CODEWITHSUNDEEP
pythonpandasdataframe
ketan
ketan

Reputation: 2904

How to check values of column in one dataframe available or not in column of another dataframe?

I have two dataframes-

df1_data = {'sym1' :{0:'abc a01',1:'pqr q02',2:'xyz y03',3:'mno o12',4:'lmn l45'}}
df1 = pd.DataFrame(df1_data)
print df1

df2_data = {'sym2' :{0:'abc a01',1:'xxx p0',2:'xyz y03',3:'mno o12',4:'lmn l45',5:'rrr r1',6:'kkk k3'}}
df2 = pd.DataFrame(df2_data)
print df2

output-

      sym1
0  abc a01
1  pqr q02
2  xyz y03
3  mno o12
4  lmn l45
      sym2
0  abc a01
1   xxx p0
2  xyz y03
3  mno o12
4  lmn l45
5   rrr r1
6   kkk k3

I want to check sym2 column values available or not in df2 dataframes sym1 column. If symbols in sym2 column are not available then I want list of that symbols which are not available in sym1 column. If all symbols are available then list must be empty.

Expected Result-

list -> ['xxx p0','rrr r1','kkk k3']

Upvotes: 2

Views: 253

Answers (2)

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210982

Here is another, bit faster, solution:

In [54]: df2.set_index('sym2').index.difference(df1.set_index('sym1').index).values
Out[54]: array(['kkk k3', 'rrr r1', 'xxx p0'], dtype=object)

or as vanilla Python list:

In [74]: df2.set_index('sym2').index.difference(df1.set_index('sym1').index).values.tolist()
Out[74]: ['kkk k3', 'rrr r1', 'xxx p0']

Timings for 700K and 500K DFs:

In [55]: df1 = pd.concat([df1] * 10**5, ignore_index=True)

In [57]: df2 = pd.concat([df2] * 10**5, ignore_index=True)

In [58]: df1.shape
Out[58]: (500000, 1)

In [59]: df2.shape
Out[59]: (700000, 1)

In [67]: %timeit df2.set_index('sym2').index.difference(df1.set_index('sym1').index).values
10 loops, best of 3: 123 ms per loop

In [68]: %timeit df2.ix[~df2.sym2.isin(df1.sym1), 'sym2']
1 loop, best of 3: 216 ms per loop

In [72]: %timeit df2.set_index('sym2').index.difference(df1.set_index('sym1').index).values.tolist()
10 loops, best of 3: 123 ms per loop

Upvotes: 1

jezrael
jezrael

Reputation: 863611

You can use boolean indexing with isin, then select by ix and convert to list by tolist:

print (~df2.sym2.isin(df1.sym1))
0    False
1     True
2    False
3    False
4    False
5     True
6     True
Name: sym2, dtype: bool

print (df2.ix[~df2.sym2.isin(df1.sym1), 'sym2'])
1    xxx p0
5    rrr r1
6    kkk k3
Name: sym2, dtype: object

print (df2.ix[~df2.sym2.isin(df1.sym1), 'sym2'].tolist())
['xxx p0', 'rrr r1', 'kkk k3']

Upvotes: 4

Related Questions