How to cycle through values in Pandas Dataframe?

April 8, 2023

What’s the fastest way to cycle through and repeat names in one dataframe when updating certain rows in a second dataframe? Thank you for your help. I’ve spent many days on this and am getting nowhere.

Rows with Rules 4,5,6,10,11,12 should cycle through and repeat names in the Names Dataframe.

This is the dataframe I want to change:

Rule	Name
12	Nan
9	Nan
7	Nan
1	Nan
3	Nan
2	Nan
4	Nan
6	Nan
9	Nan
4	Nan
1	Nan
4	Nan
5	Nan
2	Nan
11	Nan
6	Nan
9	Nan
2	Nan
10	Nan
3	Nan
10	Nan
3	Nan
1	Nan
4	Nan
4	Nan
3	Nan
7	Nan
7	Nan
12	Nan

This is the result I am trying to achieve:

Rule	Name
12	Adams
9	Nan
7	Nan
1	Nan
3	Nan
2	Nan
4	Baker
6	Clark
9	Nan
4	Davis
1	Nan
4	Adams
5	Baker
2	Nan
11	Clark
6	Davis
9	Nan
2	Nan
10	Adams
3	Nan
10	Baker
3	Nan
1	Nan
4	Clark
4	Davis
3	Nan
7	Nan
7	Nan
12	Adams

Name Dataframe

Name
Adams
Baker
Clark
Davis

I tried to numpy vectorize, but only got one name for all the entries that I wanted to change.

>Solution :

IIUC, you can

check if each element of df1 Rule column isin your rule list.
Then cycle through and repeat names of df2 Name column depending on the length of the contained rule.
At last conditionally assign the repeat names to df1 Rule column.

lst = [4,5,6,10,11,12]
m = df1['Rule'].isin(lst)
df1.loc[m, 'Name'] = (df2['Name'].tolist() * (m.sum()//len(df2['Name']) + 1))[:m.sum()]

print(df1)

    Rule   Name
0     12  Adams
1      9    Nan
2      7    Nan
3      1    Nan
4      3    Nan
5      2    Nan
6      4  Baker
7      6  Clark
8      9    Nan
9      4  Davis
10     1    Nan
11     4  Adams
12     5  Baker
13     2    Nan
14    11  Clark
15     6  Davis
16     9    Nan
17     2    Nan
18    10  Adams
19     3    Nan
20    10  Baker
21     3    Nan
22     1    Nan
23     4  Clark
24     4  Davis
25     3    Nan
26     7    Nan
27     7    Nan
28    12  Adams