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In statistics, **multidimensional scaling** is a way to visualize the similarity of observations in a dataset in an abstract cartesian space (usually a 2-D space).

The easiest way to perform multidimensional scaling in Python is by using the **MDS()** function from the **sklearn.manifold** sub-module.

The following example shows how to use this function in practice.

**Example: Multidimensional Scaling in Python**

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd #create DataFrane df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K'], 'points': [4, 4, 6, 7, 8, 14, 16, 19, 25, 25, 28], 'assists': [3, 2, 2, 5, 4, 8, 7, 6, 8, 10, 11], 'blocks': [7, 3, 6, 7, 5, 8, 8, 4, 2, 2, 1], 'rebounds': [4, 5, 5, 6, 5, 8, 10, 4, 3, 2, 2]}) #set player column as index column df = df.set_index('player') #view Dataframe print(df) points assists blocks rebounds player A 4 3 7 4 B 4 2 3 5 C 6 2 6 5 D 7 5 7 6 E 8 4 5 5 F 14 8 8 8 G 16 7 8 10 H 19 6 4 4 I 25 8 2 3 J 25 10 2 2 K 28 11 1 2

We can use the following code to perform multidimensional scaling with the **MDS()** function from the **sklearn.manifold** module:

from sklearn.manifold import MDS #perform multi-dimensional scaling mds = MDS(random_state=0) scaled_df = mds.fit_transform(df) #view results of multi-dimensional scaling print(scaled_df) [[ 7.43654469 8.10247222] [ 4.13193821 10.27360901] [ 5.20534681 7.46919526] [ 6.22323046 4.45148627] [ 3.74110999 5.25591459] [ 3.69073384 -2.88017811] [ 3.89092087 -5.19100988] [ -3.68593169 -3.0821144 ] [ -9.13631889 -6.81016012] [ -8.97898385 -8.50414387] [-12.51859044 -9.08507097]]

Each row from the original DataFrame has been reduced to an (x, y) coordinate.

We can use the following code to visualize these coordinates in a 2-D space:

import matplotlib.pyplot as plt #create scatterplot plt.scatter(scaled_df[:,0], scaled_df[:,1]) #add axis labels plt.xlabel('Coordinate 1') plt.ylabel('Coordinate 2') #add lables to each point for i, txt in enumerate(df.index): plt.annotate(txt, (scaled_df[:,0][i]+.3, scaled_df[:,1][i])) #display scatterplot plt.show()

Players from the original DataFrame who have similar values across the original four columns (points, assists, blocks, and rebounds) are located close to each other in the plot.

For example, players **F** and **G** are located close to each other. Here are their values from the original DataFrame:

#select rows with index labels 'F' and 'G' df.loc[['F', 'G']] points assists blocks rebounds player F 14 8 8 8 G 16 7 8 10

Their values for points, assists, blocks, and rebounds are all fairly similar, which explains why theyâ€™re located so close together in the 2-D plot.

By contrast, consider players **B** and **K** who are located far apart in the plot.

If we refer to their values in the original DataFrame, we can see that theyâ€™re quite different:

#select rows with index labels 'B' and 'K' df.loc[['B', 'K']] points assists blocks rebounds player B 4 2 3 5 K 28 11 1 2

Thus, the 2-D plot is a nice way to visualize how similar each players are across all of the variables in the DataFframe.

Players who have similar stats are grouped close together while players who have very different stats are located far apart from each other in the plot.

**Additional Resources**

The following tutorials explain how to perform other common tasks in Python:

How to Normalize Data in Python

How to Remove Outliers in Python

How to Test for Normality in Python