Merge pull request #150 from datacarpentry/annajiat-patch-1

Update 13-matplotlib.md

Author: annajiat

_episodes/13-matplotlib.md

@@ -24,9 +24,9 @@ Although we are using Matplotlib in this episode, pandas can make use of several
 
 ## Importing matplotlib
 
-The matplotlib library can be imported using any of the import techniques we have seen. As `pandas` is generally imported with `import panas as pd`, you will find that `matplotlib` is most commonly imported with `import matplotlib as plt` where 'plt' is the alias.
+The matplotlib library can be imported using any of the import techniques we have seen. As `pandas` is generally imported with `import pandas as pd`, you will find that `matplotlib` is most commonly imported with `import matplotlib as plt` where 'plt' is the alias.
 
-In addition to importing the library, in a Jupyter notebook environment we need to tell Jupyter that when we produce a graph we want it to be display the graph in a cell in the notebook just like any other results. To do this we use the `%matplotlib inline` directive.  
+In addition to importing the library, in a Jupyter notebook environment we need to tell Jupyter that when we produce a graph, we want it to be display the graph in a cell in the notebook just like any other results. To do this we use the `%matplotlib inline` directive.  
 
 If you forget to do this, you will have to add `plt.show()` to see the graphs.
 
@@ -64,7 +64,7 @@ import numpy as np
 import pandas as pd
 
 np.random.seed(12345)            # set a seed value to ensure reproducibility of the plots
-s = pd.Series(np.random.rand(20) )
+s = pd.Series(np.random.rand(20))
 #s
 # plot the bar chart
 s.plot(kind='bar')
@@ -94,7 +94,7 @@ plt.show()
 > > The width of the bars can be changed with a parameter in the 'bar' function
 > >
 > > ~~~
-> > plt.bar(range ( len ( s )), s, width = 0.5)   # the default width is 0.8
+> > plt.bar(range(len(s)), s, width = 0.5)   # the default width is 0.8
 > > ~~~
 > > {: .language-python}
 > {: .solution}
@@ -134,13 +134,13 @@ plt.show()
 ~~~
 {: .language-python}
 
-In general most graphs can be broken down into a series of elements which, although typically related in some way, can all exist independently of each other. This allows us to create the graph in a rather piecemeal fashion.
+In general, most graphs can be broken down into a series of elements which, although typically related in some way, can all exist independently of each other. This allows us to create the graph in a rather piecemeal fashion.
 
 The labels (if any) on the x and y axis are independent of the data values being represented. The title and the legend are also independent objects within the overall graph.
 
 In matplotlib you create the graph by providing values for all of the individual components you choose to include. When you are ready, you call the `show` function.
 
-Using this same approach we can plot two sets of data on the same graph
+Using this same approach, we can plot two sets of data on the same graph.
 
 We will use a scatter plot to demonstrate some of the available features.
 
@@ -158,11 +158,11 @@ We will also add other common features like a title, a legend and labels on the
 
 ~~~
 # Generate some date for 2 sets of points.
-x1 = pd.Series(np.random.rand(20) - 0.5 )
-y1 = pd.Series(np.random.rand(20) - 0.5 )
+x1 = pd.Series(np.random.rand(20) - 0.5)
+y1 = pd.Series(np.random.rand(20) - 0.5)
 
-x2 = pd.Series(np.random.rand(20) + 0.5 )
-y2 = pd.Series(np.random.rand(20) + 0.5 )
+x2 = pd.Series(np.random.rand(20) + 0.5)
+y2 = pd.Series(np.random.rand(20) + 0.5)
 
 
 # Add some features
@@ -171,10 +171,10 @@ plt.ylabel('Range of y values')
 plt.xlabel('Range of x values')
 
 # plot the points in a scatter plot
-plt.scatter(x1,y1, c='red', label='Red Range' )  # 'c' parameter is the colour and 'label' is the text for the legend
-plt.scatter(x2,y2, c='blue', label='Blue Range')
+plt.scatter(x1, y1, c='red', label='Red Range')  # 'c' parameter is the colour and 'label' is the text for the legend
+plt.scatter(x2, y2, c='blue', label='Blue Range')
 
