Selecting rows from a Dataframe based on values in multiple columns in pandas

Question

This question is very related to another, and I'll even use the example from the very helpful accepted solution on that question. Here's the example from the accepted solution (credit to unutbu):

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                   'B': 'one one two three two two one three'.split(),
                   'C': np.arange(8), 'D': np.arange(8) * 2})
print(df)
#      A      B  C   D
# 0  foo    one  0   0
# 1  bar    one  1   2
# 2  foo    two  2   4
# 3  bar  three  3   6
# 4  foo    two  4   8
# 5  bar    two  5  10
# 6  foo    one  6  12
# 7  foo  three  7  14

print(df.loc[df['A'] == 'foo'])

yields

     A      B  C   D
0  foo    one  0   0
2  foo    two  2   4
4  foo    two  4   8
6  foo    one  6  12
7  foo  three  7  14

But what if I want to pick out all rows that include both 'foo' and 'one'? Here that would be row 0 and 6. My attempt at it is to try

print(df.loc[df['A'] == 'foo' and df['B'] == 'one'])

This does not work, unfortunately. Can anybody suggest a way to implement something like this? Ideally it would be general enough that there could be a more complex set of conditions in there involving and and or, though I don't actually need that for my purposes.

Answer 1

There is only a very small change needed in your code: change the and with & (and add parentheses for correct ordering of comparisons):

In [104]: df.loc[(df['A'] == 'foo') & (df['B'] == 'one')]
Out[104]:
     A    B  C   D
0  foo  one  0   0
6  foo  one  6  12

The reason you have to use & is that this will do the comparison element-wise on arrays, while and expect to compare two expressions that evaluate to True or False.
Similarly, when you want the or comparison, you can use | in this case.

Answer 2

You can do this with tiny altering in your code:

print(df[df['A'] == 'foo'][df['B'] == 'one'])

Output:

     A    B  C   D
0  foo  one  0   0
6  foo  one  6  12