# How to compute the standard error of the mean with Pandas using Python

• 2 min## What is the standard error of the mean?

The standard error usually gives you an **idea **of **how close **your **sample **is to the **true population**.

With the mean that would be how close your **sample mean **is to the **true population mean**.

### An example

E.g. You have a basket of apples, some are rotten. You can say that on **average 1 **out of **100 **are rotten (so 1/100 = 0.01 or 1%).

The farmer that sold you the apples does have on average 1000 rotten apples out of 50000 in his stock. (so 1000/50000 = 0.02 or 2%).

We can assume that you were lucky when buying your basket of apples because on average you got more good apples than you should have got. (0.02 - 0.01 = 0.01 or 1% difference).

Now, if your friends also bought some apples, the standard error of the mean would be the variation in the number of rotten apples between you and your friends.

### The conclusion

All that to say that you cannot reliably conclude that you get 1% rotten apple on average and the standard error of the mean is here to tell you how far you are from reality.

Enough talking,

## Here is the code

Here you are! You now know how to compute the standard error of the mean with Pandas using Python.

# More on DataFrames

If you ** want to know more** about

**DataFrame**and

**Pandas**. Check out the other articles I wrote on the topic, just here :