使用不同长度的给定列表使 dataframe 具有不同的列长度

Question

I have two lists of different length and i want to form a DataFrame having these lists as columns of a DataFrame. Lets say my lists are L1 =["a","b"] L2 = ["g","h","o","y"] L3 = ["k","u","e"]

i want a dataframe in which looks like

 L1 L2 L3
 a  g  k
 b  h  u
    o  e
    y   

The question is just not limited for only three columns.It is in the range of hundreds

Answer 1

Following results, it might be looking for, the idea is to convert the list into Data frame, you could also create a function that take as input, list arguments and not type every list into Data-Frame by hand.

Example 1:

import pandas as pd 
import numpy as np

## your list
L1 =["a","b"] 
L2 = ["g","h","o","y"] 
L3 = ["k","u","e"]

## Convert List into DataFrame 
L1 = pd.DataFrame(L1)
L2 = pd.DataFrame(L2)
L3 = pd.DataFrame(L3)

## Concanate the created DataFrames and Fill 'NaN' with or empty spaces 
db =pd.concat([L1,L2,L3], ignore_index=True, axis=1).replace(np.nan, '')

## Results
print(db)

Example 2 (Function passing list arguments):

import pandas as pd 
import numpy as np

## your list
L1 =["a","b"] 
L2 = ["g","h","o","y"] 
L3 = ["k","u","e"]

## function passing arguments
def wrapper(*args):
    return pd.concat([pd.DataFrame(i) for i in args], ignore_index=True, axis=1).replace(np.nan, '')


## Results
print(wrapper(L1,L2,L3))

Answer 2

You can use zip_longest iterator from itertools . It will pad the shorter sequences with fillvalue which is None by default. You can specify something else like an empty string in the example below.

from itertools import zip_longest
df = pd.DataFrame(zip_longest(L1,L2,L3, fillvalue=''), 
                  columns=['L1','L2','L3'])