Python - 如何按月对 Pandas DataFrame 进行分组?
pythonserver side programmingprogramming更新于 2025/10/20 22:04:17
我们将使用 groupby 对 Pandas DataFrame 进行分组。使用 grouper 函数选择要使用的列。我们将按月分组并计算每月注册价格总和，如下所示的汽车销售记录示例。
首先，假设以下是包含三列的 Pandas DataFrame −
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 −
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
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。计算每个月的注册价格 −
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
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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/
