Standard deviation is a measure of how spread out the values in a set are. A low standard deviation indicates that the values are close to the mean, while a high standard deviation indicates that the values are spread out over a wider range.

Here’s how to calculate standard deviation in Pandas.

## How to calculate standard deviation in Pandas

In Pandas, you can calculate the standard deviation of a column using the `std()`

method. The `std()`

method takes a single argument, which is the name of the column.

For example, the following code calculates the standard deviation of the `my_column1`

column in the `my_df`

DataFrame:

import pandas as pd my_df = pd.DataFrame({"my_column1": [9, 2, 3, 5], "my_column2": [3, 7, 6, 4], "my_column3": [4, 8, 8, 8]}) print(f'The standard deviation of columns:\n{my_df.std()}')

This code outputs the following:

The standard deviation of columns: my_column1 3.095696 my_column2 1.825742 my_column3 2.000000 dtype: float64

By default, the `std()`

method calculates the sample standard deviation. This means that the standard deviation is calculated using the sum of squared deviations from the mean, divided by the number of values minus 1.

If you want to calculate the population standard deviation, you can set the `ddof`

parameter to 0. The `ddof`

parameter stands for “degrees of freedom”, and it is used to adjust the standard deviation calculation to account for the fact that we are estimating the population standard deviation from a sample.

For example, the following code calculates the population standard deviation of the `my_column1`

column in the `my_df`

DataFrame:

standard_deviation = my_df['column_name'].std(ddof=0)

The `std()`

method can also be used to calculate the standard deviation of multiple columns. To do this, you can pass a list of column names to the `std()`

method.

For example, the following code calculates the standard deviation of the `my_column1`

and `my_column2`

columns in the `my_df`

DataFrame:

statistics = my_df['column_name'].describe() standard_deviation = statistics['std']

The `std()`

method is a versatile tool that can be used to calculate the standard deviation of one or multiple columns in a Pandas DataFrame.

## Using the describe() Method

In addition to the `std()`

method, you can also use the `describe()`

method to calculate the standard deviation of a column. The `describe()`

method returns a DataFrame that contains summary statistics for the values in the column, including the mean, standard deviation, minimum, maximum, and quartiles.

For example, the following code calculates the standard deviation of the `my_column1`

column in the `my_df`

DataFrame using the `describe()`

method:

statistics = my_df["my_column1"].describe() standard_deviation = statistics["std"]

The `describe()`

method is a more versatile tool than the `std()`

method, as it can be used to calculate summary statistics for multiple columns. However, the `std()`

method is more efficient, as it only calculates the standard deviation of a single column.

For more details see the documentation of std function.

## Tips for Calculating Standard Deviation

Here are some additional tips for calculating the standard deviation in Pandas:

- If you are working with a large dataset, it may be more efficient to use the
`std()`

method instead of the`describe()`

method. - If you are only interested in the standard deviation of a single column, you can use the
`std()`

method. - If you are interested in the standard deviations of multiple columns, you can use the
`describe()`

method. - If you want to calculate the population standard deviation, you can set the
`ddof`

parameter to 0. The`ddof`

parameter stands for “degrees of freedom”, and it is used to adjust the standard deviation calculation to account for the fact that we are estimating the population standard deviation from a sample. - You can also use the
`numpy`

library to calculate the standard deviation. The`numpy.std()`

method is a more general-purpose method that can be used to calculate the standard deviation of any array of numbers.

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