CODEWITHSUNDEEP
pythonpandasnumpy
user1582859
user1582859

Reputation: 63

Using isin() to determine what should be printed

Right now I have two dataframes (data1 and data2)

I would like to print a column of string values in the dataframe called data1, based on whether the ID exists in both data2 and data1.

What I am doing now gives me a boolean list (True or False if the ID exists in the both dataframes but not the column of strings).

print(data2['id'].isin(data1.id).to_string())

yields

0      True
1      True
2      True
3      True
4      True
5      True

Any ideas would be appreciated.

Here is a sample of data1

'user_id', 'id', 'rating', 'unix_timestamp'

196 242 3   881250949
186 302 3   891717742
22  377 1   878887116

And data2 contains something like this

'id', 'title', 'release_date', 'video_release_date', 'imdb_url'

37|Nadja (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Nadja%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
38|Net, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Net,%20The%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
39|Strange Days (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Strange%20Days%20(1995)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0

Upvotes: 3

Views: 1117

Answers (2)

jezrael
jezrael

Reputation: 862791

If all values of ids are unique:

I think you need merge with inner join. For data2 select only id column, on parameter should be omit, because joining on all columns - here only id:

df = pd.merge(data1, data2[['id']])

Sample:

data1 = pd.DataFrame({'id':list('abcdef'),
                      'B':[4,5,4,5,5,4],
                      'C':[7,8,9,4,2,3]})

print (data1)
   B  C id
0  4  7  a
1  5  8  b
2  4  9  c
3  5  4  d
4  5  2  e
5  4  3  f

data2 = pd.DataFrame({'id':list('frcdeg'),
                      'D':[1,3,5,7,1,0],
                      'E':[5,3,6,9,2,4],})

print (data2)
   D  E id
0  1  5  f
1  3  3  r
2  5  6  c
3  7  9  d
4  1  2  e
5  0  4  g

df = pd.merge(data1, data2[['id']])
print (df)
   B  C id
0  4  9  c
1  5  4  d
2  5  2  e
3  4  3  f

If id are duplicated in one or another Dataframe use another answer, also added similar solutions:

df = data1[data1['id'].isin(set(data1['id']) & set(data2['id']))]

ids = set(data1['id']) & set(data2['id'])
df = data2.query('id in @ids')

df = data1[np.in1d(data1['id'], np.intersect1d(data1['id'], data2['id']))]

Sample:

data1 = pd.DataFrame({'id':list('abcdef'),
                      'B':[4,5,4,5,5,4],
                      'C':[7,8,9,4,2,3]})

print (data1)
   B  C id
0  4  7  a
1  5  8  b
2  4  9  c
3  5  4  d
4  5  2  e
5  4  3  f

data2 = pd.DataFrame({'id':list('fecdef'),
                      'D':[1,3,5,7,1,0],
                      'E':[5,3,6,9,2,4],})

print (data2)
   D  E id
0  1  5  f
1  3  3  e
2  5  6  c
3  7  9  d
4  1  2  e
5  0  4  f

df = data1[data1['id'].isin(set(data1['id']) & set(data2['id']))]
print (df)
   B  C id
2  4  9  c
3  5  4  d
4  5  2  e
5  4  3  f

EDIT:

You can use:

df = data2.loc[data1['id'].isin(set(data1['id']) & set(data2['id'])), ['title']]

ids = set(data1['id']) & set(data2['id'])
df = data2.query('id in @ids')[['title']]

df = data2.loc[np.in1d(data1['id'], np.intersect1d(data1['id'], data2['id'])), ['title']]

Upvotes: 2

cs95
cs95

Reputation: 402603

You can compute the set intersection of the two columns -

ids = set(data1['id']).intersection(data2['id'])

Or,

ids = np.intersect1d(data1['id'], data2['id'])

Next, query/filter out relevant rows.

data1.loc[data1['id'].isin(ids), 'id']

Upvotes: 1

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