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

Types of Charts

Knowing which chart to use is important in presenting your data properly. We’ll go over several charts today; however, these are some of the common charts you’ll want to know:

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  Line Chart: Exploring data over time
  
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  Bar Chart: Comparing categories of data and tracks changes over time
  
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  Pie Chart: Explores parts of a whole, that is, fractions
  
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  Scatter Plot: Like line charts, tracks correlations between two categories
  
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  Histogram: Unrelated from bar charts, shows distribution of variables
  
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  Candlestick Chart: Used a lot in financial sector, that is, can compare a stock over a period
  
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  Box Chart: Looks identical to candlestick charts, and compares minimum, 1st, median, 3rd quartiles, and max values
  

Depending on what you need to accomplish in conceptualizing your data, you will be able to choose a specific type of chart to portray your data.

Installing Matplotlib

To install matplotlib, make sure your virtual environment is activated first, then write the following command into the terminal:

$ pip install matplotlib

After running the command, it should install a few packages that matplotlib requires. If you’d like to check and make sure you downloaded the proper library, just write out the list command.
CHapter 10 INtroduCtIoN to data aNalYsIs

Importing Matplotlib

To follow along with the rest of this lesson, let’s open and continue from our previous notebook file “Week_10” and simply add a markdown cell at the bottom that says, “Matplotlib.”
Like Pandas, matplotlib has an industry standard name when you import the library:

# importing the matplotlib library from matplotlib import pyplot as plt # industry standard name of plt when importing

Go ahead and run the cell. We import pyplot as plt so that we can reference the many charts that matplotlib has to offer.