[Tutor] Issues Inserting Graphical Overlay Using Matplotlib Patches

[Mark Lawrence]

On 28/09/2020 17:24, Stephen Malcolm wrote:
> Dear all,
> 
> I'm having some trouble inserting a graphical overlay in python.
> I've highlighted the code (in red) I'm using for this specific operation i.e. the stats and overlay part (and where I'm getting the error)::
> 
> #pandas used to read dataset and return the data
> #numpy and matplotlib to represent and visualize the data
> #sklearn to implement kmeans algorithm
> 
> import pandas as pd
> import numpy as np
> import matplotlib.pyplot as plt
> from sklearn.cluster import KMeans
> 
> #import the data
> data = pd.read_csv('banknotes.csv')
> 
> #extract values
> x=data['V1']
> y=data['V2']
> 
> #print range to determine normalization
> print ("X_max : ",x.max())
> print ("X_min : ",x.min())
> print ("Y_max : ",y.max())
> print ("Y_min : ",y.min())
> 
> # Import the sklearn function
> from sklearn.preprocessing import StandardScaler
> 
> # standardize the data
> scaler = StandardScaler()
> X_scaled = scaler.fit_transform(X)
> X_scaled
> 
> #statistical analyis using mean and standard deviation
> 
> plt.figure(figsize=(6, 6))
> plt.scatter(data.iloc[:, 0], data.iloc[:, 1])
> plt.xlabel('V1')
> plt.ylabel('V2')
> plt.title('Visualization of raw data');
> 
> import matplotlib.patches as patches
> 
> mean = np.mean(data, 0)
> std_dev = np.std(data, 0)
> 
> ellipse = patches.Ellipse ([mean[0], mean [1]], std_dev[0]*2, std_dev[1]*2, alpha=0.25)
> 
> graph.scatter(data[:,0])data[:,1])
> graph.scatter(mean[0], mean[1])
> graph.add_patch(ellipse)
> 
> Error states:
> 
> ile "<ipython-input-21-1a9b341de4cf>", line 16
>      graph.scatter(data[:,0])data[:,1])
>                                 ^
> SyntaxError: invalid syntax

graph.scatter(data[:,0], data[:,1]) as I can't at a quick glance see 
anything else.

> 
> Can someone please tell me where I'm going wrong?
> 
> Thank you in advance...
> 
> S
>

-- 
My fellow Pythonistas, ask not what our language can do for you, ask
what you can do for our language.

Mark Lawrence