Pandas GroupBy一列并获取平均值、最小值和最大值 |
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Pandas GroupBy一列并获取平均值、最小值和最大值
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Pandas GroupBy一列并获取平均值、最小值和最大值
我们可以使用Groupby函数将数据框架分成若干组，并对其进行不同的操作。其中之一就是聚合。聚合，即计算每个组的统计参数，例如，创建平均数、最小数、最大数或总和。 让我们来看看我们如何通过一个列来分组一个数据框架并获得它们的平均值、最小值和最大值。 示例 1: import pandas as pd # creating a dataframe df = pd.DataFrame([('Bike', 'Kawasaki', 186), ('Bike', 'Ducati Panigale', 202), ('Car', 'Bugatti Chiron', 304), ('Car', 'Jaguar XJ220', 210), ('Bike', 'Lightning LS-218', 218), ('Car', 'Hennessey Venom GT', 270), ('Bike', 'BMW S1000RR', 188)], columns =('Type', 'Name', 'top_speed(mph)')) df输出 :
寻找平均值、最小值和最大值。 # using groupby function with aggregation # to get mean, min and max values result = df.groupby('Type').agg({'top_speed(mph)': ['mean', 'min', 'max']}) print("Mean, min, and max values of Top Speed grouped by Vehicle Type") print(result)输出 :
示例 2: import pandas as pd # creating a dataframe sales_data = pd.DataFrame({ 'customer_id':[3005, 3001, 3002, 3009, 3005, 3007, 3002, 3004, 3009, 3008, 3003, 3002], 'salesman_id': [102, 105, 101, 103, 102, 101, 101, 106, 103, 102, 107, 101], 'purchase_amt':[1500, 2700, 1525, 1100, 948, 2400, 5700, 2000, 1280, 2500, 750, 5050]}) sales_data输出 :
寻找平均值、最小值和最大值。 # using groupby function with aggregation # to get mean, min and max values result = sales_data.groupby('salesman_id').agg({'purchase_amt': ['mean', 'min', 'max']}) print("Mean, min, and max values of Purchase Amount grouped by Salesman id") print(result)输出 :
示例 3: import pandas as pd # creating a dataframe df = pd.DataFrame({"Team": ["Radisson", "Radisson", "Gladiators", "Blues", "Gladiators", "Blues", "Gladiators", "Gladiators", "Blues", "Blues", "Radisson", "Radisson"], "Position": ["Player", "Extras", "Player", "Extras", "Extras", "Player", "Player", "Player", "Extras", "Player", "Player", "Extras"], "Age": [22, 24, 21, 29, 32, 20, 21, 23, 30, 26, 20, 31]}) df输出 :
寻找平均值、最小值和最大值。 # using groupby function with aggregation # to get mean, min and max values result = df.groupby('Team').agg({'Age': ['mean', 'min', 'max']}) print("Mean, min, and max values of Age grouped by Team") print(result)输出 :
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