From the course: Data Analysis with Python and Pandas
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Chart styles
From the course: Data Analysis with Python and Pandas
Chart styles
- [Instructor] Alright so now let's take a look at chart styles. Customizing our charts in Pandas and Python is quite a bit of work, and we start to run into limitations when we're just using the Pandas Matplotlib API calling the plot method on our data frames. And if you want to be able to customize our charts to a very high level, we need to learn the Matplotlib in Seaborn libraries which could be a course in itself. So we're really focusing on giving you a very high level ability to format your charts but this will be a course that you will need to take if you are going to try to produce very high end charts in Python. But one thing that can be very helpful and help us get around the need to learn these libraries very deeply are pre-made style templates provided to us by the Matplotlib and Seaborn libraries. Once we set a style at the top of our notebook, it will be applied to all charts below. So here we're importing Seaborn as SNS, and then we're setting the style to dark grid. I…
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Contents
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The Matplotlib API and the plot() method9m 33s
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Challenge: Basic line chart49s
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Solution: Basic line chart3m 1s
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Chart titles3m 26s
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Chart colors5m 13s
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Line styles2m 1s
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Chart legends and gridlines3m 51s
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Chart styles4m 8s
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Challenge: Stylized line chart1m 11s
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Solution: Stylized line chart1m 21s
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Subplots and figure size5m 28s
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Challenge: Subplots1m 33s
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Solution: Subplots2m 59s
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Bar charts6m 13s
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Grouped and stacked bar charts5m 9s
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Challenge: Bar charts1m 11s
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Solution: Bar charts2m 19s
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Pie charts and scatterplots6m 52s
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Challenge: Scatterplots1m
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Solution: Scatterplots2m 10s
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Histograms3m 46s
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Challenge: Histograms33s
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Solution: Histograms1m 18s
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Saving plots and further exploration3m 41s
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Key takeaways2m 12s
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