Python - 如何按天对 Pandas DataFrame 进行分组?
pythonserver side programmingprogramming更新于 2025/10/21 1:16:17
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。我们将按天分组，并计算注册价格与天间隔的总和，如下所示的汽车销售记录示例。
在 groupby() grouper 方法中将频率设置为天间隔，这意味着，如果频率为 7D，则意味着数据按每月 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 Pandas DataFrame，其中包含三列 −
import pandas as pd
# 数据框，其中一列为 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]
}
)
接下来，使用 Grouper 在 groupby 函数中选择 Date_of_Purchase 列。频率设置为 7D，即间隔 7 天，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
# dataframe 其中一列为 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 7 days...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='7D')).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 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
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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/
