Multidimensional pandas dataframe data conversion numpy multidimensional array-CodePudding

How to realize the transfer from the left to the right of numpy array format:
Note: for example, using ABC only actual situation there may be unknown number of multiple tag

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Data can use the code form to? Digital soon, too

CodePudding user response:

reference 1st floor weixin_45903952 response:

data can use the code form to? Dozen figures also get soon

Data is generated, casually could transform

CodePudding user response:

reference 1st floor weixin_45903952 response:

data can use the code form to? Dozen figures also get soon

Data is generated, casually could transform

CodePudding user response:

reference 1st floor weixin_45903952 response:

data can use the code form to? Dozen figures also get soon

Data is generated, casually could transform

CodePudding user response:

 
The import pandas as pd 
The import numpy as np 
Import pandas. IO. Formats. Excel # set the format to use 
The import xlsxwriter # set the format to use, PIP install xlsxwriter 

Chengji=[[" a ", 95, 100, 99], [" a ", 98, 99, 100], [" a ", 95, 98, 88], 
[" b ", 98, 97, 87], [" b ", 90, 96, 85], [" b ", 94, 93, 91], [" a ", 99, 93, 91]] 

Data=https://bbs.csdn.net/topics/pd.DataFrame (chengji, columns=[' categories', "language", 'mathematics',' political ']) 
Print (data index, data. The columns) # vertical sequence, cross headings 
Print (" * * "50," raw data ") 

Print (data) 
Data1=data. Set_index (' categories') 
Print (data1) 
# data1=data. Groupby (' categories') # a single group, available ([...] [...]. ) more columns group 
# data2=data1 [' language '] agg ([np sum, np. Mean]) 
50 # print (" * * ", "Chinese, after using a classification NP sum, average") 
# print (data2) 
Lb=set (data) [r]. 'categories' tolist ()) 
Print (lb) 

Data2=pd. DataFrame () 
For LBX in lb: 
Nr=data1 [data1. Index==LBX] [r]. "Chinese" tolist () 
For num, NRX enumerate in (nr) : 
Data2. Loc [num, LBX]=NRX 
Print (data2)

CodePudding user response:

 import pandas as pd 

Data=https://bbs.csdn.net/topics/[[' A ', 329119], [' A ', 303068], [' A ', 282649], [' B ', 170843], [' B ', 147040], [' B ', 141867], [' B ', 119436], [' C ', 110657], [' C ', 106842], [' C ', 100016], [' C ', 106842], [' C ', 89662]] 

Df=pd DataFrame (data=https://bbs.csdn.net/topics/data, columns=[' Name 'and' data ']) 
B=df [' Name ']. Unique () 
D={} 
For I in range (0, len (b)) : 
D [b] [I]=df. Loc [df. Name==b [I], 'Data']. Reset_index (drop=True) 
Result=pd. DataFrame (d, the columns=b) 
Print (result)