got a pd database called data:
transaction_id house_id date_sale sale_price boolean_2015
0 1 1 31 Mar 2016 £880,000 True
3 4 2 31 Mar 2016 £450,000 True
4 5 3 31 Mar 2016 £680,000 True
6 7 4 31 Mar 2016 £1,850,000 True
7 8 5 31 Mar 2016 £420,000 True
and another one called houses:
id address postcode postcode first
0 1 Flat 78, Andrewes House, Barbican, London, Gre... EC2Y 8AY EC2Y
1 2 Flat 35, John Trundle Court, Barbican, London,... EC2Y 8DJ EC2Y
and question is how do I add a column to data called 'postcode_first' where I look up data['house_id'] and add the first part of the postcode to each row in data['postcode_first']?
the closest I got was
data['postcode'] = np.where(houses['id'] == data['house_id'])
but this doesnt make sense at all any help guys? EDIT also tried data['postcode'] = houses.loc[houses['id'] == data['house_id']]['postcode_first']
but this returned
Traceback (most recent call last):
File "/Users/saminahbab/Documents/House_Prices/final project/sql_functions.py", line 30, in <module>
data['postcode'] = houses.loc[houses['id'] == data['house_id']]['postcode_first']
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/ops.py", line 735, in wrapper
raise ValueError('Series lengths must match to compare')
ValueError: Series lengths must match to compare
which shouldnt matter because I am trying to essentially say data['postcode'] equals houses['postcode_first'] WHERE houses['id'] equals data['house_id']
You can use map() method:
In [108]: df['postcode'] = df.house_id.map(h.set_index('id')['postcode first'])
In [109]: df
Out[109]:
transaction_id house_id date_sale sale_price boolean_2015 postcode
0 1 1 31 Mar 2016 £880,000 True EC2Y
3 4 2 31 Mar 2016 £450,000 True EC2Y
4 5 3 31 Mar 2016 £680,000 True NaN
6 7 4 31 Mar 2016 £1,850,000 True NaN
7 8 5 31 Mar 2016 £420,000 True NaN
Data:
In [110]: h
Out[110]:
id address postcode postcode first
0 1 Flat 78, Andrewes House, Barbican, London, Gre EC2Y 8AY EC2Y
1 2 Flat 35, John Trundle Court, Barbican, London EC2Y 8DJ EC2Y
In [113]: df
Out[113]:
transaction_id house_id date_sale sale_price boolean_2015
0 1 1 31 Mar 2016 £880,000 True
3 4 2 31 Mar 2016 £450,000 True
4 5 3 31 Mar 2016 £680,000 True
6 7 4 31 Mar 2016 £1,850,000 True
7 8 5 31 Mar 2016 £420,000 True
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