Sure! Setup:
>>> import pandas as pd
>>> from random import randint
>>> df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)],
'B': [randint(1, 9)*10 for x in range(10)],
'C': [randint(1, 9)*100 for x in range(10)]})
>>> df
A B C
0 9 40 300
1 9 70 700
2 5 70 900
3 8 80 900
4 7 50 200
5 9 30 900
6 2 80 700
7 2 80 400
8 5 80 300
9 7 70 800
We can apply column operations and get boolean Series objects:
>>> df["B"] > 50 0 False 1 True 2 True 3 True 4 False 5 False 6 True 7 True 8 True 9 True Name: B >>> (df["B"] > 50) & (df["C"] == 900) 0 False 1 False 2 True 3 True 4 False 5 False 6 False 7 False 8 False 9 False
[Update, to switch to new-style .loc]:
And then we can use these to index into the object. For read access, you can chain indices:
>>> df["A"][(df["B"] > 50) & (df["C"] == 900)] 2 5 3 8 Name: A, dtype: int64
but you can get yourself into trouble because of the difference between a view and a copy doing this for write access. You can use .loc instead:
>>> df.loc[(df["B"] > 50) & (df["C"] == 900), "A"]
2 5
3 8
Name: A, dtype: int64
>>> df.loc[(df["B"] > 50) & (df["C"] == 900), "A"].values
array([5, 8], dtype=int64)
>>> df.loc[(df["B"] > 50) & (df["C"] == 900), "A"] *= 1000
>>> df
A B C
0 9 40 300
1 9 70 700
2 5000 70 900
3 8000 80 900
4 7 50 200
5 9 30 900
6 2 80 700
7 2 80 400
8 5 80 300
9 7 70 800
Note that I accidentally typed == 900 and not != 900, or ~(df["C"] == 900), but I’m too lazy to fix it. Exercise for the reader. :^)