极客教程如何在Pandas中执行SUMIF函数
sumif()函数用于对DataFrame中的一组项目进行求和操作，它可以应用于单列和多列，我们也可以将此函数与groupby函数一起使用。
方法1：用groupby()对所有列进行SUMIF处理
此函数用于显示所有列与分组列的总和
语法: dataframe.groupby(‘group_column’).sum()
其中，
- dataframe是输入数据帧
- group_column是DataFrame中要分组的列。
- sum()函数是为了执行求和操作
创建有4列的学生DataFrame
# import pandas module
import pandas as pd
# create dataframe with 4 columns
data = pd.DataFrame({
"name": ['sravan', 'jyothika', 'harsha',
'ramya', 'sravan', 'jyothika',
'harsha', 'ramya', 'sravan', 'jyothika',
'harsha', 'ramya'],
"subjects": ['java', 'java', 'java', 'python',
'python', 'python', 'html/php',
'html/php', 'html/php', 'php/js',
'php/js', 'php/js'],
"internal marks": [98, 79, 89, 97, 82, 98, 90,
87, 78, 89, 93, 94],
"external marks": [88, 71, 89, 97, 82, 98, 80,
87, 71, 89, 92, 64],
})
# display dataframe
print(data)
输出 :
通过对特定列进行分组来执行所有列的总和
# import pandas module
import pandas as pd
# create dataframe with 4 columns
data = pd.DataFrame({
"name": ['sravan', 'jyothika', 'harsha', 'ramya',
'sravan', 'jyothika', 'harsha', 'ramya',
'sravan', 'jyothika', 'harsha', 'ramya'],
"subjects": ['java', 'java', 'java', 'python',
'python', 'python', 'html/php',
'html/php', 'html/php', 'php/js',
'php/js', 'php/js'],
"internal marks": [98, 79, 89, 97, 82, 98, 90,
87, 78, 89, 93, 94],
"external marks": [88, 71, 89, 97, 82, 98, 80,
87, 71, 89, 92, 64],
})
# find sum of all columns group by name
print(data.groupby('name').sum())
# find sum of all columns group by subjects
print(data.groupby('subjects').sum())
输出:
方法2：单列的SUMIF函数
在这里，我们通过对某一列进行分组，对该列进行sumif操作
语法： dataframe.groupby('group_column')['column_name].sum()
其中
- dataframe是输入数据框架
- group_column是数据框架中要分组的列
- column_name是获得该列与分组列的总和。
- sum()函数用于执行求和操作。
# import pandas module
import pandas as pd
# create dataframe with 4 columns
data = pd.DataFrame({
"name": ['sravan', 'jyothika', 'harsha', 'ramya',
'sravan', 'jyothika', 'harsha', 'ramya',
'sravan', 'jyothika', 'harsha', 'ramya'],
"subjects": ['java', 'java', 'java', 'python',
'python', 'python', 'html/php',
'html/php', 'html/php', 'php/js',
'php/js', 'php/js'],
"internal marks": [98, 79, 89, 97, 82, 98, 90,
87, 78, 89, 93, 94],
"external marks": [88, 71, 89, 97, 82, 98, 80,
87, 71, 89, 92, 64],
})
# find sum of columns group by
# name with internal marks column
print(data.groupby('name')['internal marks'].sum())
print("---------------")
# find sum of columns group by
# name with external marks column
print(data.groupby('name')['external marks'].sum())
print("---------------")
# find sum of columns group by
# subjects with internal marks column
print(data.groupby('subjects')['internal marks'].sum())
print("---------------")
# find sum of columns group by
# subjects with external marks column
print(data.groupby('subjects')['external marks'].sum())
输出:
方法3：对多列进行SUMIF操作
这里我们将在多列上使用sumif操作。
语法： dataframe.groupby('group_column')[['column_names']].sum()
其中,
- dataframe是输入的数据框架
- group_column是数据框架中要分组的列
- column_names是获得这些列与被分组的列的总和
- sum()函数用于执行求和操作。
# import pandas module
import pandas as pd
# create dataframe with 4 columns
data = pd.DataFrame({
"name": ['sravan', 'jyothika', 'harsha', 'ramya',
'sravan', 'jyothika', 'harsha', 'ramya',
'sravan', 'jyothika', 'harsha', 'ramya'],
"subjects": ['java', 'java', 'java', 'python',
'python', 'python', 'html/php',
'html/php', 'html/php', 'php/js',
'php/js', 'php/js'],
"internal marks": [98, 79, 89, 97, 82, 98, 90,
87, 78, 89, 93, 94],
"external marks": [88, 71, 89, 97, 82, 98, 80,
87, 71, 89, 92, 64],
})
# find sum of columns group by name with
# external marks and internal marks column
print(data.groupby('name')[['external marks',
'internal marks']].sum())
print("---------------")
# find sum of columns group by subjects
# with external marks and internal marks column
print(data.groupby('subjects')[['external marks',
'internal marks']].sum())
输出:
