pandas - Python - Remove rows and columns in Matrix where all values are 0 -


i have matrix created dataframe , want remove columns every value in 0.

i have seen examples using dropna df2.loc[:, (df2 != 0).any(axis=0)] doesnt dataframe.

this how created matrix:

a = ['psychology','education','social policy','sociology','pol. sci. & internat. studies','development studies','social anthropology','area studies','science , technology studies','law & legal studies','economics','management & business studies','human geography','environmental planning','demography','social work','tools, technologies & methods','linguistics','history'] final_df = new_df[new_df['subject'].isin(a)]  ctrs = {location: counter(gp.grantrefnumber) location, gp in final_df.groupby('subject')}  ctrs = list(ctrs.items()) overlaps = [(loc1, loc2, sum(min(ctr1[k], ctr2[k]) k in ctr1))     i, (loc1, ctr1) in enumerate(ctrs, start=1)     (loc2, ctr2) in ctrs[i:] if loc1 != loc2] overlaps += [(l2, l1, c) l1, l2, c in overlaps]  df22 = pd.dataframe(overlaps, columns=['loc1', 'loc2', 'count']) df22 = df22.set_index(['loc1', 'loc2']) df22 = df22.unstack().fillna(0).astype(int)  #the end part of next line filters top 'x' amount. b = np.sort(np.unique(df22.values.ravel()))[-20:] df2 = df22.where(df22.isin(b),0.0)

interestingly (or not), when type df2.columns, get:

multiindex(levels=[[u'count'], [u'area studies', u'demography', u'development studies', u'economics', u'education', u'environmental planning', u'history', u'human geography', u'law & legal studies', u'linguistics', u'management & business studies', u'pol. sci. & internat. studies', u'psychology', u'science , technology studies', u'social anthropology', u'social policy', u'social work', u'sociology', u'tools, technologies & methods']],            labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]],            names=[none, u'loc2'])

which might why struggling.

you need all true of columns contains 0 ~ invert condition:

df = pd.dataframe({'b':[0,0,0,0,0,0],                    'c':[0,8,9,4,2,3],                    'd':[0,3,5,7,1,0],                    'e':[0,3,6,9,2,4]})  print (df)    b  c  d  e 0  0  0  0  0 1  0  8  3  3 2  0  9  5  6 3  0  4  7  9 4  0  2  1  2 5  0  3  0  4  df = df.loc[~df.eq(0).all(axis=1), ~df.eq(0).all()] print (df)    c  d  e 1  8  3  3 2  9  5  6 3  4  7  9 4  2  1  2 5  3  0  4

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