# A Detailed Guide to the Python stdev() function

Filed Under: Python Modules Hey, folks! In continuation of our series on Python statistical functions, today we will be unveiling standard deviation using the Python stdev() method.

Standard deviation is a statistical entity that represents the variation in the data i.e. it depicts the deviation of data values from the center value (the mean of the data).

Usually, standard deviation is calculated using the below formula–

`Standard Deviation = (Variance)^1/2`

Now, let us start with the implementation and calculation of Standard Deviation using Python in-built function.

## Getting started with Python stdev() function

`Python statistics module` contains various in-built functions to perform the data analysis and other statistical functions. The `statistics.stdev() function` is used to calculate the standard deviation of the passed data values to the function as argument.

Syntax:

```statistics.stdev(data)
```

Example:

```import statistics
data = range(1,10)

res_std = statistics.stdev(data)
print(res_std)
```

In the above example, we have created data of numbers from 1-10 using the range() function. Further, we apply the stdev() function to evaluate the standard deviation of the data values.

Output:

```2.7386127875258306
```

## Python standard deviation with NumPy module

Python NumPy module converts the data elements into an array form to perform numeric manipulations on it.

Further, `numpy.std() function` can be used to calculate the standard deviation of all the data values present in the NumPy array.

Syntax:

```numpy.std(data)
```

We need to import the NumPy module into the Python environment to get access to the in-built functions of the same using the below code–

```import numpy
```

Example:

```import numpy as np
import pandas as pd
data = np.arange(1,30)
res_std = np.std(data)
print(res_std)
```

In the above example, we have generated an array of elements from 1-30 using `numpy.arange() function`. After which, we pass the array to the `numpy.std() function` to calculate the standard deviation of the array elements.

Output:

```8.366600265340756
```

## Python standard deviation with Pandas module

Python Pandas module converts the data values into a DataFrame and helps us analyse and work with huge datasets. The `pandas.DataFrame.std() `function is used to calculate the standard deviation of the data column values of a particular DataFrame.

Syntax:

```pandas.DataFrame.std()
```

Example 1:

```import numpy as np
import pandas as pd
data = np.arange(1,10)
df = pd.DataFrame(data)
res_std = df.std()
print(res_std)
```

In the above example, we have converted a NumPy array into a DataFrame and the applied the `DataFrame.std() function` to get the standard deviation of the data values.

Output:

```0    2.738613
dtype: float64
```

Example 2:

```import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

res_std = data['qsec'].std()
print(res_std)
```

In the above example, we have used a dataset and calculated the standard deviation of the data column ‘qsec’ using the DataFrame.std() function.

Input Dataset:

Output:

```1.7869432360968431
```

## Conclusion

Thus, in this article, we have understood the working of Python stdev() function along with NumPy and Pandas module.

## References

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