Differences in relative abundances obtained in taxabarplot using grouped tables with calculated in Python by "average" method from numpy

Dear all!
Me again.
I produced a taxabarplot file with grouped by niche samples:

    qiime feature-table group \
        --i-table Infected_table.qza \
        --p-axis 'sample' \
        --m-metadata-file Metadata/metadata.tsv \
        --m-metadata-column Niche \
        --p-mode 'mean-ceiling' \
        --o-grouped-table Infected_Niche_grouped.qza 

    qiime taxa barplot \
        --i-table Infected_Niche_grouped.qza \
        --i-taxonomy Data/combo_taxonomy.qza \
        --m-metadata-file Metadata/Niche_metadata \
        --o-visualization $taxabar Niche_taxabarplot.qzv

Then we decided to test the differences between the same Phylum in two different niches - roots and galls. But in taxabarplot, abundances already grouped by Niches. So I created another taxabarplot with original table:

qiime taxa barplot \
    --i-table Data/tables/Infected_table.qza \
    --i-taxonomy Data/combo_taxonomy.qza \
    --m-metadata-file Metadata/metadata.tsv \
    --o-visualization Results/Taxa_barplots/Infected_taxabarplot.qzv

Now I want to apply Mann–Whitney U test to test differences in relative abundances of top 10 features between two niches:
The code in Python 3 I used:

import pandas as pd
from numpy import average
from itertools import combinations
from scipy.stats import mannwhitneyu as mwu
from statsmodels.stats.multitest import multipletests

def bar_unzip(qza,lev):  
    a = !unzip $qza
    digest = a[1].split('/')[0].replace('  inflating: ','')
    inf = digest + '/data/level-%s.csv'%lev
    data = pd.read_csv(inf, sep=',',index_col=0)
    !rm -r $digest
    return data

def relative(df):
    cols = [col for col in df.columns if 'D_1__' in col]
    df = df.loc[:,cols]
    df.columns = [col.split('D_1__')[-1] for col in df.columns]
    df = df.loc[:, (df.sum(axis=0) != 0)]
    df = df.div(df.sum(axis=1), axis=0)*100
    df = df.append(df.agg(['mean']))
    return df
data = bar_unzip('Results/Taxa_barplots/Infected_taxabarplot.qzv',2)

# Root vs Gall
r_df,g_df = relative(data.loc[data.Niche=='Root'].copy()),relative(data.loc[data.Niche=='Gall'].copy())
cols = list(set(r_df.columns.tolist()[0:10]+g_df.columns.tolist()[0:10]))
mwu_results = pd.DataFrame(columns=['mean_root','mean_gall','u','p'])
for col in cols:
    root,gall = r_df[col].tolist(),g_df[col].tolist()
    u, p = mwu(root,gall)
    mwu_results.loc[col] = [average(root),average(gall),u,p]          
mwu_results['q'] = multipletests(mwu_results.p,method='fdr_bh')[1]
mwu_results['p'] = round(mwu_results['p'],4)
mwu_results['q'] = round(mwu_results['q'],4)

In the result, I got this table:

Here, we can see, that, for example, mean relative abundance of Proteobacteria in Root samples equals to 45.86, but in my previous Niche_taxabarplot file with grouped by Niche tables it is showing, that relative abundance of Proteobacteria in root samples equals to 42.79

Could someone help me to find a mistake in my code?