✅ Updated with bonus resources and guidesĭata Visualization in Python with Matplotlib and Pandas is a book designed to take absolute beginners to Pandas and Matplotlib, with basic Python knowledge, and allow them to build a strong foundation for advanced work with theses libraries - from simple plots to animated 3D plots with interactive buttons. ✅ Updated regularly for free (latest update in April 2021) You can explode as many of them as you'd like, with different values to highlight different categories. Setting this value to 1 would offset it by a lot, relative to the chart, so usually, you'll explode wedges by 0.1, 0.2, 0.3, and similar values. By default, all wedges have an explode value of 0, so they're all connected to the center. The explode argument accepts an array of values, from 0.1, where the values themselves define how further away the wedge is from the center. Assuming that we'd want to point out the fact that most people don't think it's unlikely, we can explode the wedge: import matplotlib.pyplot as pltĪx.pie(x, labels = labels, colors = colors, autopct= '%.0f%%', explode = explode) For example, in our survey, a really small percentage of the respondents feel like the advent of something in question is Very Unlikely. Sometimes, it's important to highlight certain entries. If we had omitted the surrounding %.% symbols, the strings wouldn't be formatted as percentages, but as literal values. Labels = Ĭolors = Īx.pie(x, labels = labels, colors = colors, autopct= '%.0f%%')īy setting autopct to %.0f%%, we've chosen to format the percentages with 0 decimal places (only whole numbers), and added a % sign at the end. It automatically sets the percentages in each wedge/slice, and accepts the standard Python string formatting notation: import matplotlib.pyplot as plt To add numerical percentages to each slice, we use the autopct argument. Though, it's oftentimes easier for us to both interpret a Pie Chart visually, and numerically. Looking at the Pie Chart we've made so far, it's clear that there are more Unsure and Likely respondents than other categories individually.
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