熊猫–按一列分组，获得平均值、最小值和最大值
原文:https://www.geesforgeks.org/pandas-group by-one-column-get-mean-min-max-values/
我们可以使用 Groupby 函数将数据帧拆分成组，并对其应用不同的操作。其中之一就是聚合。聚合，即计算创建的每个组的统计参数，例如平均值、最小值、最大值或总和。
让我们看一下如何按一列对数据帧进行分组，并获得它们的平均值、最小值和最大值。
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:
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:
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:
