**Hint:**You can adjust the default video playback speed in your account settings.

**Hint:**You can set the default subtitles language in your account settings.

**Sorry!**Looks like there’s an issue with video playback 🙁 This might be due to a temporary outage or because of a configuration issue with your browser. Please see our video player troubleshooting guide to resolve the issue.

# Filtering With Operators

**00:00**
Data filtering is another powerful feature of pandas. It works similarly to indexing with Boolean arrays in NumPy. Let me show you what I mean. Let’s suppose we pick off the `'py-score'`

column and we use the Boolean comparison operator greater or equal to (`>=`

) and put a value of `40`

.

**00:22**
What this will do is return a pandas Series, and the values of the Series that have a value of `False`

are going to correspond to the row that had a value less than `40`

. So in our DataFrame, we’ve got the row with label `11`

, score of `25`

, and that is not greater than `40`

, so we have a `False`

, and so on.

**00:45**
So what we could do is if we wanted to extract from this DataFrame, all of the candidates that had a score on their Python test of at least `40`

, we could simply pass this pandas Series as a selector in the DataFrame. So for example, if I go like this and I’m simply adding this space at the right and left just for a little bit more readability for you to see, this is going to pick off only the rows where the score in the Python test was at least `40`

.

**01:16**
And from this, we see that `15`

, `14`

, and row `16`

all had that. So let’s run this, and let’s see what we get!

**01:28**
So, here we go. We’ve got all of the candidates whose score on the Python test was at least `40`

. Now, if you’re going to be creating conditions like this that might get more complicated or with more logic, a good thing to do is to simply create a new pandas `Series`

.

**01:46**
Maybe we’ll call this `filter_`

, and maybe we’ll save that, then run that again on the DataFrame using the `filter_`

Series that we just created.

**01:58**
What this allows me to do is if I did also want to extract not only the candidates that scored at least `40`

in their Python score, but also, say, at least `90`

in their Django score, then I can use the *AND* operator (`&`

) to add in that we want also the rows whose JS score is at least `90`

.

**02:20**
So if we compare these two on the left of the `&`

operator, we’ve got a pandas `Series`

object, which we saw before, and then on the right, we have another pandas `Series`

object. And so the `&`

operator is going to be comparing element-wise and checking if both of the elements are `True`

, then the resulting element for the resulting pandas Series is going to have a value of `True`

, and `False`

otherwise. I’ll run that.

**02:47**
This, actually, is a good thing that happened. We got an error. And the reason why we get an error is that with these operators, we need to enclose the conditions on the left or on the right—or if we have more conditions—using parentheses.

**03:02**
And the reason why we need to add the parentheses is because the bitwise `&`

operator, it can only act on integers. What’s happening here is that the bitwise operators *AND* (`&`

) and *OR* (`|`

), they have higher precedence than the comparison operators, and so Python is first evaluating the bitwise `&`

.

**03:22**
And we see there, we’ve got this `40`

and also the column there on the right. And so this is what the error is. Now, in addition, though, you may be wondering why we’re using the bitwise `&`

operator.

**03:36**
The reason is because what we want to do is we want to do an element-wise *AND*, and so if we use the regular Boolean `and`

operator, then Python will implicitly obtain the truth values of each of the operands, and so we’ll return a final, either `True`

Boolean or `False`

Boolean, but what we want is an array of Boolean values.

**03:59**
And so pandas overwrites these bitwise `&`

and `|`

operators to do exactly that. That’s why we need to use this bitwise `&`

to get our element-wise *AND* operations. Let’s run that again.

**04:16**
We get that, and then if we run that again, then we see that we only get the candidates that had a Python score of at least `40`

and a Django score of at least `90`

.

**04:29**
If you recall, from our previous filter, that got rid of one candidate.

**04:34**
Now, you can use other operators, for example, the *OR* (`|`

) operator. So maybe instead of either asking for both the Python score and the JS score to be at least `40`

for Python and `90`

for the JS score, we can say, “Well, if one of the conditions is `True`

, then that’s the filter that we want.” So if we run that, in this case, we’ll probably get a few more candidates.

**04:56**
So here are the candidates that had either at least a `40`

in their Python score—we’ve got three—or they got at least a `90`

in their Django score.

**05:08**
All right, so that is a brief overview of the ways that you can filter data out of a DataFrame. There’s a couple other methods that can be considered as a filtering data from a DataFrame, and these are `.where()`

and `.filter()`

, and so we’ll take a look at those next.

Become a Member to join the conversation.