# What are Pandas Series Mean and Standard Deviation

As a data scientist or software engineer, it’s likely that you’ve worked with the Pandas library in Python. Pandas is a powerful tool for data manipulation and analysis that provides a wide range of functionalities to work with tabular data. One of the most frequently used functionalities is the computation of mean and standard deviation of a series.

In this blog post, we will explore the Pandas series mean and standard deviation and provide a step-by-step guide on how to compute them.

## What is a Pandas Series?

Before we dive into computing the mean and standard deviation of a Pandas series, let’s first understand what a Pandas series is.

In Pandas, a series is a one-dimensional labeled array that can hold any data type. It is similar to a column in a spreadsheet or a database table. Each element in a series is associated with a label, which is called an index. The index can be a numeric or a string value.

To create a Pandas series, you can use the `pd.Series()`

function and pass a list or an array of values as the argument.

```
import pandas as pd
# Creating a Pandas series
data = [1, 2, 3, 4, 5]
s = pd.Series(data)
```

In the above example, we created a Pandas series `s`

with the values `[1, 2, 3, 4, 5]`

. The index of the series is automatically generated as `[0, 1, 2, 3, 4]`

.

## Computing the Mean of a Pandas Series

The mean of a Pandas series is the average of all the values in the series. It is computed by adding up all the values in the series and then dividing by the total number of values.

To compute the mean of a Pandas series, you can use the `mean()`

method.

```
# Computing the mean of a Pandas series
mean = s.mean()
print(mean)
```

Output:

```
3.0
```

In the above example, we computed the mean of the Pandas series `s`

. The output will be `3.0`

, which is the average of all the values in the series.

## Computing the Standard Deviation of a Pandas Series

The standard deviation of a Pandas series measures how much the values in the series deviate from the mean. It is computed by taking the square root of the sum of the squared differences between each value and the mean, divided by the total number of values.

To compute the standard deviation of a Pandas series, you can use the `std()`

method.

```
# Computing the standard deviation of a Pandas series
std = s.std()
print(std)
```

Output:

```
1.5811388300841898
```

In the above example, we computed the standard deviation of the Pandas series `s`

. The output will be `1.5811388300841898`

, which is the measure of how much the values in the series deviate from the mean.

## Conclusion

In this blog post, we explored the Pandas series mean and standard deviation. We learned that a Pandas series is a one-dimensional labeled array that can hold any data type, and it is similar to a column in a spreadsheet or a database table.

We also learned that the mean of a Pandas series is the average of all the values in the series, and it is computed by adding up all the values in the series and then dividing by the total number of values. The standard deviation of a Pandas series measures how much the values in the series deviate from the mean, and it is computed by taking the square root of the sum of the squared differences between each value and the mean, divided by the total number of values.

By following the step-by-step guide in this blog post, you can easily compute the mean and standard deviation of any Pandas series. These metrics are useful in many data analysis tasks, including outlier detection, data cleaning, and data visualization.

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