Python - 如何按天对 Pandas DataFrame 进行分组？
我们将使用.pandasDataFrame对PandasDataFrame进行分组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，我们将按天分组并计算按天间隔的注册价格总和。
在groupby()grouper方法中将频率设置为天数间隔，这意味着，如果频率为7D，则表示数据按每月7天的间隔分组，直到日期列中给出的最后一个日期。
首先，假设以下是我们的三列PandasDataFrame-
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("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]
}
)接下来，使用Grouper在groupby函数中选择Date_of_Purchase列。频率设置为7D，即7天的间隔分组到列中提到的最后一个日期-
print"\nGroup Dataframe by 7 days...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='7D')).sum()
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("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 7 days...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='7D')).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 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 7 days...
Reg_Price
Date_of_Purchase
2021-01-04 2450.0
2021-01-11 NaN
2021-01-18 1800.0
2021-01-25 NaN
2021-02-01 NaN
2021-02-08 NaN
2021-02-15 NaN
2021-02-22 NaN
2021-03-01 NaN
2021-03-08 NaN
2021-03-15 1700.0
2021-03-22 NaN
2021-03-29 NaN
2021-04-05 NaN
2021-04-12 NaN
2021-04-19 NaN
2021-04-26 NaN
2021-05-03 1350.0
2021-05-10 NaN
2021-05-17 NaN
2021-05-24 NaN
2021-05-31 NaN
2021-06-07 1000.0
2021-06-14 NaN
2021-06-21 1100.0
2021-06-28 900.0
2021-07-05 1400.0