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![PythonForDataScience Cheat Sheet Matplotlib Learn Python Interactively at www.DataCamp.com Matplotlib DataCamp Learn Python for Data Science Interactively Prepare The Data Also see Lists & NumPy Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. 1 >>> import numpy as np >>> x = np.linspace(0, 10, 100) >>> y = np.cos(x) >>> z = np.sin(x) Show Plot >>> plt.show() Save Plot Save figures >>> plt.savefig('foo.png') Save transparent figures >>> plt.savefig('foo.png', transparent=True) 6 5 >>> fig = plt.figure() >>> fig2 = plt.figure(figsize=plt.figaspect(2.0)) Create Plot2 Plot Anatomy & Workflow All plotting is done with respect to an Axes. In most cases, a subplot will fit your needs. A subplot is an axes on a grid system. >>> fig.add_axes() >>> ax1 = fig.add_subplot(221) # row-col-num >>> ax3 = fig.add_subplot(212) >>> fig3, axes = plt.subplots(nrows=2,ncols=2) >>> fig4, axes2 = plt.subplots(ncols=3) Customize Plot Colors, Color Bars & Color Maps Markers Linestyles Mathtext Text & Annotations Limits, Legends & Layouts The basic steps to creating plots with matplotlib are: 1Prepare data 2Create plot 3Plot 4 Customize plot 5Save plot 6Show plot >>> import matplotlib.pyplot as plt >>> x = [1,2,3,4] >>> y = [10,20,25,30] >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.plot(x, y, color='lightblue', linewidth=3) >>> ax.scatter([2,4,6], [5,15,25], color='darkgreen', marker='^') >>> ax.set_xlim(1, 6.5) >>> plt.savefig('foo.png') >>> plt.show() Step 3, 4 Step 2 Step 1 Step 3 Step 6 Plot Anatomy Workflow 4 Limits & Autoscaling >>> ax.margins(x=0.0,y=0.1) Add padding to a plot >>> ax.axis('equal') Set the aspect ratio of the plot to 1 >>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) Set limits for x-and y-axis >>> ax.set_xlim(0,10.5) Set limits for x-axis Legends >>> ax.set(title='An Example Axes', Set a title and x-and y-axis labels ylabel='Y-Axis', xlabel='X-Axis') >>> ax.legend(loc='best') No overlapping plot elements Ticks >>> ax.xaxis.set(ticks=range(1,5), Manually set x-ticks ticklabels=[3,100,-12,"foo"]) >>> ax.tick_params(axis='y', Make y-ticks longer and go in and out direction='inout', length=10) Subplot Spacing >>> fig3.subplots_adjust(wspace=0.5, Adjust the spacing between subplots hspace=0.3, left=0.125, right=0.9, top=0.9, bottom=0.1) >>> fig.tight_layout() Fit subplot(s) in to the figure area Axis Spines >>> ax1.spines['top'].set_visible(False) Make the top axis line for a plot invisible >>> ax1.spines['bottom'].set_position(('outward',10))Move the bottom axis line outward Figure Axes >>> data = 2 * np.random.random((10, 10)) >>> data2 = 3 * np.random.random((10, 10)) >>> Y, X = np.mgrid[-3:3:100j, -3:3:100j] >>> U = -1 - X**2 + Y >>> V = 1 + X - Y**2 >>> from matplotlib.cbook import get_sample_data >>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy')) >>> lines = ax.plot(x,y) Draw points with lines or markers connecting them >>> ax.scatter(x,y) Draw unconnected points, scaled or colored >>> axes[0,0].bar([1,2,3],[3,4,5]) Plot vertical rectangles (constant width) >>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) Plot horiontal rectangles (constant height) >>> axes[1,1].axhline(0.45) Draw a horizontal line across axes >>> axes[0,1].axvline(0.65) Draw a vertical line across axes >>> ax.fill(x,y,color='blue') Draw filled polygons >>> ax.fill_between(x,y,color='yellow') Fill between y-values and 0 Plotting Routines3 1D Data >>> fig, ax = plt.subplots() >>> im = ax.imshow(img, Colormapped or RGB arrays cmap='gist_earth', interpolation='nearest', vmin=-2, vmax=2) 2D Data or Images Vector Fields >>> axes[0,1].arrow(0,0,0.5,0.5) Add an arrow to the axes >>> axes[1,1].quiver(y,z) Plot a 2D field of arrows >>> axes[0,1].streamplot(X,Y,U,V) Plot 2D vector fields Data Distributions >>> ax1.hist(y) Plot a histogram >>> ax3.boxplot(y) Make a box and whisker plot >>> ax3.violinplot(z) Make a violin plot >>> axes2[0].pcolor(data2) Pseudocolor plot of 2D array >>> axes2[0].pcolormesh(data) Pseudocolor plot of 2D array >>> CS = plt.contour(Y,X,U) Plot contours >>> axes2[2].contourf(data1) Plot filled contours >>> axes2[2]= ax.clabel(CS) Label a contour plot Figure Axes/Subplot Y-axis X-axis 1D Data 2D Data or Images >>> plt.plot(x, x, x, x**2, x, x**3) >>> ax.plot(x, y, alpha = 0.4) >>> ax.plot(x, y, c='k') >>> fig.colorbar(im, orientation='horizontal') >>> im = ax.imshow(img, cmap='seismic') >>> fig, ax = plt.subplots() >>> ax.scatter(x,y,marker=".") >>> ax.plot(x,y,marker="o") >>> plt.title(r'$sigma_i=15$', fontsize=20) >>> ax.text(1, -2.1, 'Example Graph', style='italic') >>> ax.annotate("Sine", xy=(8, 0), xycoords='data', xytext=(10.5, 0), textcoords='data', arrowprops=dict(arrowstyle="->", connectionstyle="arc3"),) >>> plt.plot(x,y,linewidth=4.0) >>> plt.plot(x,y,ls='solid') >>> plt.plot(x,y,ls='--') >>> plt.plot(x,y,'--',x**2,y**2,'-.') >>> plt.setp(lines,color='r',linewidth=4.0) >>> import matplotlib.pyplot as plt Close & Clear >>> plt.cla() Clear an axis >>> plt.clf() Clear the entire figure >>> plt.close() Close a window](https://image.slidesharecdn.com/pythonmatplotlibcheatsheet-181208150952/75/Python-matplotlib-cheat_sheet-1-2048.jpg)
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Python matplotlib cheat_sheet
Matplotlib is a Python library used for 2D plotting. It can create publication-quality figures in both hardcopy and interactive formats across platforms. The basic steps for creating plots with Matplotlib are: 1) prepare data, 2) create a plot, 3) add elements to the plot, 4) customize the plot, 5) save the plot, and 6) show the plot. Matplotlib provides various functions for plotting different types of data, customizing figures, axes, and layouts.
