2
df2

输出：

在上面的示例中，数据框“df”根据“权重”列的值分为 2 个部分“df1”和“df2”。

方法 2：使用Dataframe.groupby() 。

此方法用于根据某些标准将数据分组。

例子：

Python3

# importing pandas library
import pandas as pd
 
# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]
 
# creating a pandas dataframe
df = pd.DataFrame(player_list,
                  columns = ['Name', 'Age',
                             'Weight', 'Salary'])
 
# splitting the dataframe into 2 parts
# on basis of 'Salary' column values
# using dataframe.groupby() function
df1, df2 = [x for _, x in df.groupby(df['Salary'] < 4528000)]
 
# printing df1
df1

输出：

Python3

# printing df2
df2

输出：

在上面的示例中，数据框“df”根据“ Salary ”列的值分为 2 个部分“df1”和“df2”。