Python Projects for Beginners a ten-Week Bootcamp Approach to Python Programming

Importance of Histogram Distribution

To see why histograms are so important with understanding central distribution, we’ll need to create some more fake data. We’ll then plot both datasets and see how they stack up:
CHapter 10 INtroduCtIoN to data aNalYsIs

# showing the importance of histogram's display central distribution florida = [ np.random.normal(loc=60, scale=15) for x in range(1000) ]
# assume numpy is imported
california = [ np.random.normal(loc=35, scale=5) for x in range(1000) ]
# chart 1
plt.hist(florida, bins=45, color="r", alpha=0.4) # alpha is
opacity, making it see through plt.show( ) # chart 2
plt.hist(california, bins=45, color="b", alpha=0.4) # alpha is opacity,
making it see through
plt.show( ) # chart 3
plt.hist(florida, bins=45, color="r", alpha=0.4) # alpha is
opacity, making it see through
plt.hist(california, bins=45, color="b", alpha=0.4) # alpha is opacity,
making it see through
plt.show( )

Go ahead and run the cell. We’re able to output three different histograms within this cell because of the three show methods being called. When you look at the first two histograms, they look identical. It’s tough to see, without looking further into the charts, that the data is completely different. Therefore, to view the data properly, we output the third histogram with both datasets overlapping as seen in Figure 10-7.
We’re now able to clearly see the difference in central distribution of each dataset. This is important when it comes to analyzing data. We set alpha to 0.4 because it allows us to set the opacity. The higher the number, the more solid the data becomes.
CHapter 10 INtroduCtIoN to data aNalYsIs

Figure 10-7. Histogram distribution plotting importance
Note When rendering several charts, matplotlib understands how to separate each plot by resetting the chart to empty after the show method is run, until then all information being plotted will be included in one chart.