Axes Cookbook

• Drawing plots in logarithmic scale in Matplotlib
  To draw plots in logarithmic scale in Matplotlib, use the following semilogy(~) method.
• Drawing the axis line in Matplotlib
  To draw the axis line in Matplotlib, use the axhline(~) and axvline(~) methods.
• Inverting y-axis in Matplotlib
  To invert the y-axis in Matplotlib use the invert_yaxis() method.
• Plotting two lines with different y axis in Matplotlib
  We can plot two lines with different y axis in Matplotlib using the twinx() method.
• Removing axis in Matplotlib
  To completely remove an axis in Matplotlib, set plt.axis("off").
• Setting only the lower or upper limit in Matplotlib
  To set only the lower or upper limit for plt.xlim(~), set either the left or right parameter. For plt.ylim(~), set either the bottom or top parameter.
• Setting x-axis limit in Matplotlib
  It is possible to set a limit on the x-axis (i.e. the domain) in Matplotlib using the xlim(~) method.
• Setting y-axis limit in Matplotlib
  It is possible to set a limit on the y-axis (i.e. the range) in Matplotlib using the y-lim(~) method.

Colors Cookbook

• Adding multiple plots with different colors in Matplotlib
  By default, Matplotlib actually changes the color of the additional plots you add.
• Changing background color of figure in Matplotlib
  It is possible to change the background color of a figure in Matplotlib using set_facecolor(~).
• Changing color based on value in Matplotlib
  To change the color of a data point based on its value in Matplotlib, we can leverage the cmap parameter within the plt.scatter(~) method.
• Changing line color of a plot in Matplotlib
  It is possible to change the line color of a plot in Matplotlib by passing the color argument to plt.plot(~).
• Changing the background color of a plot in Matplotlib
  To change the background color of a plot in Matplotlib use the set_facecolor(~) method.
• Changing the color of axes in Matplotlib
  It is possible to change the color of axes in Matplotlib using the set_color(~) method.
• Changing the color of tick labels in Matplotlib
  To change the color of tick labels in Matplotlib use the tick_params(~) method.
• Drawing semi-transparent plots in Matplotlib
  To draw semi-transparent plots in Matplotlib, set the alpha parameter in the plot(~) method.
• Filling in the area above a curve in Matplotlib
  To fill in the area above a curve in Matplotlib, use the plt.fill_between(~) method.
• Filling in the area underneath a curve in Matplotlib
  To fill the area underneath a curve in Matplotlib, use the plt.fill_between(~) method.
• Getting specific color value from a color map in Matplotlib
  To get a specific color value from a color map in Matplotlib use the cm.get_cmap(~) method.
• Inverting color maps in Matplotlib
  To invert a color map in Matplotlib, attach the _r suffix to the color map.
• Making the background figure transparent in Matplotlib
  We can make the background figure transparent in Matplotlib using the set_alpha(~) method.
• Shading an area in Matplotlib
  To shade an area in Matplotlib, use the fill(~) method.

Customizing Plots Cookbook

• Changing line width in Matplotlib
  We can change the line width in Matplotlib by specifying the keyword-argument linewidth.
• Changing size of figures in Matplotlib
  We can change the size of figures in Matplotlib using the figsize argument or using the set_size_inches method.
• Changing the plot style in Matplotlib
  To change the plot style in Matplotlib use the the plt.style.use(~) method.
• Removing grid lines in Matplotlib
  To remove grid lines in Matplotlib, use the plt.grid(~) method and pass False argument.
• Setting the linestyle in Matplotlib
  We can specify the linestyle of a plot using the linestyle parameter of the plot(~) method.
• Showing grid lines in Matplotlib
  It is possible to show grid lines in Matplotlib using the plot.grid(~) method.

Graphs Cookbook

• Drawing a bar chart in Matplotlib
  To draw bar charts in Matplotlib we can use the ax.bar(~) method.
• Drawing a box plot in Matplotlib
  To draw a box plot in Matplotlib use the boxplot(~) method.
• Drawing a function in Matplotlib
  Here we provide a helper function to help to draw functions in Matplotlib.
• Drawing a histogram in Matplotlib
  We can draw a histogram in Matplotlib using the plt.hist(~) method.
• Drawing a horizontal line in Matplotlib
  To draw a horizontal line in Matplotlib, use the axhline(~) function.
• Drawing a line plot in Matplotlib
  To draw a basic line plot in Matplotlib, use the plt.plot(~) function.
• Drawing a normal curve in Matplotlib
  To draw the normal curve, we need to use the norm.pdf(~) method of the scipy.stats library.
• Drawing a scatterplot in Matplotlib
  To draw a basic 2D scatter plot in Matplotlib, we can use the scatter(~) function.
• Drawing a single point in Matplotlib
  To draw a single point in Matplotlib, use the plt.scatter(~) function.
• Drawing a stacked bar chart in Matplotlib
  To draw stacked bar charts in Matplotlib we can use the ax.bar(~) method.
• Drawing a vertical line in Matplotlib
  To draw a vertical line in Matplotlib, use the axvline(~) function.
• Drawing arrows in Matplotlib
  In Matplotlib, use the arrow(~) method to draw arrows.
• Drawing circles in Matplotlib
  To draw circles in Matplotlib, create a Circle object and add it into the axis using the add_artist() method.
• Drawing empty circles in Matplotlib
  To draw empty circles in Matplotlib we can specify facecolors="none" within the plt.scatter(~) method.
• Drawing error bars in Matplotlib
  To draw error bars in Matplotlib, use the plt.errorbar(~) method.
• Drawing horizontal bar plots in Matplotlib
  In Matplotlib, we can draw a horizontal bar plot using the barh(~) method
• Drawing multiple histograms in one plot in Matplotlib
  To draw multiple histograms in one plot in Matplotlib using the plt.hist(~) method.
• Normalizing a histogram in Matplotlib
  We can normalize a histogram in Matplotlib using the density keyword argument and setting it to True. By normalizing a histogram, the sum of the bar area equals 1.
• Plotting scatter plot with category in Matplotlib
  To plot a scatter plot with categories or classes in Matplotlib, supply the c and cmap arguments to plt.scatter(~).

