[python] pandas processing of data tables

Overview:

The processing of the data table is mainly to transpose the rows and columns of the data in the data table, convert the data table into a tree structure, table association, etc.

1. Row and column transposition

Call the T property of the DateFrame object directly:

import pandas as pd

dt=pd.read_excel('产品统计表.xlsx',sheet_name=0)
print(dt)
a=dt.T
print(a)

 2. Transform into a tree structure

Use the stack() function

import pandas as pd

dt=pd.read_excel('产品统计表.xlsx',sheet_name=0)
print(dt)
a=dt.stack()
print(a)

3. Splicing of data tables 

The splicing of data tables is to merge two or more data tables into one table. The main functions used are merge() and concat()

merge()：

import pandas as pd

dt1=pd.read_excel('产品表.xlsx',sheet_name=0)
dt2=pd.read_excel('产品表.xlsx',sheet_name=1)
print(dt1)
print(dt2)
a=pd.merge(dt1,dt2,how='outer',on='员工姓名')
#how默认为inner，如果设置为outer，为外连接，与SQL类似
#on为关联条件，如果不设置，则默认两张表中所有的相同字段
print(a)

concat() function:

import pandas as pd

dt1=pd.read_excel('产品表.xlsx',sheet_name=0)
dt2=pd.read_excel('产品表.xlsx',sheet_name=1)
#print(dt1)
#print(dt2)
a=pd.concat([dt1,dt2],ignore_index=True)
#ignore_index如果不设置，默认为False，区别为结果的首列的序列，True为重置行标签，False为保持两表原来的标签
print(a)

Results of the: