按列值分割熊猫数据框
原文:https://www.geesforgeks.org/split-pandas-data frame-by-column-value/
有时，为了更准确地分析数据帧，我们需要将其分成 2 个或更多部分。熊猫提供了根据列索引、行索引和列值等拆分数据帧的功能。
让我们看看如何在 Python 中按列值拆分熊猫数据帧？
现在，让我们创建一个数据帧:
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# 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 Villers', 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:采用 布尔掩蔽 的方法。
此方法仅用于打印数据帧中我们传递布尔值“真”的部分。
例 1:
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# 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 Villers', 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:
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df2 = df[df['Age'] < 37]
# printing df2
df2
输出:
在上面的例子中，基于列“ Age 的值，数据帧“df”被分成 2 个部分“df1”和“df2”。
例 2:
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# 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 Villers', 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:
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# printing df2
df2
输出:
在上面的例子中，基于列“权重的值，数据帧“df”被分成 2 个部分“df1”和“df2”。
方法二:使用data frame.group by()。
此方法用于根据某些标准将数据分成组。
示例:
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# 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 Villers', 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:
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# printing df2
df2
输出:
在上面的例子中，数据帧“df”根据列“工资的值被分成两部分“df1”和“df2”。