Legend Cookbook

• Adding a legend to a plot in Matplotlib
  To add a legend to a plot in Matplotlib pass through the label parameter.
• Adding a single legend to subplots in Matplotlib
  We can use the get_legend_handles_labels() method on the last axis only to add a single legend to subplots in Matplotlib.
• Placing the legend outside the plot in Matplotlib
  We can place the legend outside the plot in Matplotlib using the bbox_to_anchor argument of the legend method.
• Removing the legend in Matplotlib
  We can remove the legend in Matplotlib using the remove() method.
• Specifying exact location of the legend in Matplotlib
  To specify the exact location of the legend in Matplotlib, set the bbox_to_anchor and loc parameters of the legend(~) method.

Miscellaneous Cookbook

• Displaying an image in Matplotlib
  We can import an image from a file in Matplotlib using the imread method.
• Opening a plot in a window in Jupyter Notebook for Matplotlib
  In Jupyter Notebook, adding the following line of code "%matplotlib qt" will open plots in a new window.
• Resetting rcparams to default settings in Matplotlib
  To reset rcparams to default settings in Matplotlib we can use plt.rcParams.update(mpl.rcParamsDefault).
• Showing a plot inline in Jupyter Notebook for Matplotlib
  Instead of showing the plot in a new window, we can display the plot directly in Jupyter Notebook by executing the following line of code: %matplotlib inline.

Saving Cookbook

• Saving a graph in Matplotlib
  It is possible to save a graph as png, svg or pdf format in Matplotlib.
• Saving a graph as a svg in Matplotlib
  To save your graph as a svg in Matplotlib pass the format="svg" argument to the savefig method.

Subplots Cookbook

• Adding a main title to subplots in Matplotlib
  We can add a main title to subplots in Matplotlib using the suptitle(~) method.
• Adding title to subplots in Matplotlib
  To add a title to the subplots in Matplotlib, use the title.set_text(~) method.
• Creating subplots in Matplotlib
  We can create subplots in Matplotlib by calling plt.subplots(~).
• Fixing spacing between subplots in Matplotlib
  We can fix the spacing between subplots in Matplotlib using the subplots_adjust(~) method.
• Making plots side by side in Matplotlib
  To draw plots side by side in Matplotlib, use the subplots(~) method.
• Making subplots share the same axis in Matplotlib
  When creating subplots in Matplotlib, we can make the subplots share the same x axis or y axis by passing sharex=True or sharey=True to the plt.subplots(~) call.

Text, Label and Annotations Cookbook

• Adding a title to a plot in Matplotlib
  It is possible to add a title to a plot in Matplotlib using plt.title(~).
• Adding arrows to an annotation in Matplotlib
  We can add arrows to an annotation in Matplotlib by passing the arrowprops argument to the ax.annotate(~) method.
• Adding axis labels to a plot in Matplotlib
  It is possible to add axis labels to a plot in Matplotlib using plt.xlabel(~) and plt.ylabel(~).
• Adding markers to a plot in Matplotlib
  We can add markers to a plot in Matplotlib by passing the marker argument when calling the plot(~) method.
• Annotating data points in Matplotlib
  To annotate data points in Matplotlib, use the annotate(~) method.
• Applying an offset to annotations in Matplotlib
  To apply an offset to annotations in Matplotlib we leverage the xytext and textcoords parameters within the plt.annotate(~) method.
• Changing the default font size in Matplotlib
  We can change the default font size in Matplotlib using plt.rcParams.update(~) method.
• Changing the marker size of scatter plots in Matplotlib
  We can specify the s argument in plt.scatter(~) to change the size of all markers in Matplotlib.
• Changing the number of ticks in Matplotlib
  We can change the number of ticks in Matplotlib by specifying the nbins argument of locator_params method.
• Changing the tick size in Matplotlib
  To change the tick size in Matplotlib, use the tick_params(~) method.
• Removing certain ticks in Matplotlib
  We can remove certain ticks in Matplotlib by using the set_visible(~) method.
• Removing column name label from pie charts in Matplotlib
  To remove the column name label from pie charts in Matplotlib we can set the y-axis label to not be visible.
• Removing default axis labels in Matplotlib
  To remove default axis labels in Matplotlib, specify xticks and yticks with empty values.
• Rotating axis labels in Matplotlib
  To rotate axis labels in Matplotlib, use the xticks(~) and the yticks(~) method.
• Rotating custom tick labels in Matplotlib
  To rotate custom tick labels in Matplotlib we can leverage the set_xticklabels(~) method.
• Specifying custom tick labels in Matplotlib
  We can specify custom tick labels in Matplotlib using the xticks() method.
• Writing mathematical expressions in Matplotlib
  To write mathematical expressions in Matplotlib, precede the expression with an r and then place the mathematical expression within a pair of dollar signs ($).