How to create columns in Pandas using a list-CodePudding

I am trying to create a table in Pandas and I want my columns to be titled by the numbers between 0 and 56, but typing them by myself is very inefficient.

Here is my code:

 import pandas as pd 
  
 x = [i for i in range(57)] 

 df = pd.DataFrame(data=x,columns=["0", "1", "2", ... "56"])

I'd rather use something more efficient, so I tried:

import pandas as pd 

x = [i for i in range(57)]
y = [i for i in range(57)]

df = pd.DataFrame(data=x,columns=[y])

This will give me an ValueError. I've also tried much, much more, but the best I could get was the whole list as one column. I looked through the official Pandas website and so on but didnt found a solution to my problem without using Excel. So do you have an idea how I can reach my goal only using NumPy, Pandas and Python?

CodePudding user response：

You need x to be 2D and y 1D (notice the square brackets):

import pandas as pd 
x = [i for i in range(57)]
y = [i for i in range(57)]
df = pd.DataFrame(data=[x], columns=y)

Output:

    0   1   2   3   4   5   6   7   8   9   ...  47  48  49  50  51  52  53  \
0   0   1   2   3   4   5   6   7   8   9  ...  47  48  49  50  51  52  53   

   54  55  56  
0  54  55  56  

[1 rows x 57 columns]

NB. If you really want names from 0 to n, just omit the "columns" parameter as this will be the default

CodePudding user response：

By default, column names are listed from 0 .. N

import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(57)[None,:])

Result:

    0   1   2   3   4   5   6   7   8   9   10  11  12  13  ...  43  44  45  46  47  48  49  50  51  52  53  54  55  56
0   0   1   2   3   4   5   6   7   8   9   10  11  12  13  ...  43  44  45  46  47  48  49  50  51  52  53  54  55  56

[1 rows x 57 columns]