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Visualization in Python with matplotlib

Simple Line ChartSimple Line Chart

That's good! Let's fill our empty plot with some data.

We can pass two iterable objects (such as, lists, arrays, tuples) as parameters for the .plot() function, and they will be used for building the plot. By default, all the points will be connected with the blue line in the respective order as they are stored in the objects.

For example, in the following chapters you will work with the CO2 emission level data. The data is stored as a Series object with years being indices, and CO2 levels being values. Before we start, let's inspect the data we will work with.

Let's visualize the CO2 emission level over years for Italy.

Here we used indices (the .index attribute) as values for the x-axis, and emission levels (the .values attribute) as values for the y-axis. Values for the x-axis were converted into integers (by applying the .astype() method since Python read them as strings initially).

Disclaimer: FREE DATA FROM WORLD BANK VIA GAPMINDER.ORG, CC-BY LICENSE.

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Seção 1. Capítulo 2
course content

Conteúdo do Curso

Visualization in Python with matplotlib

1. Basics: Line Charts

Creating an Empty PlotSimple Line ChartCreate a Simple Line ChartAdding One More LineAdd Data to the PlotAdding Legend to a PlotAdd LegendPlot Labels and TitleAdd Lables and TitlesChanging Line StyleCustomize Your Line ChartCreate a Complete Line Chart

2. Bar Charts

Simple Bar ChartCreate a Simple Bar ChartStacked BarsCreate a Stacked Bar ChartGrouped BarsCreate a Grouped Bar ChartHorizontal Bar ChartCreate a Horizontal Bar ChartCustomizing Bar ChartsCreate a Complete Bar Chart

3. Scatter Plots

The Simplest Scatter PlotCreate a Simple Scatter PlotSetting Plot ParametersSet Ticks and LimitsCustomizationCustomize Your Scatter PlotGrouping ObservationsGroup by RegionFurther GroupingGroup by Continuous VariableMaking Chart InformativeAdd Information to the Plot

Visualization in Python with matplotlib

Simple Line ChartSimple Line Chart

That's good! Let's fill our empty plot with some data.

We can pass two iterable objects (such as, lists, arrays, tuples) as parameters for the .plot() function, and they will be used for building the plot. By default, all the points will be connected with the blue line in the respective order as they are stored in the objects.

For example, in the following chapters you will work with the CO2 emission level data. The data is stored as a Series object with years being indices, and CO2 levels being values. Before we start, let's inspect the data we will work with.

Let's visualize the CO2 emission level over years for Italy.

Here we used indices (the .index attribute) as values for the x-axis, and emission levels (the .values attribute) as values for the y-axis. Values for the x-axis were converted into integers (by applying the .astype() method since Python read them as strings initially).

Disclaimer: FREE DATA FROM WORLD BANK VIA GAPMINDER.ORG, CC-BY LICENSE.

Tudo estava claro?

Seção 1. Capítulo 2
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