The median of a set of numbers represents the middle value if the numbers are arranged in sorted order. It is a measure of central tendency and is often preferred over the mean as it’s not much affected by the presence of outliers. In this tutorial, we will look at how to get the median of one or more columns in a pandas dataframe.

## How to calculate the median of pandas column values?

You can use the pandas `median()`

function or the pandas `quantile()`

function to get the median of column values in a pandas dataframe. The following is the syntax:

# median of single column df['Col'].median() # median of single column with quantile() df['Col'].quantile(0.5) # median of all numerical columns in dataframe df.median() # median of all numerical columns in dataframe with quantile() df.quantile(0.5)

Let’s create a sample dataframe that we will be using throughout this tutorial to demonstrate the usage of the methods and syntax mentioned.

import pandas as pd # create a dataframe df = pd.DataFrame({ 'sepal_legth': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0], 'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4], 'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5], 'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2], 'sepices': ['setosa']*8 }) # display the dataframe print(df)

Output:

sepal_legth sepal_width petal_length petal_width sepices 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa 5 5.4 3.9 1.7 0.4 setosa 6 4.6 3.4 1.4 0.3 setosa 7 5.0 3.4 1.5 0.2 setosa

The sample dataframe is taken form a section of the Iris dataset. This sample has petal and sepal dimensions of eight data points of the “Setosa” species.

### Median of a single column

First, let’s see how to get the median of a single dataframe column.

You can use the pandas series `median()`

function to get the median of individual columns (which essentially are pandas series). For example, let’s get the median of the “sepal_length” column in the above dataframe.

# median of sepal_length column print(df['sepal_length'].median())

Output:

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4.95

You see that we get the median of all values in the “sepal_length” column as the scaler value 4.95.

### Median of a single column using `quantile()`

Additionally, you can also use pandas `quantile()`

function which gives the nth percentile value. Median is the 50th percentile value. So, to get the median with the `quantile()`

function, pass 0.5 as the argument.

# median of sepal_length column using quantile() print(df['sepal_length'].quantile(0.5))

Output:

4.95

### Median of more than one column

Use the pandas dataframe `median()`

function to get the median values for all the numerical columns in the dataframe. For example, let’s get the median of all the numerical columns in the dataframe “df”

# mean of multiple columns print(df.median())

Output:

sepal_length 4.95 sepal_width 3.40 petal_length 1.40 petal_width 0.20 dtype: float64

We get the result as a pandas series.

### Median of more than one column using `quantile()`

Additionally, you can use the pandas dataframe `quantile()`

function with an argument of 0.5 to get the median of all the numerical columns in a dataframe. Let’s use this function on the dataframe “df” created above.

# mean of multiple columns using quantile() print(df.quantile(0.5))

Output:

sepal_length 4.95 sepal_width 3.40 petal_length 1.40 petal_width 0.20 Name: 0.5, dtype: float64

You can see that we get the median of all the numerical columns present in the dataframe.

Note that you can also use the pandas `describe()`

function to look at key statistics including the median values of the numerical columns in the dataframe.

# get dataframe statistics df.describe()

Output:

The median here is represented by the 50% value (that is, the value at the 50th percentile).

For more on the pandas dataframe median() function, refer to its documention.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

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Tutorials on getting statistics for pandas dataframe values –

- Pandas – Get Mean of one or more Columns
- Pandas – Get Standard Deviation of one or more Columns
- Pandas – Get Median of One or More Columns
- Get correlation between columns of Pandas DataFrame
- Cumulative Sum of Column in Pandas DataFrame
- Pandas – Count Missing Values in Each Column
- Get Rolling Window estimates in Pandas
- Get the number of rows in a Pandas DataFrame
- Pandas – Count of Unique Values in Each Column