I have a dataframe like below
name item
0 Jack A
1 Sarah B
2 Ross A
3 Sean C
4 Jack C
5 Ross B
What I like to do is to produce a dictionary that connects people to the products they are related to.
{Jack: [1, 0, 1], Sarah: [0, 1, 0], Ross:[1, 1, 0], Sean:[0, 0, 1]}
I feel like this should be done fairly easily using pandas.groupby
I have tried looping through the dataframe, but I have >1E7 entries, and looping does not look very efficient.
Check with crosstab and to_dict
pd.crosstab(df.item,df.name).to_dict('l')
{'Jack': [1, 0, 1], 'Ross': [1, 1, 0], 'Sarah': [0, 1, 0], 'Sean': [0, 0, 1]}
Another interesting option is using str.get_dummies:
# if you need counts
df.set_index('item')['name'].str.get_dummies().sum(level=0).to_dict('l')
# if you want to record boolean indicators
df.set_index('item')['name'].str.get_dummies().max(level=0).to_dict('l')
# {'Jack': [1, 0, 1], 'Ross': [1, 1, 0], 'Sarah': [0, 1, 0], 'Sean': [0, 0, 1]}
