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Python Pandas: Boolean indexing on multiple columns [duplicate]

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26

9

This question already has an answer here:

• 
  selecting across multiple columns with python pandas?
  
  3 answers
  
  

despite there being at least two good tutorials on how to index a DataFrame in Python's pandas library, I still can't work out an elegant way of SELECTing on more than one column.

>>> d = pd.DataFrame('x':[1, 2, 3, 4, 5], 'y':[4, 5, 6, 7, 8])
>>> d
 x y
0 1 4
1 2 5
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2] # This works fine
 x y
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2 & d['y']>7] # I had expected this to work, but it doesn't
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I have found (what I think is) a rather inelegant way of doing it, like this

>>> d[d['x']>2][d['y']>7]

But it's not pretty, and it scores fairly low for readability (I think).

Is there a better, more Python-tastic way?

python pandas dataframe

share|improve this question

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

marked as duplicate by towi, Pere Villega, skuntsel, Roman C, rael_kid Jun 21 '13 at 11:01

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

add a comment | 

26

9

This question already has an answer here:

• 
  selecting across multiple columns with python pandas?
  
  3 answers
  
  

despite there being at least two good tutorials on how to index a DataFrame in Python's pandas library, I still can't work out an elegant way of SELECTing on more than one column.

>>> d = pd.DataFrame('x':[1, 2, 3, 4, 5], 'y':[4, 5, 6, 7, 8])
>>> d
 x y
0 1 4
1 2 5
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2] # This works fine
 x y
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2 & d['y']>7] # I had expected this to work, but it doesn't
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I have found (what I think is) a rather inelegant way of doing it, like this

>>> d[d['x']>2][d['y']>7]

But it's not pretty, and it scores fairly low for readability (I think).

Is there a better, more Python-tastic way?

python pandas dataframe

share|improve this question

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

marked as duplicate by towi, Pere Villega, skuntsel, Roman C, rael_kid Jun 21 '13 at 11:01

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

add a comment | 

This question already has an answer here:

• 
  selecting across multiple columns with python pandas?
  
  3 answers
  
  

despite there being at least two good tutorials on how to index a DataFrame in Python's pandas library, I still can't work out an elegant way of SELECTing on more than one column.

>>> d = pd.DataFrame('x':[1, 2, 3, 4, 5], 'y':[4, 5, 6, 7, 8])
>>> d
 x y
0 1 4
1 2 5
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2] # This works fine
 x y
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2 & d['y']>7] # I had expected this to work, but it doesn't
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I have found (what I think is) a rather inelegant way of doing it, like this

>>> d[d['x']>2][d['y']>7]

But it's not pretty, and it scores fairly low for readability (I think).

Is there a better, more Python-tastic way?

python pandas dataframe

share|improve this question

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

>>> d = pd.DataFrame('x':[1, 2, 3, 4, 5], 'y':[4, 5, 6, 7, 8])
>>> d
 x y
0 1 4
1 2 5
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2] # This works fine
 x y
2 3 6
3 4 7
4 5 8
>>> d[d['x']>2 & d['y']>7] # I had expected this to work, but it doesn't
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

>>> d[d['x']>2][d['y']>7]

share|improve this question

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

share|improve this question

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

asked Jun 20 '13 at 14:21

LondonRobLondonRob

28.1k1676117

2 Answers
2

active

oldest

votes

64

It is a precedence operator issue.

You should add extra parenthesis to make your multi condition test working:

d[(d['x']>2) & (d['y']>7)]

This section of the tutorial you mentioned shows an example with several boolean conditions and the parenthesis are used.

share|improve this answer

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

add a comment | 

1

There may still be a better way, but

In [56]: d[d['x'] > 2] and d[d['y'] > 7]
Out[56]: 
 x y
4 5 8

works.

share|improve this answer

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  this works, but ends up using python operators (rather than numpy) and so is going to be much slower
  
  – Jeff
  Jun 20 '13 at 14:44
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  that's a nice solution. I like the fact that it explicitly uses and. Makes it clearer that there are two conditions being evaluated.
  
  – LondonRob
  Jun 20 '13 at 14:45
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Oh, I've just found a duplicate of this question. Whoops.
  
  – LondonRob
  Jun 20 '13 at 14:48
  

  
  
  
  

add a comment | 

64

It is a precedence operator issue.

You should add extra parenthesis to make your multi condition test working:

d[(d['x']>2) & (d['y']>7)]

This section of the tutorial you mentioned shows an example with several boolean conditions and the parenthesis are used.

share|improve this answer

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

add a comment | 

64

It is a precedence operator issue.

You should add extra parenthesis to make your multi condition test working:

d[(d['x']>2) & (d['y']>7)]

This section of the tutorial you mentioned shows an example with several boolean conditions and the parenthesis are used.

share|improve this answer

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

add a comment | 

It is a precedence operator issue.

You should add extra parenthesis to make your multi condition test working:

d[(d['x']>2) & (d['y']>7)]

This section of the tutorial you mentioned shows an example with several boolean conditions and the parenthesis are used.

share|improve this answer

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

d[(d['x']>2) & (d['y']>7)]

share|improve this answer

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

edited Jun 15 '17 at 20:41

A-B-B

24.4k66470

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

answered Jun 20 '13 at 14:43

BoudBoud

19.4k64057

1

There may still be a better way, but

In [56]: d[d['x'] > 2] and d[d['y'] > 7]
Out[56]: 
 x y
4 5 8

works.

share|improve this answer

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  this works, but ends up using python operators (rather than numpy) and so is going to be much slower
  
  – Jeff
  Jun 20 '13 at 14:44
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  that's a nice solution. I like the fact that it explicitly uses and. Makes it clearer that there are two conditions being evaluated.
  
  – LondonRob
  Jun 20 '13 at 14:45
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Oh, I've just found a duplicate of this question. Whoops.
  
  – LondonRob
  Jun 20 '13 at 14:48
  

  
  
  
  

add a comment | 

1

There may still be a better way, but

In [56]: d[d['x'] > 2] and d[d['y'] > 7]
Out[56]: 
 x y
4 5 8

works.

share|improve this answer

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  this works, but ends up using python operators (rather than numpy) and so is going to be much slower
  
  – Jeff
  Jun 20 '13 at 14:44
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  that's a nice solution. I like the fact that it explicitly uses and. Makes it clearer that there are two conditions being evaluated.
  
  – LondonRob
  Jun 20 '13 at 14:45
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Oh, I've just found a duplicate of this question. Whoops.
  
  – LondonRob
  Jun 20 '13 at 14:48
  

  
  
  
  

add a comment | 

There may still be a better way, but

In [56]: d[d['x'] > 2] and d[d['y'] > 7]
Out[56]: 
 x y
4 5 8

works.

share|improve this answer

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456

In [56]: d[d['x'] > 2] and d[d['y'] > 7]
Out[56]: 
 x y
4 5 8

share|improve this answer

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456

answered Jun 20 '13 at 14:33

TomAugspurgerTomAugspurger

16k35456