Python - 如何按月对 Pandas DataFrame 进行分组？
我们将使用groupby对PandasDataFrame进行分组。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，我们将按月分组并计算每月注册价格的总和。
首先，假设以下是我们的三列PandasDataFrame-
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选择groupby()函数内的Date_of_Purchase列。频率freq设置为“M”以按月分组-
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
#dataframewithoneofthecolumnsasDate_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
#GroupertoselectDate_of_Purchasecolumnwithingroupbyfunction
print"\nGroup Dataframe by month...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='M')).sum()输出结果这将产生以下输出。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