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如何在熊猫数据框中选择多列

原文:https://www.geesforgeks.org/如何选择多列熊猫-数据框/

Python 是进行数据分析的优秀语言，主要是因为以数据为中心的 Python 包的奇妙生态系统。 【熊猫】 就是其中一个包，让导入和分析数据变得容易多了。

让我们讨论在熊猫数据框中选择多列的所有不同方法。

方法#1: 基本方法

给定一个字典，其中包含作为关键字的雇员实体和作为值的这些实体的列表。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# select two columns
df[['Name', 'Qualification']]

输出:

选择第二至第四列。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# select all rows 
# and second to fourth column
df[df.columns[1:4]]

输出:

方法 2: 使用[loc[]](https://www.geeksforgeeks.org/python-pandas-extracting-rows-using-loc/)

示例 1: 选择两列

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# select three rows and two columns
df.loc[1:3, ['Name', 'Qualification']]

输出:

示例 2: 选择一列到另一列。在我们的例子中，我们选择列名“名称”到“地址”。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# select two rows and 
# column "name" to "Address"
# Means total three columns
df.loc[0:1, 'Name':'Address']

输出:

示例 3: 首先按标签格式过滤行和选择列，然后选择所有列。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']
       }
# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# .loc DataFrame method
# filtering rows and selecting columns by label
# format
# df.loc[rows, columns]
# row 1, all columns
df.loc[0, :]

输出:

方法 3: 使用[iloc[]](https://www.geeksforgeeks.org/python-extracting-rows-using-pandas-iloc/)

例 1: 选择前两列。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# Remember that Python does not
# slice inclusive of the ending index.
# select all rows 
# select first two column
df.iloc[:, 0:2] 

输出:

示例 2: 选择所有或部分列，使用 iloc 逐个选择。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# iloc[row slicing, column slicing]
df.iloc [0:2, 1:3]

输出:

方法 4: 使用.ix

使用.ix逐个选择全部或部分列。

# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame 
df = pd.DataFrame(data)

# select all rows and 0 to 2 columns 
print(df.ix[:, 0:2])

输出:

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