I have two dataframes. First one:
import pandas as pd a = [['xxx', 'admin'], ['yyy', 'admin,super admin'], ['zzz', 'guest,admin,superadmin']] df1 = pd.DataFrame(a, columns=['user', 'groups'])
second one:
b = [['xxx', 'admin,super admin'], ['www', 'admin,super admin'], ['zzz', 'guest,superadmin']] df2 = pd.DataFrame(b, columns=['user', 'groups'])
this is the first one:
user groups 0 xxx admin 1 yyy admin,super admin 2 zzz guest,admin,superadmin
this is the second one:
user groups 0 xxx admin,super admin 1 www admin,super admin 2 zzz guest,superadmin
I want to do two things:
if the second one’s user is not in the first one, then print out. like: www is not in the list
if the user is in the list, but group is not equal then print out:
likexxx
user have more:super admin
than the list
zzz
user has less:admin
than the list.
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Answer
If there are same index values ina number of length in both DataFrame and need compare values per rows:
print (df1.index.equals(df2.index)) True #compare rows for not equal mask = df1['user'].ne(df2['user']) #filter rows by mask and column user in df2 a = df2.loc[mask, 'user'].tolist() print (a) ['www']
#join both DataFrames together df1 = pd.concat([df1, df2], axis=1, keys=('a','b')) df1.columns = df1.columns.map('_'.join) #filter only same user rows df1 = df1[~mask] #split columns by , ans convert to sets df1['a'] = df1['a_groups'].apply(lambda x: set(x.split(','))) df1['b'] = df1['b_groups'].apply(lambda x: set(x.split(','))) #get difference of sets, join to strings with separator , df1['a_diff'] = [', '.join(x.difference(y)) for x, y in zip(df1['b'],df1['a'] )] df1['b_diff'] = [', '.join(x.difference(y)) for x, y in zip(df1['a'],df1['b'] )] print (df1) a_user a_groups b_user b_groups 0 xxx admin xxx admin,super admin 2 zzz guest,admin,superadmin zzz guest,superadmin a b a_diff b_diff 0 {admin} {admin, super admin} super admin 2 {admin, superadmin, guest} {superadmin, guest} admin
#filter by casting set columns to boolean, empty sets are converted to False b = df1.loc[df1['a_diff'].astype(bool), ['a_user','a_diff']] print (b) a_user a_diff 0 xxx super admin c = df1.loc[df1['b_diff'].astype(bool), ['a_user','b_diff']] print (c) a_user b_diff 2 zzz admin