Python - 如何按年份对 Pandas DataFrame 进行分组？
我们将使用groupby().使用grouper功能选择要使用的列。对于下面显示的汽车销售记录示例，我们将按年份分组并计算注册价格与年份间隔的总和。
首先，假设以下是我们的三列PandasDataFrame-
#其中一列为Date_of_Purchase的数据框
dataFrame = pd.DataFrame(
{
"Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
"Date_of_Purchase": [pd.Timestamp("2021-06-10"),
pd.Timestamp("2019-07-11"),
pd.Timestamp("2016-06-25"),
pd.Timestamp("2021-06-29"),
pd.Timestamp("2020-03-20"),
pd.Timestamp("2019-01-22"),
pd.Timestamp("2011-01-06"),
pd.Timestamp("2013-01-04"),
pd.Timestamp("2014-05-09")
],
"Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
}
)接下来，使用Grouper在groupby函数中选择Date_of_Purchase列。频率设置为3Y，即以3年为一组进行分组。
示例
以下是代码-
import pandas as pd
#其中一列为Date_of_Purchase的数据框
dataFrame = pd.DataFrame(
{
"Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
"Date_of_Purchase": [pd.Timestamp("2021-06-10"),
pd.Timestamp("2019-07-11"),
pd.Timestamp("2016-06-25"),
pd.Timestamp("2021-06-29"),
pd.Timestamp("2020-03-20"),
pd.Timestamp("2019-01-22"),
pd.Timestamp("2011-01-06"),
pd.Timestamp("2013-01-04"),
pd.Timestamp("2014-05-09")
],
"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 3 years...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='3Y')).sum())输出结果这将产生以下输出-
DataFrame...
Car Date_of_Purchase Reg_Price
0 Audi 2021-06-10 1000
1 Lexus 2019-07-11 1400
2 Tesla 2016-06-25 1100
3 Mercedes 2021-06-29 900
4 BMW 2020-03-20 1700
5 Toyota 2019-01-22 1800
6 Nissan 2011-01-06 1300
7 Bentley 2013-01-04 1150
8 Mustang 2014-05-09 1350
Group Dataframe by 3 years...
Reg_Price
Date_of_Purchase
2011-12-31 1300
2014-12-31 2500
2017-12-31 1100
2020-12-31 4900
2023-12-31 1900