# matlab-qualgraphs-notes ## MatPlotLib Qualitative Graphing Notes So, last time was all of the line graphs. Now for all those other silly kinds of graphs. The good news is most of the labeling and fiddling is the same across any kind of graph, but some labels are more necessary than others. Bar graphs without a labeled x-axis are particularly useless, as are pie charts without legends. ```python from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] sales = [91, 76, 56, 66, 52, 27] plt.bar(range(len(drinks)), sales) ax = plt.subplot(1, 1, 1) ax.set_xticks(range(len(drinks))) ax.set_xticklabels(drinks) plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_1_0.png) ## Bar Graphs with more than One Bar Making a two-bar plot is annoying enough that Excel will sound nice. Still, if it's a large dataset, this is still nicer, it just takes a little extra code. Remember that by default bars are 2u apart, so split bars should be 0.8, etc. The following code has a nice way of breaking down the needed widths so Python can do the really annoying parts of the design. Stacked bar graphs are a little easier, as seen in the second example. ```python from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] sales1 = [91, 76, 56, 66, 52, 27] sales2 = [65, 82, 36, 68, 38, 40] n1 = 1 # Relative Number of Dataset n2 = 2 t = 2 # Total Number of Datasets d = 6 # Number of Dimensions w = 0.8 # Width of the bars store1_x = [t*element + w*n1 for element in range(d)] store2_x = [t*element + w*n2 for element in range(d)] plt.bar(store1_x, sales1) plt.bar(store2_x, sales2) plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_3_0.png) ```python from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] sales1 = [91, 76, 56, 66, 52, 27] sales2 = [65, 82, 36, 68, 38, 40] legend_values = ['Location 1', 'Location 2'] ax_stack = plt.subplot(1, 1, 1) ax_stack.set_xticks(range(len(drinks))) ax_stack.set_xticklabels(drinks) plt.bar(range(len(drinks)), sales1) plt.bar(range(len(drinks)), sales2, bottom=sales1) plt.legend(legend_values) plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_4_0.png) ```python from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] ounces_of_milk = [6, 9, 4, 0, 9, 0] error = [0.6, 0.9, 0.4, 0, 0.9, 0] ax_err = plt.subplot(1, 1, 1) ax_err.set_xticks(range(len(drinks))) ax_err.set_xticklabels(drinks) plt.bar(range(len(ounces_of_milk)), ounces_of_milk, yerr=error, capsize=5) plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_5_0.png) ```python from matplotlib import pyplot as plt import numpy as np unit_topics = ['Limits', 'Derivatives', 'Integrals', 'Diff Eq', 'Applications'] As = [6, 3, 4, 3, 5] Bs = [8, 12, 8, 9, 10] Cs = [13, 12, 15, 13, 14] Ds = [2, 3, 3, 2, 1] Fs = [1, 0, 0, 3, 0] x = range(5) c_bottom = np.add(As, Bs) d_bottom = np.add(c_bottom, Cs) f_bottom = np.add(d_bottom, Ds) plt.figure(figsize=(10,8)) ax = plt.subplot() plt.bar(range(len(As)), As) plt.bar(range(len(Bs)), Bs, bottom=As) plt.bar(range(len(Cs)), Cs, bottom=c_bottom) plt.bar(range(len(Ds)), Ds, bottom=d_bottom) plt.bar(range(len(Fs)), Fs, bottom=f_bottom) ax.set_xticks(range(len(unit_topics))) ax.set_xticklabels(unit_topics) plt.title('Grade distribution') plt.xlabel('Unit') plt.ylabel('Number of Students') plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_6_0.png) ```python from matplotlib import pyplot as plt exam_scores1 = [62.58, 67.63, 81.37, 52.53, 62.98, 72.15, 59.05, 73.85, 97.24, 76.81, 89.34, 74.44, 68.52, 85.13, 90.75, 70.29, 75.62, 85.38, 77.82, 98.31, 79.08, 61.72, 71.33, 80.77, 80.31, 78.16, 61.15, 64.99, 72.67, 78.94] exam_scores2 = [72.38, 71.28, 79.24, 83.86, 84.42, 79.38, 75.51, 76.63, 81.48,78.81, 79.23,74.38,79.27,81.07,75.42,90.35,82.93,86.74,81.33,95.1,86.57, 83.66,85.58,81.87,92.14,72.15,91.64,74.21,89.04,76.54,81.9,96.5,80.05, 74.77,72.26,73.23,92.6,66.22,70.09,77.2] plt.figure(figsize=(10, 8)) plt.hist(exam_scores1, bins=12, normed=1, histtype='step', linewidth=2) plt.hist(exam_scores2, bins=12, normed=1, histtype='step', linewidth=2) legend_values = ['1st Yr Teaching', '2nd Yr Teaching'] plt.legend(legend_values) plt.title('Final Exam Score Distribution') plt.xlabel('Percentage') plt.ylabel('Frequency') plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_7_0.png) ### Line Graphs with Neat Features ```python from matplotlib import pyplot as plt months = range(12) month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] revenue = [16000, 14000, 17500, 19500, 21500, 21500, 22000, 23000, 20000, 19500, 18000, 16500] ax = plt.subplot(1, 1, 1) ax.set_xticks(months) ax.set_xticklabels(month_names) y_lower = [i*0.9 for i in revenue] y_upper = [i*1.1 for i in revenue] plt.plot(months, revenue) plt.fill_between(months, y_lower, y_upper, alpha=0.2) plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_9_0.png) ### Pie Charts Pie charts are terrible, because they give no historical context. But, people still like them, so here's how to make one. Note, always use the `plt.axis('equal')` statement, as otherwise the pie chart will default to weirdly tilted. No one likes tilted pie charts. ```python from matplotlib import pyplot as plt payment_method_names = ["Card Swipe", "Cash", "Apple Pay", "Other"] payment_method_freqs = [270, 77, 32, 11] plt.pie(payment_method_freqs, autopct='%0.1f%%') plt.axis('equal') plt.legend(payment_method_names) plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_11_0.png) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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