How to append rows in pandas.DataFrame in for loop

Question

Hi I am trying to append rows in a new created pandas dataframe


###expression
for i in active_brain_regions:
    indices = np.where(LM == i)
    expr = gene[indices]
    mean = np.mean(expr)
    dat= [[i, mean, str("nifti")]]
    dd = pd.DataFrame(dat, columns = ['region', 'mean expr', 'gene_id'])
    dd.append(dd)

dd should look like

 0.0  -0.424069   nifti
 1.0       -1.0   nifti
 .
 . 
 .
 11.0    0.23352   nifti

But after running the for loop dd is just a dataframe of size (1,3) with the last entry of region 11..

jezrael · Accepted Answer · 2020-02-09 14:55:26Z

4

You can assign back, because is used pandas method DataFrame.append:

dd = dd.append(dd)

Or create list of DataFrames by pure python append (working inplace) and use concat only once:

L = []
for i in active_brain_regions:
    indices = np.where(LM == i)
    expr = gene[indices]
    mean = np.mean(expr)
    dat= [[i, mean, str("nifti")]]
    dd = pd.DataFrame(dat, columns = ['region', 'mean expr', 'gene_id'])
    L.append(dd)

 df = pd.concat(L, ignore_index=True)

But fastest solution should be create list of lists and pass to DataFrame constructor only once:

L = []
for i in active_brain_regions:
    indices = np.where(LM == i)
    expr = gene[indices]
    mean = np.mean(expr)
    dat= [[i, mean, str("nifti")]]
    L.append(dat)

 df = pd.DataFrame(L, columns = ['region', 'mean expr', 'gene_id'])

answered Feb 9, 2020 at 14:55

jezrael

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