-plt.legend( loc=4 )  # the locations 1,2,3 and 4 are top-right, top-left, bottom-left and bottom-right
+plt.legend(loc=4)  # the locations 1,2,3 and 4 are top-right, top-left, bottom-left and bottom-right
 # Show the graph with the two sets of points
 plt.show()
 ~~~
@@ -185,36 +185,36 @@ The `c` or `color` parameter can be set to any color matplotlib recognises. Full
 
 > ## Exercise
 >
-> In the scatterplot the s parameter determines the size of the dots. s can be a simple numeric value, say s=100, which will produce dots all of the same size. However you can pass a list of values (or a pandas Series) to provide sizes for the individual dots. This approach is very common as it allows us to provide an extra variable worth of information on the graph.
+> In the scatterplot the s parameter determines the size of the dots. s can be a simple numeric value, say s=100, which will produce dots all of the same size. However, you can pass a list of values (or a pandas Series) to provide sizes for the individual dots. This approach is very common as it allows us to provide an extra variable worth of information on the graph.
 >
 > 1. Modify the code we used for the scatter plot to include a size value for each of the points in the series being plotted.
-> (The downside is that some of the smaller dots may be completely covered by the larger dots. To try and highlight when this has happened we can change the opacity of the dots.)
+> (The downside is that some of the smaller dots may be completely covered by the larger dots. To try and highlight when this has happened, we can change the opacity of the dots.)
 >
-> 2. Find out which parameter controls the opacity of the dots ( clue - it is not called opacity), add it to you code and set it > to a reasonable value .
+> 2. Find out which parameter controls the opacity of the dots (clue - it is not called opacity), add it to you code and set it > to a reasonable value.
 >
 > > ## Solution
 > >
 > > ~~~
 > > # Generate some data for 2 sets of points.
 > > # and additional data for the sizes - suitably scaled
-> > x1 = pd.Series(np.random.rand(20) - 0.5 )
-> > y1 = pd.Series(np.random.rand(20) - 0.5 )
-> > z1 = pd.Series(np.random.rand(20)*200 )
+> > x1 = pd.Series(np.random.rand(20) - 0.5)
+> > y1 = pd.Series(np.random.rand(20) - 0.5)
+> > z1 = pd.Series(np.random.rand(20) * 200)
 > >
-> > x2 = pd.Series(np.random.rand(20) + 0.5 )
-> > y2 = pd.Series(np.random.rand(20) + 0.5 )
-> > z2 = pd.Series(np.random.rand(20)*200 )
+> > x2 = pd.Series(np.random.rand(20) + 0.5)
+> > y2 = pd.Series(np.random.rand(20) + 0.5)
+> > z2 = pd.Series(np.random.rand(20) * 200)
 > >
 > > # Add some features
 > > plt.title('Scatter Plot')
 > > plt.ylabel('Range of y values')
 > > plt.xlabel('Range of x values')
 > >
 > > # plot the points in a scatter plot
-> > plt.scatter(x1,y1, c='red', label='Red Range', s=z1, alpha=0.5 )  # 's' parameter is the dot size
-> > plt.scatter(x2,y2, c='blue', label='Blue Range', s=z2, alpha=0.5) # 'alpha' is the opacity
+> > plt.scatter(x1, y1, c='red', label='Red Range', s=z1, alpha=0.5)  # 's' parameter is the dot size
+> > plt.scatter(x2, y2, c='blue', label='Blue Range', s=z2, alpha=0.5) # 'alpha' is the opacity
 > >
-> > plt.legend( loc=4 )
+> > plt.legend(loc=4)
 > > plt.show()
 > > ~~~
 > > {: .language-python}
@@ -271,18 +271,16 @@ plt.show()
 ~~~
 {: .language-python}
 
-will fail.
-
-However we can use the pandas plot method
+will fail. However, we can use the pandas plot method.
 
-~~~
+~~~,
 df = pd.DataFrame(np.random.normal(size=(100,5)), columns=list('ABCDE'))
 df.plot(kind = 'box', return_type='axes') # the return_type='axes' is only needed for forward compatibility
 ~~~
 {: .language-python}
 
 We can add a title to the above by adding the `title` parameter. However there are no parameters for adding the axis labels.
-To add labels we can use matplotlib directly.
+To add labels, we can use matplotlib directly.
 
 ~~~
 df = pd.DataFrame(np.random.normal(size=(100,5)), columns=list('ABCDE'))