如何在熊猫数据框中选择多列
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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