How set value on dataframe given a variable number of conditions?

Question

from itertools import product
import pandas as pd

animals = ["dogs", "cats"]
eyes = ['brown', 'blue', 'green']
height = ['short', 'average', 'tall']
a = [animals, eyes, height]
df = pd.DataFrame(list(product(*a)), columns=["animals", "eyes", "height"])
df['value'] = 1

Output:

   animals   eyes   height  value
0     dogs  brown    short      1
1     dogs  brown  average      1
2     dogs  brown     tall      1
3     dogs   blue    short      1
4     dogs   blue  average      1
5     dogs   blue     tall      1
6     dogs  green    short      1

Question: How do I create a single function such that zeros "value" in one or many rows given one or many conditions?

Examples:

# This would change all the 1s into 0s for all dogs with blue eyes.
zero_out(df, [("animals", "dogs"), ("eyes", "blue")])

# This would change all the 1s into 0s for all tall animals.
zero_out(df, [("height", "tall")])

My attempts thus far: I tried to do this with *unpacking but had no luck because I don't know how to set multiple conditions using unpacked variables. It's easy to set multiple conditions if I hardcode the number of conditions though... df[(condition1) & (condition2) & (condition3)] = 0

Further, and perhaps this is outside the scope of the question, how can i set a variable number of conditions given a regular if statement using *unpacking (or without hardcoding the number of conditions in the if statement?

E.g.

if a > 0 and b > 4
#Or...
if a > 0 and b > 4 and c < 2

Your help is appreciated.

Answer 1

If I understand you correctly, you are looking for .query() method:

import pandas as pd
from itertools import product

animals = ["dogs", "cats"]
eyes = ['brown', 'blue', 'green']
height = ['short', 'average', 'tall']
a = [animals, eyes, height]
df = pd.DataFrame(list(product(*a)), columns=["animals", "eyes", "height"])
df['value'] = 1


def zero_out(df, lst):
    q = ' & '.join( '{} == "{}"'.format(col, val) for col, val in lst )
    df.loc[df.query(q).index, 'value'] = 0

zero_out(df, [("height", "tall")])
print(df)

Prints:

   animals   eyes   height  value
0     dogs  brown    short      1
1     dogs  brown  average      1
2     dogs  brown     tall      0
3     dogs   blue    short      1
4     dogs   blue  average      1
5     dogs   blue     tall      0
6     dogs  green    short      1
7     dogs  green  average      1
8     dogs  green     tall      0
9     cats  brown    short      1
10    cats  brown  average      1
11    cats  brown     tall      0
12    cats   blue    short      1
13    cats   blue  average      1
14    cats   blue     tall      0
15    cats  green    short      1
16    cats  green  average      1
17    cats  green     tall      0

---

Or zero_out(df, [("animals", "dogs"), ("eyes", "blue")]):

   animals   eyes   height  value
0     dogs  brown    short      1
1     dogs  brown  average      1
2     dogs  brown     tall      1
3     dogs   blue    short      0
4     dogs   blue  average      0
5     dogs   blue     tall      0
6     dogs  green    short      1
7     dogs  green  average      1
8     dogs  green     tall      1
9     cats  brown    short      1
10    cats  brown  average      1
11    cats  brown     tall      1
12    cats   blue    short      1
13    cats   blue  average      1
14    cats   blue     tall      1
15    cats  green    short      1
16    cats  green  average      1
17    cats  green     tall      1