【问题标题】:Extract one column into multiple Column csv file将一列提取到多列csv文件中
【发布时间】:2020-12-29 11:35:03
【问题描述】:

我的 credit credit_scoring.csv 是这样的,我怎样才能以有条理的方式制作 14 列,并且每列都有对应的值

Seniority;Home;Time;Age;Marital;Records;Job;Expenses;Income;Assets;Debt;Amount;Price;Status
    0                                         9.0;1.0;60.0;30.0;0.0;1.0;1.0;73.0;129.0;0.0;0...
    1                                         17.0;1.0;60.0;58.0;1.0;1.0;0.0;48.0;131.0;0.0;...
    2                                         10.0;0.0;36.0;46.0;0.0;2.0;1.0;90.0;200.0;3000...
    3                                         0.0;1.0;60.0;24.0;1.0;1.0;0.0;63.0;182.0;2500....
    4                                         0.0;1.0;36.0;26.0;1.0;1.0;0.0;46.0;107.0;0.0;0...
    .                                         .................................................
    .                                         .................................................
    .                                         .................................................
    .                                         .................................................

【问题讨论】:

    标签: python-3.x pandas dataframe numpy data-analysis


    【解决方案1】:

    您可以简单地将 read_csv() 与 sep=';' 一起使用

    您的示例数据不是很好,但我尽力做到了。

    我将它保存为a.csv,这是代码:

    In [1]: import pandas as pd
    
    In [2]: pd.read_csv('a.csv', sep=';')
    Out[2]:
       Seniority  Home  Time   Age  Marital  Records  Job  Expenses  Income  Assets  Debt  Amount  Price  Status
    0        9.0   1.0  60.0  30.0      0.0      1.0  1.0      73.0   129.0     0.0   0.0     NaN    NaN     NaN
    1       17.0   1.0  60.0  58.0      1.0      1.0  0.0      48.0   131.0     0.0   NaN     NaN    NaN     NaN
    2       10.0   0.0  36.0  46.0      0.0      2.0  1.0      90.0   200.0  3000.0   NaN     NaN    NaN     NaN
    3        0.0   1.0  60.0  24.0      1.0      1.0  0.0      63.0   182.0  2500.0   NaN     NaN    NaN     NaN
    4        0.0   1.0  36.0  26.0      1.0      1.0  0.0      46.0   107.0     0.0   0.0     NaN    NaN     NaN
    

    【讨论】:

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