Take multiple lists into dataframe

Question

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

Attempt 1:

Have three lists, and zip them together and use that res = zip(lst1,lst2,lst3)
Yields just one column

Attempt 2:

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

yields either one row by 3 columns (the way above) or if I transpose it is 3 rows and 1 column

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

Asked By: jfalkson

||

Source

Answer 1

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
...

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'])

Answered By: maxymoo

Answer 2

Just adding that using the first approach it can be done as –

pd.DataFrame(list(map(list, zip(lst1,lst2,lst3))))

Answered By: Aditya Guru

Answer 3

Adding to Aditya Guru‘s answer here. There is no need of using map. You can do it simply by:

pd.DataFrame(list(zip(lst1, lst2, lst3)))

This will set the column’s names as 0,1,2. To set your own column names, you can pass the keyword argument columns to the method above.

pd.DataFrame(list(zip(lst1, lst2, lst3)),
              columns=['lst1_title','lst2_title', 'lst3_title'])

Answered By: Abhinav Gupta

Answer 4

Adding one more scalable solution.

lists = [lst1, lst2, lst3, lst4]
df = pd.concat([pd.Series(x) for x in lists], axis=1)

Answered By: oopsi

Answer 5

Adding to above answers, we can create on the fly

df= pd.DataFrame()
list1 = list(range(10))
list2 = list(range(10,20))
df['list1'] = list1
df['list2'] = list2
print(df)

hope it helps !

Answered By: Wickkiey

Answer 6

@oopsi used pd.concat() but didn’t include the column names. You could do the following, which, unlike the first solution in the accepted answer, gives you control over the column order (avoids dicts, which are unordered):

import pandas as pd
lst1 = range(100)
lst2 = range(100)
lst3 = range(100)

s1=pd.Series(lst1,name='lst1Title')
s2=pd.Series(lst2,name='lst2Title')
s3=pd.Series(lst3 ,name='lst3Title')
percentile_list = pd.concat([s1,s2,s3], axis=1)

percentile_list
Out[2]: 
    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
7           7          7          7
8           8          8          8
...

Answered By: dabru

Answer 7

you can simply use this following code

train_data['labels']= train_data[["LABEL1","LABEL1","LABEL2","LABEL3","LABEL4","LABEL5","LABEL6","LABEL7"]].values.tolist()
train_df = pd.DataFrame(train_data, columns=['text','labels'])

Answered By: Shaina Raza

Answer 8

There are several ways to create a dataframe from multiple lists.

list1=[1,2,3,4]
list2=[5,6,7,8]
list3=[9,10,11,12]

pd.DataFrame({'list1':list1, 'list2':list2, 'list3'=list3})
pd.DataFrame(data=zip(list1,list2,list3),columns=['list1','list2','list3'])

Answered By: Reetesh Kumar

Answer 9

I just did it like this (python 3.9):

import pandas as pd
my_dict=dict(x=x, y=y, z=z) # Set column ordering here
my_df=pd.DataFrame.from_dict(my_dict)

This seems to be reasonably straightforward (albeit in 2022) unless I am missing something obvious…

In python 2 one could’ve used a collections.OrderedDict().

Answered By: jtlz2

Take multiple lists into dataframe

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