How to compute standard error of mean of groups in pandas?
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# How to compute standard error of mean of groups in pandas?

This recipe helps you compute standard error of mean of groups in pandas

0

## Recipe Objective

Many a times, we have groups and might be interested to combine them thereby calculating standard deviation of dataset.

So this recipe is a short example on how to compute standard error of mean of groups in pandas. Let's get started.

## Step 1 - Import the library

``` import pandas as pd import seaborn as sb ```

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays. Seaborn is just used in here to import dataset.

## Step 2 - Setup the Data

``` df = sb.load_dataset('tips') print(df.head()) ```

Here we have imported tips dataset from seaborn library.

## Step 3 - Finding standard error of the groups

``` print(df.groupby(['sex','smoker','day','time','size']).std()) ```

Here we have performed groupby on certain columns and finally taking out the standard error of our dataset.

## Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

```Scroll down to the ipython file to look at the results.
```

We can see standard error being found out for each groups.

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