python - Finding and Counting String Values in Pandas DataFrame -


i have pandas data frame , in string values want count. strings want count "synonymous_coding" , "non_synonymous_coding". i've found these strings located in columns 23, 24, 25, 29 , 31.

columns 23 looks this:

15392                                               oanc=c 15393                                                  114 15394    eff=non_synonymous_coding(moderate|missense|gc... 15395                                       0/0:30:90.29:0 15396                                            psc=0.441 15397                                            psc=0.030 15398                                              bsc=884 ...

column 24 looks this:

3092    exon(modifier||||870|rsph10b|protein_coding|co... 3093    non_synonymous_coding(moderate|missense|acg/at... 3094                      intergenic(modifier||||||||||1) 3095                      intergenic(modifier||||||||||1) 3096    downstream(modifier||489|||pms2||coding|nr_003... 3097    downstream(modifier||408|||pms2||coding|nr_003... 3098                                                dp=12 ...

column 25 looks like:

13062                                                      c 13063                                                      c 13064    eff=synonymous(modifier|||||dkfzp434l192||coding... 13065    eff=synonymous(modifier|||||dkfzp434l192||coding... 13066                                                 canc=g 13067                                                      c 13068                                                      g

column 29 looks like:

15688                                                  0:0 15689                                                  0:0 15690                                                  nan 15691    eff=synonymous_coding(low|silent|tcc/tcg|s782|... 15692                                                  0:0 15693                                                  nan 15694                                                  0:1

and column 31 looks like:

3081                                                   45 3082                                               1432:0 3083                                                  0:0 3084    synonymous_coding(low|silent|acg/aca|t473|482|... 3085                                                    9 3086                                                  0:0 3087                                                  0:0

i wanted know how can go through 5 columns , count number of times strings "synonymous_coding" or "non_synonymous_coding" appears without double counting. because there might rows these strings appear in 2 or more different columns.

thank you.

rodrigo

here's worked through, include code used create dataframe. can see algorithm focusing on main() method

def create_df():     grid = (         {'a': ["exon(modifier||||870|rsph10b|protein_coding|co)",                "non_synonymous_coding(moderate|missense|acg/at)",                "intergenic(modifier||||||||||1)",                "downstream(modifier||489|||pms2||coding|nr_003)",                "downstream(modifier||408|||pms2||coding|nr_003)"],          'b': ["foo",                "eff=non_synonymous_coding(moderate|missense|gc",                "non_synonymous_coding(moderate|missense|acg/at)",                "psc=0.441",                "bsc=884"],          'c': ["bar",                "bar",                "eff=synonymous(modifier|||||dkfzp434l192||coding",                "eff=synonymous(modifier|||||dkfzp434l192||coding",                "eff=synonymous_coding(low|silent|tcc/tcg|s782|"],          'd': ["eff=synonymous_coding(low|silent|tcc/tcg|s782|",                "0:0",                "0:0",                "eff=synonymous_coding(low|silent|tcc/tcg|s782|",                "eff=synonymous_coding(low|silent|tcc/tcg|s782|"],         }     )     return pd.dataframe(grid)  def get_masks(df):     non_syn = pd.dataframe(index=df.index, columns=df.columns)     synonymous = pd.dataframe(index=df.index, columns=df.columns)      in df:         non_syn[i] = df[i].str.contains("non_synonymous_coding")         synonymous[i] = df[i][~non_syn[i]].str.contains("synonymous_coding")      return non_syn, synonymous.dropna()  def count_unique_truths(df):     # make unique across rows, , restore regular     df = df.transpose().drop_duplicates().transpose()     return np.sum(df).sum()  def main():     df = create_df()     non_syn, synonymous = get_masks(df)     non_syn_count = count_unique_truths(non_syn)     synonymous_count = count_unique_truths(synonymous)     print(df)     print("synonymous count = {:d}\nnon_synonymous count = {:d}".format(int(synonymous_count), int(non_syn_count)))     df.groupby()  if __name__ == '__main__':     main()

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