Pandas入门之十六:级联
作者:互联网
已信任 Jupyter 服务器: 本地 Python 3: Not Started [1] import pandas as pd import numpy as np [2] one = pd.DataFrame({ 'name':['alex','xm','xh','lc','ll'], 'subject':['python','java','go','js','html'], 'score':[88,79,68,96,66] }) one name subject score 0 alex python 88 1 xm java 79 2 xh go 68 3 lc js 96 4 ll html 66 [4] two = pd.DataFrame({ 'name':['xc','xm','xh','lc','ll'], 'subject':['php','java','go','js','html'], 'score':[89,79,68,96,66] }) two name subject score 0 xc php 89 1 xm java 79 2 xh go 68 3 lc js 96 4 ll html 66 [6] # 按行 pd.concat([one,two],ignore_index=True) name subject score 0 xc php 89 1 xm java 79 2 xh go 68 3 lc js 96 4 ll html 66 5 xc php 89 6 xm java 79 7 xh go 68 8 lc js 96 9 ll html 66 [7] # 按列 pd.concat([one,two],ignore_index=True,axis=1) 0 1 2 3 4 5 0 xc php 89 xc php 89 1 xm java 79 xm java 79 2 xh go 68 xh go 68 3 lc js 96 lc js 96 4 ll html 66 ll html 66 [-]
标签:xh,级联,java,入门,xm,go,66,68,Pandas 来源: https://www.cnblogs.com/vvzhang/p/15024286.html