Created
November 29, 2012 13:39
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Pareto Chart with Matplotlib
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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| def combined_label(perc, tot): | |
| """ | |
| Format a label to include by Euros and %. | |
| """ | |
| return "{0:,.0f}k EUR, {1:.0f}%".format(perc * tot / 1000, perc * 100) | |
| def cost_cum(data, focus, subject): | |
| """ | |
| Accumulate the stats. | |
| - data is a DataFrame, | |
| - focus is the colum to group by | |
| - subject is the column to aggregate. | |
| """ | |
| # Setup data frame | |
| parts = data[[focus, 'cost']].groupby(focus).sum().sort(subject, ascending=False) | |
| parts['percent'] = parts['cost'] / parts.cost.sum() | |
| parts['cum_percent'] = parts['percent'].cumsum() | |
| return parts | |
| def cost_pareto(data, focus_name, limit_percent = 0.75): | |
| # Filter and organize the data frame | |
| top_parts = data[data['cum_percent'] < limit_percent] | |
| top_parts.set_index(top_parts['percent']) | |
| # Draw the plots | |
| fig = plt.figure(figsize=(10,7)) | |
| fig.subplots_adjust(bottom=0.4, left=0.15) | |
| ax = fig.add_subplot(1,1,1) | |
| top_parts['cum_percent'].plot(ax=ax, color="k", drawstyle="steps-post") | |
| top_parts['percent'].plot(ax=ax, kind="bar", color="k", alpha=0.5) | |
| ax.set_ylim(bottom=0, top=1) | |
| tick_nums = [x/float(100) for x in range(0,101,20)] | |
| ax.set_yticks(tick_nums) | |
| tot_cost = top_parts['cost'].sum() | |
| ax.set_yticklabels([combined_label(x, tot_cost) for x in tick_nums]) | |
| ax.set_title("Top %s%% of Cost Split By %s" % (int(limit_percent * 100), focus)) | |
| ax.set_xlabel("") | |
| return ax | |
| accumulated = cost_cum(data, 'Part', 'Cost') | |
| chart = cost_pareto(accumulated, 'Part', 0.9) |
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Good work!
For what it's worth, here's my take at making a production-ready code to do this task. Take a look at https://github.com/tisimst/paretochart for the file and a nice example of its usage and customizability. Cheers!
Abraham (tisimst)