[FIXED] Transforming every n rows of pandas Series into n columns of a DataFrame

Issue

I'm trying to transform a pandas Series like:

Date	Value
2020-01-01	-1175
2020-01-02	-475
2020-01-03	1945
2020-01-06	-1295
2020-01-07	-835
2020-01-08	-785
2020-01-09	895
2020-01-10	-665

into a pandas DataFrame like:

date	0	1	2	3	4
2020-01-01	-1175	-475	1945	-1295	-665
2020-01-02	-475	1945	-1295	-835	-785
2020-01-03	1945	-1295	-835	-785	895
2020-01-06	-1295	-835	-785	895	-665

Every 5 (or n) rows of the Series forms one row in the DataFrame.

Sample data along with my current (ugly but working) code is as follows:

import pandas as pd
srs = pd.Series(index=pd.DatetimeIndex(pd.date_range(start="2020-01-01",end="2020-1-10",freq="B")), 
                data=[-1175,-475,1945,-1295,-835,-785,895,-665])
n = 5
df = pd.concat({i: srs.shift(-i) for i in range(n)}, axis=1).dropna()
df = df[range(n)]
df.index = df.index.droplevel(level=0)

I was wondering if there is a better/neater/nicer way to do this?

Solution

Try with sliding_window_view from numpy:

n = 5
v = np.lib.stride_tricks.sliding_window_view(srs.to_numpy(), n)
df = pd.DataFrame(v, index=srs.index[:v.shape[0]])

df:

               0     1     2     3    4
2020-01-01 -1175  -475  1945 -1295 -835
2020-01-02  -475  1945 -1295  -835 -785
2020-01-03  1945 -1295  -835  -785  895
2020-01-06 -1295  -835  -785   895 -665

---

Complete Working Example:

import numpy as np
import pandas as pd

srs = pd.Series(index=pd.DatetimeIndex(
    pd.date_range(start="2020-01-01", end="2020-1-10", freq="B")
), data=[-1175, -475, 1945, -1295, -835, -785, 895, -665])

n = 5
v = np.lib.stride_tricks.sliding_window_view(srs.to_numpy(), n)
df = pd.DataFrame(v, index=srs.index[:v.shape[0]])

print(df)

Answered By - Henry Ecker