如何用熊猫在 Python 中创建透视表？
数据透视表是一个统计表，它总结了一个像大数据集一样的大表。这是数据处理的一部分。数据透视表中的汇总可能包括平均值、中位数、总和或其他统计术语。透视表最初与 MS Excel 相关联，但是我们可以使用 Pandas 使用 dataframe.pivot() 方法在 Python 中创建一个透视表。
语法: dataframe.pivot(self，索引=无，列=无，值=无，aggfunc)
参数– 索引:列用于制作新帧的索引。 列:列为新框架的列。 值:用于填充新帧值的列。 aggfunc: 函数，函数列表，dict，默认 numpy.mean
示例 1: 让我们首先创建一个包含水果销售的数据框。
# importing pandas
import pandas as pd
# creating dataframe
df = pd.DataFrame({'Product' : ['Carrots', 'Broccoli', 'Banana', 'Banana',
'Beans', 'Orange', 'Broccoli', 'Banana'],
'Category' : ['Vegetable', 'Vegetable', 'Fruit', 'Fruit',
'Vegetable', 'Fruit', 'Vegetable', 'Fruit'],
'Quantity' : [8, 5, 3, 4, 5, 9, 11, 8],
'Amount' : [270, 239, 617, 384, 626, 610, 62, 90]})
df
输出:
获取每款产品的总销量
# creating pivot table of total sales
# product-wise aggfunc = 'sum' will
# allow you to obtain the sum of sales
# each product
pivot = df.pivot_table(index =['Product'],
values =['Amount'],
aggfunc ='sum')
print(pivot)
输出:
获取各品类总销量
# creating pivot table of total
# sales category-wise aggfunc = 'sum'
# will allow you to obtain the sum of
# sales each product
pivot = df.pivot_table(index =['Category'],
values =['Amount'],
aggfunc ='sum')
print(pivot)
输出:
按类别和产品获取总销售额
# creating pivot table of sales
# by product and category both
# aggfunc = 'sum' will allow you
# to obtain the sum of sales each
# product
pivot = df.pivot_table(index =['Product', 'Category'],
values =['Amount'], aggfunc ='sum')
print (pivot)
输出–
按类别获取平均、中间、最低销售额
# creating pivot table of Mean, Median,
# Minimum sale by category aggfunc = {'median',
# 'mean', 'min'} will get median, mean and
# minimum of sales respectively
pivot = df.pivot_table(index =['Category'], values =['Amount'],
aggfunc ={'median', 'mean', 'min'})
print (pivot)
输出–
获取产品的平均、中值、最低销售额
# creating pivot table of Mean, Median,
# Minimum sale by product aggfunc = {'median',
# 'mean', 'min'} will get median, mean and
# minimum of sales respectively
pivot = df.pivot_table(index =['Product'], values =['Amount'],
aggfunc ={'median', 'mean', 'min'})
print (pivot)
输出:
