I have the following simple data frame:
stores = ['a', 'a', 'b', 'b', 'b']
brands = ['Nike', 'Nike', 'Adidas', 'Nike', 'Adidas']
colours = ['Black', 'Black', 'White', 'Black', 'Black']
data = dict(stores=stores, brands=brands, colours=colours)
df = pd.DataFrame(data, columns=data.keys())
I'd like to query this using a list of columns and a corresponding list of values. For e.g.
columns = ['stores', 'brands']
values = ['a', 'Nike']
df[columns == values]
Is this possible?
This should be possible using numpy.logical_and with reduce for an arbitrary number of conditions:
import numpy as np
df[np.logical_and.reduce([df[col] == val for col, val in zip(columns, values)])]
Results:
stores brands colours
0 a Nike Black
1 a Nike Black
You can do this in a similar way to thesilkworm's answer using only pandas:
query = " & ".join([c + " == '" + v + "'" for c,v in zip(columns, values)])
df.query(query)
Output using the above code:
>>> query = " & ".join([c + " == '" + v + "'" for c,v in zip(columns, values)])
>>> query
"stores == 'a' & brands == 'Nike'"
>>> df.query(query)
stores brands colours
0 a Nike Black
1 a Nike Black
Note the inclusion of single quotes around v in the list comprehension. These are important, since we're comparing a string value. For more info, see the query documentation for pandas.
