Conditional filtering on multiple columns for a pandas dataframe

Question

I have a pandas dataframe with three columns, A (Dates), B (Categorical Values), and C (Actual Values).

A     |  B   |  C
01-19 |  5   |  0.34
01-19 |  3   |  0.25
01-19 |  7   |  0.07
01-20 |  5   |  0.15
01-20 |  2   |  0.36

And so on.

What I want is to filter rows according to their dates and a specific threshold - something like:

[(01-19, x<6), (01-20, x<3)]

In which case that'd give me

A     |  B   |  C
01-19 |  5   |  0.34
01-19 |  3   |  0.25
01-20 |  2   |  0.36

---

My solution is to set up a multi index with A and B, but then I'm not entirely sure how to filter through the B's.

Answer 1

Idea is create dictionary of tresholds for all values of A column, then Series.map to new Series, so possible compare by B column and filter by boolean indexing :

d = {'01-19': 6, '01-20' : 3}
df = df[df['B'] < df['A'].map(d)]
print (df)
       A  B     C
0  01-19  5  0.34
1  01-19  3  0.25
4  01-20  2  0.36

Detail :

print (df['A'].map(d))
0    6
1    6
2    6
3    3
4    3
Name: A, dtype: int64

Answer 2

If only two conditional you can you "and" and "or" operators like that:

df = df[((df['A'] == '01-19') & (df['B'] < 6)) | ((df['A'] == '01-20') & (df['B'] < 3))]