Python list of lists to pandas DataFrame-CodePudding

I have this sample code below that turns a Python list of lists into a pandas DataFrame, but I need to DataFrame orientation to be inversed, any idea how to achieve this?

import numpy as np
import pandas as pd
from IPython.display import display, HTML

i = np.zeros(10)
i.fill(40)

j = np.zeros(10)
j.fill(60)

df = pd.DataFrame([i, j])

display(df)

The output of this is a structure like this:

	0	1	2	3	4	5	6	7	8	9
0	40	40	40	40	40	40	40	40	40	40
1	60	60	60	60	60	60	60	60	60	60

But, what I want is to get a structure like this, how can I turn a list of lists into such format?

0	1
40	60
40	60
40	60
40	60
40	60
40	60
40	60
40	60
40	60

CodePudding user response：

just transpose the dataframe :

df = df.T

or prepare your df the way you want at the first place:

df = pd.DataFrame({0: i, 1: j})

output:

>>
      0     1
0  40.0  60.0
1  40.0  60.0
2  40.0  60.0
3  40.0  60.0
4  40.0  60.0
5  40.0  60.0
6  40.0  60.0
7  40.0  60.0
8  40.0  60.0
9  40.0  60.0

CodePudding user response：

Suggest trying

df.transpose()

for the purpose.

CodePudding user response：

Let's try to create a two columns dataframe directly

df = pd.DataFrame(zip(i, j))
# or specify the column header
df = pd.DataFrame(zip(i, j), columns=['a', 'b'])

print(df)

      0     1
0  40.0  60.0
1  40.0  60.0
2  40.0  60.0
3  40.0  60.0
4  40.0  60.0
5  40.0  60.0
6  40.0  60.0
7  40.0  60.0
8  40.0  60.0
9  40.0  60.0

CodePudding user response：

If you want to correct this before creating your dataframe, you can use numpy.column_stack and then create the dataframe.

inp_arr = np.column_stack((i,j))
df = pd.DataFrame(inp_arr)