Here is what I have, a list with 8 values, and want to split into 4 columns, which means I’d get 2 rows:
list = ['Jan 01 2020', '51.62', '51.87', '50.69', 'Jan 02 2020', '51.98', '52.14', '51.48']
columnNames = ['Date', 'A', 'B', 'C']
And, here what I expect to get:
Date A B C
Jan 01 2020 51.62 51.87 50.69
Jan 02 2020 51.98 52.14 51.48
I tried this, but I don’t know how to make it jump to another row after reading 4th, 8th, 12th,… value (I have a way bigger list actually)
import pandas as pd
list = ['Jan 01 2020', '51.62', '51.87', '50.69', 'Jan 02 2020', '51.98', '52.14', '51.48']
columnNames = ['Date', 'A', 'B', 'C']
df = pd.DataFrame(list, columns = columnNames)
print(df)
df = pd.DataFrame([list[n:n+4] for n in range(0, len(list), 4)], columns=columnNames)
You just need to splice the list into 4 elements each.
PS: it is a bad idea to name your variable as list
Split list into 2. I prefer the splitting method below because it will still work with nth length of the list
l1 = list[:len(lst)//2]
l2 = list[len(lst)//2:]
Create DatFrame
df = pd.DataFrame([l1, l2],columns =columnNames)
Use np.array_split to split your array into 2 equal parts:
In [65]: import numpy as np
In [61]: l1,l2 = np.array_split(list, 2)
In [42]: columnNames = ['Date', 'A', 'B', 'C']
In [44]: df = pd.DataFrame([l1, l2], columns=columnNames)
In [45]: df
Out[45]:
Date A B C
0 Jan 01 2020 51.62 51.87 50.69
1 Jan 02 2020 51.98 52.14 51.48