Blog - bar#25
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story645
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Aug 20, 2018
| ## Matplotlib | ||
| This is a little more complicated in Matplotlib. Since Matplotlib is procedural, we have to manually tell Matplotlib to stack the bars. Also notice that we are calling a new function now (`ax.bar()`) to get a bar plot. | ||
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| To stack the bars, we have to create new subset dataframes and then plot each one separately (specifying that bottom of one plot should be the top of another `bottom=a_scores`). One way to do this is to subset via indexing (option 1). Another way to do this is to use the `df.groupby()` function (option 2). |
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Worried that this phrasing may be confusing since the bottom=a uses the top of a as the bottom of b. Maybe just state it explicitly? In this example, we first plot group a. Then we plot group b, using the bottom=a kwarg to plot b starting from the top of each a bar (and yes that can be phrased more cleanly too)
tacaswell
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Aug 20, 2018
| fig, ax = plt.subplots() | ||
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| (_, a), (_, b) = df.groupby('variable') | ||
| ax.bar(a['group'], a['scores'], label='a') |
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This might be clearer as
bottom = np.zeros(len(a['group'])
for label, scores in df.groupby('variable');
ax.bar(scoren['group'], scores['scores'], bottom=bottom, label=label)
bottom += scores['scores']
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This is the bar chart section from the blog post Nabarun and I were working on.