1

I have a pandas dataframe that looks something like this:

    user    date        value
    1       01-2000     5
    1       02-2000     6
    1       03-2000     7
    1       04-2000     8
    2       01-2000     3
    2       02-2000     4
    2       03-2000     5
    2       04-2000     6
    3       01-2000     10
    3       02-2000     11
    3       03-2000     12
    3       04-2000     13

I'd like to create a new column with the value for each user for the previous month, so it looks something like this:

    user    date        value    new_col
    1       01-2000     5        NaN
    1       02-2000     6        5
    1       03-2000     7        6
    1       04-2000     8        7
    2       01-2000     3        NaN
    2       02-2000     4        3
    2       03-2000     5        4
    2       04-2000     6        5
    3       01-2000     10       NaN
    3       02-2000     11       10
    3       03-2000     12       11
    3       04-2000     13       12

Please can someone advise how best to do this? Thanks.

Improve this question

1 Answer 1

Reset to default
3

Use shift():

import pandas as pd
import io

text = '''\
    user    date        value
    1       01-2000     5
    1       02-2000     6
    1       03-2000     7
    1       04-2000     8
    2       01-2000     3
    2       02-2000     4
    2       03-2000     5
    2       04-2000     6
    3       01-2000     10
    3       02-2000     11
    3       03-2000     12
    3       04-2000     13'''

df = pd.read_table(io.BytesIO(text), sep='\s+')
df['new_col'] = df.groupby('user')['value'].apply(lambda grp: grp.shift())
print(df)

yields

    user     date  value  new_col
0      1  01-2000      5      NaN
1      1  02-2000      6        5
2      1  03-2000      7        6
3      1  04-2000      8        7
4      2  01-2000      3      NaN
5      2  02-2000      4        3
6      2  03-2000      5        4
7      2  04-2000      6        5
8      3  01-2000     10      NaN
9      3  02-2000     11       10
10     3  03-2000     12       11
11     3  04-2000     13       12
Improve this answer
Sign up to request clarification or add additional context in comments.

Comments

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.