Reputation: 514
How do I extract data based on index values in different columns?
So far I was able to extract data based on index number in the same column (block of 5).
The Dataframe looks like this:
3017 39517.3886
3018 39517.4211
3019 39517.4683
3020 39517.5005
3021 39517.5486
5652 39628.1622
5653 39628.2104
5654 39628.2424
5655 39628.2897
5656 39628.3229
5677 39629.2020
5678 39629.2342
5679 39629.2825
5680 39629.3304
5681 39629.3628
Where the data extracted in col are +/- 2 rows around the index value
I would like to have something that looks more like this:
3017-3021 5652-5656 5677-5681
1 39517.3886 39628.1622 39629.2020
2 39517.4211 39628.2104 39629.2342
3 39517.4683 39628.2424 39629.2825
4 39517.5005 39628.2897 39629.3304
5 39517.5486 39628.3229 39629.3628
and so on depending on the number of data that I want to extract.
The code I'm using to extract data based on index is:
## find index based on the first 0 of a 000 - 111 list
a = stim_epoc[1:]
ss = [(num+1) for num,i in enumerate(zip(stim_epoc,a)) if i == (0,1)]
## extract data from a df (GCamp_ps) based on the previous index 'ss'
fin = [i for x in ss for i in range(x-2, x + 2 + 1) if i in range(len(GCaMP_ps))]
df = time_fip.loc[np.unique(fin)]
print(df)
Upvotes: 3
Views: 1378
Reputation: 59519
Form groups of 5 consecutive rows (since you pull +/-2 rows from a center). Then create the column and index labels and pivot
df = df.reset_index()
s = df.index//5 # If always 5 consecutive values. I.e. +/-2 rows from a center.
df['col'] = df.groupby(s)['index'].transform(lambda x: '-'.join(map(str, x.agg(['min', 'max']))))
df['idx'] = df.groupby(s).cumcount()
df.pivot(index='idx', columns='col', values=0) # Assuming column named `0`
col 3017-3021 5652-5656 5677-5681
idx
0 39517.3886 39628.1622 39629.2020
1 39517.4211 39628.2104 39629.2342
2 39517.4683 39628.2424 39629.2825
3 39517.5005 39628.2897 39629.3304
4 39517.5486 39628.3229 39629.3628
Upvotes: 5