How can I add a column to a dataframe with a value conditional on another dataframe?

Question

I'm working with two dataframes:

Dataframe1 looks like:

user (index)	apples	bananas
Pete	4	2
Sara	5	10
Kara	4	2
Tom	3	3

Dataframe2 looks like:

index	user
1	Pete
2	Sara

I want to create a new boolean column in dataframe1 that is true if the user is in dataframe 2. So output looks like:

user	apples	bananas	new column
Pete	4	2	True
Sara	5	10	True
Kara	4	2	False
Tom	3	3	False

I tried using lambda function but didn't get very far.

Answer 1

Here is an easy way of doing that.

df = df.reset_index()
df2['new_column']=True

df = pd.merge(df, df2, left_on='user', right_on='user', how = 'left')
df.new_column.fillna(False, inplace=True)

Answer 2

You can leverage the indicator param of df.merge . Then use df.replace :

In [598]: x = df1.merge(df2['user'], left_on='user (index)', right_on='user', how='left', indicator='new column').replace({'both': True, 'left_only':False}).drop('user', 1)

In [599]: x
Out[599]: 
  user (index)  apples  bananas  new column
0         Pete       4        2        True
1         Sara       5       10        True
2         Kara       4        2       False
3          Tom       3        3       False

OR:

For some better performance, use Series.map instead of df.replace :

In [609]: y = df1.merge(df2['user'], left_on='user (index)', right_on='user', how='left', indicator='new column').drop('user', 1)

In [611]: y['new column'] = y['new column'].map({'both': True, 'left_only':False})

In [612]: y
Out[612]: 
  user (index)  apples  bananas new column
0         Pete       4        2       True
1         Sara       5       10       True
2         Kara       4        2      False
3          Tom       3        3      False