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1. #Annotate text matplotlib how to#
2. #Annotate text matplotlib code#
3. #Annotate text matplotlib series#
4. #Annotate text matplotlib free#

Now we can obviously improve the readability of the plot by calling the grid function on the axes and setting it to True. Finally, we plot this with plt.show and we get this output. So in our case is the evolution of the total fertility rate in China. We then specify the xticks_grid, which is equal to six, the color is equal to red, and the plot label is equal to the string China. And the xlabel as Year and ylabel as TFR. In particular, we require x to be the column year, the DataFrame china, and y instead is df_china in position fertility. We now call the custom method and we assign it to the object named axes. So let's create a figure and axes object with the plt.subplots method and we also specify the figsize as 12 and 10. If passed, markers are shown in the plot. And marker argument, which is by default equal to none. If passed, it's the label we wish to identify the series. Plot_label, by default, is equal to none.

Color is the color we wish to apply to our series. If it's greater than zero, a custom grid is produced. Xticks_grid, which by default is set to zero. So the axes object, the xlabel and ylabel.

#Annotate text matplotlib series#

So x expresses the series object we wish to plot on the x-axis and y on the y-axis.

#Annotate text matplotlib code#

This method will wrap all that we need to plot the desired output, so that we do not need to write redundant code again later. For simplicity, I will paste here the method I have created for you called getting series plot. The TFR or total fertility rate is used in demography to indicate the average number of children that would be born to a woman over her lifetime if she were to experience the exact age-specific fertility rates throughout her lifetime.īefore diving into the technicalities, let's create a wrapper that draws the artist into the axis object. In our dataset, it is expressed by the column fertility. So we're going to use the time series related to births per woman in China, also called total fertility rate. We read the gapminder dataset, and we retain only the records for the country China as shown here. So now let us import pandas as pd and matplotlib.pyplot as plt. Annotations are usually referred to as small pieces of text that are used to draw attention to a particular part of a plot. More specifically, we are going to add annotations to the axes objects. In this lecture, we are going to explore a very important technique that is used to boost the readability of your plot. The data used in this course can be found in the following GitHub repository: Knowledge of Python's Pandas library would also be beneficial and you might want to take our courses Working with Pandas and Data Wrangling with Pandas before embarking on this Matplotlib course. To get the most out of this course, you should already be familiar with using Python, for which you can take our Introduction to Python learning path.

#Annotate text matplotlib free#

If you have any feedback related to this course, feel free to contact us at.

#Annotate text matplotlib how to#

In this course, we will explore the main functionalities of Matplotlib: we will look at how to customize Matplotlib objects, how to use various plotting techniques, and finally, we will focus on how to communicate results.

This course will guide you through all the possible techniques that are used to visualize data using the Matplotlib Python library.