Python – 对 Pandas DataFrame 的列值进行分组并计算总和
pythonserver side programmingprogramming更新于 2025/11/1 1:16:17
我们将以汽车销售记录为例，并按月分组以计算汽车每月的注册价格总和。为了求和，我们使用 sum() 方法。
首先，假设以下是我们的 Pandas DataFrame，包含三列 −
dataFrame = pd.DataFrame(
{
"Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
"Date_of_Purchase": [
pd.Timestamp("2021-06-10"),
pd.Timestamp("2021-07-11"),
pd.Timestamp("2021-06-25"),
pd.Timestamp("2021-06-29"),
pd.Timestamp("2021-03-20"),
pd.Timestamp("2021-01-22"),
pd.Timestamp("2021-01-06"),
pd.Timestamp("2021-01-04"),
pd.Timestamp("2021-05-09")
],
"Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
}
)
使用 Grouper 在 groupby() 函数中选择 Date_of_Purchase 列。频率 freq 设置为 ‘M’ 按月分组，总和使用 sum() 函数 − 计算。
print"\nGroup Dataframe by month...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='M')).sum()
示例
以下是代码 −
import pandas as pd
# dataframe 中有一列为 Date_of_Purchase
dataFrame = pd.DataFrame(
{
"Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
"Date_of_Purchase": [
pd.Timestamp("2021-06-10"),
pd.Timestamp("2021-07-11"),
pd.Timestamp("2021-06-25"),
pd.Timestamp("2021-06-29"),
pd.Timestamp("2021-03-20"),
pd.Timestamp("2021-01-22"),
pd.Timestamp("2021-01-06"),
pd.Timestamp("2021-01-04"),
pd.Timestamp("2021-05-09")
],
"Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
}
)
print"DataFrame...\n",dataFrame
# Grouper 在 groupby 函数中选择 Date_of_Purchase 列
# 按月计算总和
print"\nGroup Dataframe by month...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='M')).sum()
输出
这将产生以下输出 −
DataFrame...
Car Date_of_Purchase Reg_Price
0 Audi 2021-06-10 1000
1 Lexus 2021-07-11 1400
2 Tesla 2021-06-25 1100
3 Mercedes 2021-06-29 900
4 BMW 2021-03-20 1700
5 Toyota 2021-01-22 1800
6 Nissan 2021-01-06 1300
7 Bentley 2021-01-04 1150
8 Mustang 2021-05-09 1350
Group Dataframe by month...
Reg_Price
Date_of_Purchase
2021-01-31 4250.0
2021-02-28 NaN
2021-03-31 1700.0
2021-04-30 NaN
2021-05-31 1350.0
2021-06-30 3000.0
2021-07-31 1400.0
相关文章
有用资源
python 参考教程 - 该教程包含有关 python 的更多信息:https://www.cainiaomax.com/python/
