pandas中数据透视表pivot操作

1.导入pandas，定义DataFrame

import pandas as pd
example = pd.DataFrame({'Month': ["January", "January", "January", "January", 
                                  "February", "February", "February", "February", 
                                  "March", "March", "March", "March"],
                   'Category': ["Transportation", "Grocery", "Household", "Entertainment",
                                "Transportation", "Grocery", "Household", "Entertainment",
                                "Transportation", "Grocery", "Household", "Entertainment"],
                   'Amount': [74., 235., 175., 100., 115., 240., 225., 125., 90., 260., 200., 120.]})
example

2.使用pivot进行数据统计

example_pivot = example.pivot(index = 'Category',columns= 'Month',values = 'Amount')
example_pivot

example_pivot.sum(axis = 1)
example_pivot.sum(axis = 0)

3.读取csv

df = pd.read_csv('./data/titanic.csv')
df.head()

4.指定特征值类型（行表头、列表头、特征数据）绘制图表，图表中的数据默认为平均值

df.pivot_table(index = 'Sex',columns='Pclass',values='Fare')

5.求最大值

6.统计数量

df.pivot_table(index = 'Sex',columns='Pclass',values='Fare',aggfunc='count')

pd.crosstab(index = df['Sex'],columns = df['Pclass'])

7.求平均值

df.pivot_table(index = 'Pclass',columns='Sex',values='Survived',aggfunc='mean')

df['Underaged'] = df['Age'] <= 18
df.pivot_table(index = 'Underaged',columns='Sex',values='Survived',aggfunc='mean')