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# Using Statistical Methods on DataFrames

**00:00**
pandas provides many statistical methods for DataFrames. For example, you can get some basic stats for the numerical columns of a DataFrame by using the `.describe()`

method. So on our `df`

DataFrame, if we call the `.describe()`

method,

**00:18**
we’re going to get a new DataFrame. The rows are going to give us the stats for all of the columns that have a numerical value. We’re going to get a count—so, the number of rows—and then we’re going to get the mean, standard deviation, the min, and then the 25th, 50th, and 75th percentiles, and then the maximum.

**00:40**
This provides a quick overview of some of the statistical information of your DataFrame. Now, if you wanted to get some of these stats for a particular column—so, for example, let’s say for the `py-score`

column—

**00:55**
then we can call the `.mean()`

method and that’ll give us the mean. So that corresponds to that `35.0`

. Or say we wanted the standard deviation,

**01:07**
we could you use the `.std()`

method. Now, we can also use these methods on the entire DataFrame. So if we just wanted, say, the mean of all of the numerical columns, this is going to return a Series where the labels are going to be the columns where a numerical mean can be computed.

**01:26**
So, notice that when you apply these methods to a DataFrame, you get a pandas `Series`

, and when you apply one of these methods to a Series, like standard deviation, you’re going to get a number. All right, that’s a quick lesson there on doing some basic stats on a DataFrame.

**01:45**
In the next lesson, we’ll take a look at the methods that pandas provides for working with missing data in the DataFrame.

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tonypyon March 23, 2023Just an FYI.

When using df.mean():

FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying ‘numeric_only=None’ is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.mean()