如何通过索引标签删除 Pandas DataFrame 中的行？

Pandas 为数据分析师提供了一种使用.drop()方法删除和过滤数据框的方法。可以使用此方法使用索引标签或列名删除行。

Syntax: 
DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)
Parameters: 
labels: String or list of strings referring row or column name. 
axis: int or string value, 0 ‘index’ for Rows and 1 ‘columns’ for Columns. 
index or columns: Single label or list. index or columns are an alternative to axis and cannot be used together. 
level: Used to specify level in case data frame is having multiple level index. 
inplace: Makes changes in original Data Frame if True. 
errors: Ignores error if any value from the list doesn’t exists and drops rest of the values when errors = ‘ignore’
Return type: Dataframe with dropped values 
 

现在，让我们创建一个示例数据框

Python3

# import pandas library
import pandas as pd
  
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya','Shivangi'],
    'Age' : [23, 21, 22,21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}
  
# creating a Dataframe object
df = pd.DataFrame(details,columns = ['Name','Age','University'],
                  index = ['a', 'b', 'c', 'd'])
  
df

输出：

示例 #1：按行索引标签删除 DataFrame 中的单个行

Python3

# import pandas library
import pandas as pd
 
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}
 
# creating a Dataframe object
df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'],
                  index = ['a', 'b', 'c', 'd'])
 
# return a new dataframe by dropping a
# row 'c' from dataframe
update_df = df.drop('c')
 
update_df

输出 ：

示例 #2：通过索引标签删除 DataFrame 中的多行

Python3

# import pandas library
import pandas as pd
 
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}
 
# creating a Dataframe object
df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'],
                  index = ['a', 'b', 'c', 'd'])
 
# return a new dataframe by dropping a row
# 'b' & 'c' from dataframe
update_df = df.drop(['b', 'c'])
 
update_df

输出 ：

示例 #3：在 DataFrame 中按索引位置删除多行

Python3

# import pandas library
import pandas as pd
 
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}
 
# creating a Dataframe object
df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'],
                  index = ['a', 'b', 'c', 'd'])
 
# return a new dataframe by dropping a row
# 'b' & 'c' from dataframe using their
# respective index position
update_df = df.drop([df.index[1], df.index[2]])
 
update_df

输出 ：

示例 #4：就地从 dataFrame 中删除行

Python3

# import pandas library
import pandas as pd
 
# dictionary with list object in values
details = {
    'Name' : ['Ankit', 'Aishwarya', 'Shaurya', 'Shivangi'],
    'Age' : [23, 21, 22, 21],
    'University' : ['BHU', 'JNU', 'DU', 'BHU'],
}
 
# creating a Dataframe object
df = pd.DataFrame(details, columns = ['Name', 'Age', 'University'],
                  index = ['a', 'b', 'c', 'd'])
 
# dropping a row 'c' & 'd' from actual dataframe
df.drop(['c', 'd'], inplace = True )
 
df

输出 ：