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# Accessing Values in DataFrames

**00:00**
pandas also provides a way for you to access the data or the values of a DataFrame by using integer indices instead of, say, label indices. To do this, you have to use the `.iloc`

accessor method. In order to do this, again, we need to specify two different indices separated by a comma. The first one is going to be the rows and the second one is going to be the columns.

**00:26**
This is now going to be exactly the same as you would when you’re accessing values from a NumPy array. So here, you have to use integers. So I could just pick up the first row, so this would be the first row of the DataFrame.

**00:42**
And I can also pick off only the first row but I want only the first and the third columns. So this would be just the first and third column of the first row. Now, if I wanted all of the rows of the first and third column, then I would just use the colon notation that would give me all of the rows.

**01:07**
So basically, you can use any of the slicing techniques that you would use for a NumPy array to access a sub-DataFrame of a DataFrame with the `.iloc`

method.

**01:19**
Now, there’s sort of a weird thing that you have to keep in mind here. The actual index labels, the row labels of our DataFrame, are integers in this case, right?

**01:28**
They are from `10`

to `16`

. Now, using the `.iloc`

method, if I wanted to access say the first and the third rows, we would think, “Okay, maybe `10`

and `12`

, and we want to get all of the columns.” Then I’m going to get an error, because `10`

and `12`

—these are not actual integer indices starting from `0`

of this DataFrame. `10`

and `12`

are the actual row labels, which just happen to be integers.

**02:01**
So if I wanted to get that first row and also the third row, I would have to use these actual integers that correspond to the first row and the third row, of course always being zero-index-based.

**02:17**
All right, so that would work.

**02:21**
Now using `.iloc`

and `.loc`

, you can access individual cells or values of a DataFrame. So, for example, if I wanted to get the value in the row label `12`

, and maybe the city,

**02:37**
then I would simply pass in `12, 'city'`

. However, when you only need a single value like this, pandas recommends using specialized accessor methods called `.at`

and `.iat`

.

**02:52**
So with `.at`

, you would use the same notation. So in this case, it would be `12`

and `'city'`

,

**02:59**
and this would pick off again, `'Prague'`

—that individual value. So this gives us the same thing as before. Now, with `.at`

, you have to pass in again row labels and column labels, but if you wanted to pass integer-based indices, you would use `.iat`

.

**03:17**
So row number 12, that was the third row, so we pass in an integer index of `2`

and then `city`

was the second column, and so we’d pass in a integer index of `1`

, and that would be `'Prague'`

.

**03:33**
So basically, `.at`

and `.iat`

are the same thing as `.loc`

and `.iloc`

, except here, you’re going to get individual elements always.

**03:45**
Now that we know how to access individual cells or values in a DataFrame or entire rows or columns, let’s talk about modifying the values of a DataFrame.

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