数据透视表
使用 pandas 进行数据处理
Maggie Matsui
Senior Content Developer at DataCamp
从分组到透视表
dogs.groupby("color")["weight_kg"].mean()
color
Black 26
Brown 24
Gray 17
Tan 2
White 74
Name: weight_kg, dtype: int64
dogs.pivot_table(values="weight_kg",
index="color")
weight_kg
color
Black 26.5
Brown 24.0
Gray 17.0
Tan 2.0
White 74.0
不同统计量
dogs.pivot_table(values="weight_kg", index="color", aggfunc="median")
weight_kg
color
Black 26.5
Brown 24.0
Gray 17.0
Tan 2.0
White 74.0
多种统计量
dogs.pivot_table(values="weight_kg", index="color", aggfunc=["mean", "median"])
mean median
weight_kg weight_kg
color
Black 26.5 26.5
Brown 24.0 24.0
Gray 17.0 17.0
Tan 2.0 2.0
White 74.0 74.0
按两个变量透视
dogs.groupby(["color", "breed"])["weight_kg"].mean()
dogs.pivot_table(values="weight_kg", index="color", columns="breed")
breed Chihuahua Chow Chow Labrador Poodle Schnauzer St. Bernard
color
Black NaN NaN 29.0 24.0 NaN NaN
Brown NaN 24.0 24.0 NaN NaN NaN
Gray NaN NaN NaN NaN 17.0 NaN
Tan 2.0 NaN NaN NaN NaN NaN
White NaN NaN NaN NaN NaN 74.0
在透视表中填充缺失值
dogs.pivot_table(values="weight_kg", index="color", columns="breed", fill_value=0)
breed Chihuahua Chow Chow Labrador Poodle Schnauzer St. Bernard
color
Black 0 0 29 24 0 0
Brown 0 24 24 0 0 0
Gray 0 0 0 0 17 0
Tan 2 0 0 0 0 0
White 0 0 0 0 0 74
透视表求和
dogs.pivot_table(values="weight_kg", index="color", columns="breed",
fill_value=0, margins=True)
breed Chihuahua Chow Chow Labrador Poodle Schnauzer St. Bernard All
color
Black 0 0 29 24 0 0 26.500000
Brown 0 24 24 0 0 0 24.000000
Gray 0 0 0 0 17 0 17.000000
Tan 2 0 0 0 0 0 2.000000
White 0 0 0 0 0 74 74.000000
All 2 24 26 24 17 74 27.714286
让我们来练习！
使用 pandas 进行数据处理
