如何在熊猫身上执行 SUMIF 功能？
sumif()函数用于对数据框中的一组项目进行求和运算，可以应用于单列和多列，我们也可以将该函数与 groupby 函数配合使用。
方法 1:用 groupby()对所有列进行 SUMIF
此函数用于显示所有列相对于分组列的总和
语法:data frame . group by(' group _ column ')。总和()
在哪里
- 数据帧是输入数据帧
- group_column 是数据框中要分组的列
- sum()函数用于执行求和操作
创建包含 4 列的学生数据框
Python 3
# 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对所有列求和
Python 3
# 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 2:一列上的 SUMIF 函数
这里我们对一个特定的列执行 sumif 操作，将它与一个列分组
语法:data frame . group by(' group _ column ')[' column _ name]。总和()
在哪里
- 数据帧是输入数据帧
- group_column 是数据框中要分组的列
- column_name 是获取此列相对于分组列的总和
- sum()函数用于执行求和操作
Python 3
# 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:多列上的 SUMIF 操作
这里我们将对多列使用 sumif 操作。
语法:data frame . group by(' group _ column ')[[' column _ name ']]。总和()
哪里，
- 数据帧是输入数据帧
- group_column 是数据框中要分组的列
- column _ names 用于获取这些列相对于分组列的总和
- sum()函数用于执行求和操作
Python 3
# 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:
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