set ( title = "Scatter Plot", xlabel = "Month", ylabel = "Precipitation \n (inches)" ) plt. scatter ( months, boulder_monthly_precip, c = boulder_monthly_precip, cmap = 'YlGnBu' ) # Set plot title and axes labelsĪx2. set ( title = "Bar Plot", xlabel = "Month", ylabel = "Precipitation \n (inches)" ) # Define x and y axesĪx2. bar ( months, boulder_monthly_precip, color = 'cyan', edgecolor = 'darkblue' ) # Set plot title and axes labelsĪx1. suptitle ( "Average Monthly Precipitation for Boulder, CO", fontsize = 16 ) # Define x and y axesĪx1. In the example below, ax1.bar creates a customized bar plot in the first plot, and ax2.scatter creates a customized scatter in the second plot.įig, ( ax1, ax2 ) = plt. Once you have your fig and two axis objects defined, you can add data to each axis and define the plot with unique characteristics.
Recall that matplotlib’s object oriented approach makes it easy to include more than one plot in a figure by creating additional axis objects:įig, (ax1, ax2) = plt.subplots(num_rows, num_columns) show () You can adjust the colors of the points in a scatter plot using color maps (cmap argument), which allows you to specify a range of colors that will be applied to the data points depending on their value. set ( title = "Average Monthly Precipitation \n Boulder, CO", xlabel = "Month", ylabel = "Precipitation \n (inches)" ) plt. scatter ( months, boulder_monthly_precip, c = boulder_monthly_precip, cmap = 'YlGnBu' ) # Set plot title and axes labelsĪx. The example below uses the YlGnBu colormap, in which lower values are filled in with yellow to green shades, while higher values are filled in with increasingly darker shades of blue.ĭata Tip: To see a list of color map options, visit the matplotlib documentation on colormaps.Īx. boulder_monthly_precip), while cmap allows you to specify the color map to use for the sequence. The c argument allows you to specify the sequence of values that will be color-mapped (e.g. When using scatter plots, you can also assign each point a color based upon its data value using the c and cmap arguments. show () You can adjust the bar fill and edge colors of a bar plot using the arguments color and edgecolor.
bar ( months, boulder_monthly_precip, color = 'cyan', edgecolor = 'darkblue' ) # Set plot title and axes labelsĪx. Visit the Matplotlib documentation for a list of marker types.Īx. You can change the point marker type in your line or scatter plot using the argument marker = and setting it equal to the symbol that you want to use to identify the points in the plot.įor example, "," will display the point markers as a pixel or box, and “o” will display point markers as a circle. show () You can use plt.setp(ax.get_xticklabels(), rotation 45) to rotate the tick marks along the x axis by 45 degrees. plot ( months, boulder_monthly_precip ) # Set plot title and axes labelsĪx.
subplots ( figsize = ( 10, 6 )) # Define x and y axesĪx. It serves as a unique, practical guide to Data Visualization, in a plethora of tools you might use in your career.Fig, ax = plt. More specifically, over the span of 11 chapters this book covers 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy.
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It serves as an in-depth, guide that'll teach you everything you need to know about Pandas and Matplotlib, including how to construct plot types that aren't built into the library itself.ĭata Visualization in Python, a book for beginner to intermediate Python developers, guides you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair. ✅ 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.
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