将多个列表放入数据框 [英] Take multiple lists into dataframe

问题描述

如何获取多个列表并将它们作为不同的列放在python数据框中?我尝试了此解决方案，但遇到了一些麻烦.

How do I take multiple lists and put them as different columns in a python dataframe? I tried this solution but had some trouble.

尝试1:

• 具有三个列表，并将它们压缩在一起并使用该res = zip(lst1,lst2,lst3)
• 仅产生一列

尝试2:

percentile_list = pd.DataFrame({'lst1Tite' : [lst1],
                                'lst2Tite' : [lst2],
                                'lst3Tite' : [lst3] }, 
                                columns=['lst1Tite','lst1Tite', 'lst1Tite'])

• 产生一行3列(如上所述)，或者如果我转置则产生3行1列

我如何通过3列(三个列表)pandas数据框获得100行(每个独立列表的长度)?

How do I get a 100 row (length of each independent list) by 3 column (three lists) pandas dataframe?

推荐答案

我认为您快到了，请尝试删除lst周围的多余方括号(此外，您无需指定列名当您从像这样的字典创建数据框时:

I think you're almost there, try removing the extra square brackets around the lst's (Also you don't need to specify the column names when you're creating a dataframe from a dict like this):

import pandas as pd
lst1 = range(100)
lst2 = range(100)
lst3 = range(100)
percentile_list = pd.DataFrame(
    {'lst1Title': lst1,
     'lst2Title': lst2,
     'lst3Title': lst3
    })

percentile_list
    lst1Title  lst2Title  lst3Title
0          0         0         0
1          1         1         1
2          2         2         2
3          3         3         3
4          4         4         4
5          5         5         5
6          6         6         6
...

如果您需要性能更高的解决方案，则可以在第一次尝试中使用np.column_stack而不是zip，这在此示例中的速度提高了约2倍，但是在我看来，这是以可读性为代价:

If you need a more performant solution you can use np.column_stack rather than zip as in your first attempt, this has around a 2x speedup on the example here, however comes at bit of a cost of readability in my opinion:

import numpy as np
percentile_list = pd.DataFrame(np.column_stack([lst1, lst2, lst3]), 
                               columns=['lst1Title', 'lst2Title', 'lst3Title'])

这篇关于将多个列表放入数据框的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！