Jupyter Notebook for csv file to select 3 window rolling

Question

I have this big data in csv file:

1. I manage to open this on Jupyter Notebook.
2. The data in csv example: 1 2 3 4 5 6 7 8 9 10
3. And I wanted to open in the notebook as '3 windows rolling' without doing any (sum,mean for example)
4. The output I want in the notebook are>>

Answer 1

First open csv to get first column.

import pandas as pd

df = pd.read_csv("filename.csv")

I will use io only to simulate data from file

text = """first
1
2
3
4
5
6
7
8
9
10"""


import pandas as pd
import io

df = pd.read_csv(io.StringIO(text))

Result

   first
0      1
1      2
2      3
3      4
4      5
5      6
6      7
7      8
8      9
9     10

Next you can use shift to create other columns

df['second'] = df['first'].shift(-1)
df['third'] = df['first'].shift(-2)

Result

   first  second  third
0      1     2.0    3.0
1      2     3.0    4.0
2      3     4.0    5.0
3      4     5.0    6.0
4      5     6.0    7.0
5      6     7.0    8.0
6      7     8.0    9.0
7      8     9.0   10.0
8      9    10.0    NaN
9     10     NaN    NaN

At the end you can remove two last rows with NaN and convert all to integer

df = df[:-2].astype(int)

or if you don't have NaN in other places

df = df.dropna().astype(int)

Result:

   first  second  third
0      1       2      3
1      2       3      4
2      3       4      5
3      4       5      6
4      5       6      7
5      6       7      8
6      7       8      9
7      8       9     10

---

Minimal working code

text = """first
1
2
3
4
5
6
7
8
9
10"""

import pandas as pd
import io

df = pd.read_csv(io.StringIO(text))
#df = pd.DataFrame(range(1,11), columns=['first'])
print(df)

df['second'] = df['first'].shift(-1) #, fill_value=0)
df['third'] = df['first'].shift(-2)
print(df)

#df = df.dropna().astype(int)
df = df[:-2].astype(int)
print(df)

---

EDIT:

The same using for-loop to create any number of columns

text = """col 1
1
2
3
4
5
6
7
8
9
10"""

import pandas as pd
import io

df = pd.read_csv(io.StringIO(text))
#df = pd.DataFrame(range(1,11), columns=['col 1'])
print(df)

number = 5

for x in range(1, number+1):
    df[f'col {x+1}'] = df['col 1'].shift(-x)
print(df)

#df = df.dropna().astype(int)
df = df[:-number].astype(int)
print(df)

Result

   col 1
0      1
1      2
2      3
3      4
4      5
5      6
6      7
7      8
8      9
9     10

   col 1  col 2  col 3  col 4  col 5  col 6
0      1    2.0    3.0    4.0    5.0    6.0
1      2    3.0    4.0    5.0    6.0    7.0
2      3    4.0    5.0    6.0    7.0    8.0
3      4    5.0    6.0    7.0    8.0    9.0
4      5    6.0    7.0    8.0    9.0   10.0
5      6    7.0    8.0    9.0   10.0    NaN
6      7    8.0    9.0   10.0    NaN    NaN
7      8    9.0   10.0    NaN    NaN    NaN
8      9   10.0    NaN    NaN    NaN    NaN
9     10    NaN    NaN    NaN    NaN    NaN

   col 1  col 2  col 3  col 4  col 5  col 6
0      1      2      3      4      5      6
1      2      3      4      5      6      7
2      3      4      5      6      7      8
3      4      5      6      7      8      9
4      5      6      7      8      9     10