Pandas Get rows if value is in column dataframe

Question

I have Information Gain dataframe and tf dataframe. the data looks like this:

Information Gain

    Term      IG
0   alqur     0.641328
1   an        0.641328
2   ayatayat  0.641328
3   bagai     0.641328
4   bantai    0.641328
5   besar     0.641328

Term Frequency

            A   B   A+B
ahli        1   0   1
alas        1   0   1
alqur       0   1   1
an          0   1   1
ayatayat    0   1   1
...        ... ... ...
terus       0   1   1
tuduh       0   1   1
tulis       1   0   1
ulama       1   0   1
upaya       0   1   1

let's say table Information Gain = IG and table tf = TF

I wanted to check if IG.Term is in TF.index then get the row values so it should be like this:

    Term      A    B    A+B
0   alqur     0    1    1
1   an        0    1    1
2   ayatayat  0    1    1
3   bagai     1    0    1
4   bantai    1    1    2
5   besar     1    0    1

NB: I don't need the IG value anymore

Answer 1

Filter by Series.isin with boolean indexing and convert index to column:

df = TF[TF.index.isin(IG['Term'])].rename_axis('Term').reset_index()
print (df)
       Term  A  B  A+B
0     alqur  0  1    1
1        an  0  1    1
2  ayatayat  0  1    1

Or use DataFrame.merge with default inner join:

df = IG[['Term']].merge(TF, left_on='Term', right_index=True)
print (df)
       Term  A  B  A+B
0     alqur  0  1    1
1        an  0  1    1
2  ayatayat  0  1    1

Answer 2

You can use merge to check it like this:

ig = pandas.DataFrame([['alqur', 0.641328], ['an', 0.641328]], columns=['Term',      'IG'])
tf = pandas.DataFrame([['ahli', 1, 0, 1], ['alqur', 0, 1, 1], ['an', 0, 1, 1]], columns=['index', 'A', 'B', 'A+B'])
tf = tf.set_index('index')

rows_count, _columns_count = tf.shape
merged = tf.merge(ig, left_on='index', right_on='Term', how='inner')

merged contains not missing terms in ig.