Python - 如何按分钟对 Pandas DataFrame 进行分组?
pythonserver side programmingprogramming更新于 2025/11/5 14:04:17
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。我们将按分钟分组，并计算注册价格的总和，间隔为分钟，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。我们已将 Date_of_Purchase 设置为时间戳，包括日期和时间−
dataFrame = pd.DataFrame(
{
"Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
"Date_of_Purchase": [
pd.Timestamp("2021-07-28 00:10:00"),
pd.Timestamp("2021-07-28 00:12:00"),
pd.Timestamp("2021-07-28 00:15:00"),
pd.Timestamp("2021-07-28 00:16:00"),
pd.Timestamp("2021-07-28 00:17:00"),
pd.Timestamp("2021-07-28 00:20:00"),
pd.Timestamp("2021-07-28 00:35:00"),
pd.Timestamp("2021-07-28 00:42:00"),
pd.Timestamp("2021-07-28 00:57:00"),
],
"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 7min，即间隔 7 分钟分组 −
print"\nGroup Dataframe by 7 minutes...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='7min')).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("2021-07-28 00:10:00"),
pd.Timestamp("2021-07-28 00:12:00"),
pd.Timestamp("2021-07-28 00:15:00"),
pd.Timestamp("2021-07-28 00:16:00"),
pd.Timestamp("2021-07-28 00:17:00"),
pd.Timestamp("2021-07-28 00:20:00"),
pd.Timestamp("2021-07-28 00:35:00"),
pd.Timestamp("2021-07-28 00:42:00"),
pd.Timestamp("2021-07-28 00:57:00"),
],
"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 minutes...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='7min')).sum()
输出
这将产生以下输出 −
DataFrame... Car Date_of_Purchase Reg_Price 0 Audi 2021-07-28 00:10:00 1000 1 Lexus 2021-07-28 00:12:00 1400 2 Tesla 2021-07-28 00:15:00 1100 3 Mercedes 2021-07-28 00:16:00 900 4 BMW 2021-07-28 00:17:00 1700 5 Toyota 2021-07-28 00:20:00 1800 6 Nissan 2021-07-28 00:35:00 1300 7 Bentley 2021-07-28 00:42:00 1150 8 Mustang 2021-07-28 00:57:00 1350 Group Dataframe by 7 minutes... Reg_Price Date_of_Purchase 2021-07-28 00:07:00 2400.0 2021-07-28 00:14:00 5500.0 2021-07-28 00:21:00 NaN 2021-07-28 00:28:00 NaN 2021-07-28 00:35:00 1300.0 2021-07-28 00:42:00 1150.0 2021-07-28 00:49:00 NaN 2021-07-28 00:56:00 1350.0
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