Python matplotlib cheat_sheet
- 1. PythonForDataScience Cheat Sheet Matplotlib LearnPython Interactively at www.DataCamp.com Matplotlib DataCamp Learn Python for Data Science Interactively Prepare The Data Also see Lists & NumPy Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. 1 >>> import numpy as np >>> x = np.linspace(0, 10, 100) >>> y = np.cos(x) >>> z = np.sin(x) Show Plot >>> plt.show() Save Plot Save figures >>> plt.savefig('foo.png') Save transparent figures >>> plt.savefig('foo.png', transparent=True) 6 5 >>> fig = plt.figure() >>> fig2 = plt.figure(figsize=plt.figaspect(2.0)) Create Plot2 Plot Anatomy & Workflow All plotting is done with respect to an Axes. In most cases, a subplot will fit your needs. A subplot is an axes on a grid system. >>> fig.add_axes() >>> ax1 = fig.add_subplot(221) # row-col-num >>> ax3 = fig.add_subplot(212) >>> fig3, axes = plt.subplots(nrows=2,ncols=2) >>> fig4, axes2 = plt.subplots(ncols=3) Customize Plot Colors, Color Bars & Color Maps Markers Linestyles Mathtext Text & Annotations Limits, Legends & Layouts The basic steps to creating plots with matplotlib are: 1Prepare data 2Create plot 3Plot 4 Customize plot 5Save plot 6Show plot >>> import matplotlib.pyplot as plt >>> x = [1,2,3,4] >>> y = [10,20,25,30] >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.plot(x, y, color='lightblue', linewidth=3) >>> ax.scatter([2,4,6], [5,15,25], color='darkgreen', marker='^') >>> ax.set_xlim(1, 6.5) >>> plt.savefig('foo.png') >>> plt.show() Step 3, 4 Step 2 Step 1 Step 3 Step 6 Plot Anatomy Workflow 4 Limits & Autoscaling >>> ax.margins(x=0.0,y=0.1) Add padding to a plot >>> ax.axis('equal') Set the aspect ratio of the plot to 1 >>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) Set limits for x-and y-axis >>> ax.set_xlim(0,10.5) Set limits for x-axis Legends >>> ax.set(title='An Example Axes', Set a title and x-and y-axis labels ylabel='Y-Axis', xlabel='X-Axis') >>> ax.legend(loc='best') No overlapping plot elements Ticks >>> ax.xaxis.set(ticks=range(1,5), Manually set x-ticks ticklabels=[3,100,-12,"foo"]) >>> ax.tick_params(axis='y', Make y-ticks longer and go in and out direction='inout', length=10) Subplot Spacing >>> fig3.subplots_adjust(wspace=0.5, Adjust the spacing between subplots hspace=0.3, left=0.125, right=0.9, top=0.9, bottom=0.1) >>> fig.tight_layout() Fit subplot(s) in to the figure area Axis Spines >>> ax1.spines['top'].set_visible(False) Make the top axis line for a plot invisible >>> ax1.spines['bottom'].set_position(('outward',10))Move the bottom axis line outward Figure Axes >>> data = 2 * np.random.random((10, 10)) >>> data2 = 3 * np.random.random((10, 10)) >>> Y, X = np.mgrid[-3:3:100j, -3:3:100j] >>> U = -1 - X**2 + Y >>> V = 1 + X - Y**2 >>> from matplotlib.cbook import get_sample_data >>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy')) >>> lines = ax.plot(x,y) Draw points with lines or markers connecting them >>> ax.scatter(x,y) Draw unconnected points, scaled or colored >>> axes[0,0].bar([1,2,3],[3,4,5]) Plot vertical rectangles (constant width) >>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) Plot horiontal rectangles (constant height) >>> axes[1,1].axhline(0.45) Draw a horizontal line across axes >>> axes[0,1].axvline(0.65) Draw a vertical line across axes >>> ax.fill(x,y,color='blue') Draw filled polygons >>> ax.fill_between(x,y,color='yellow') Fill between y-values and 0 Plotting Routines3 1D Data >>> fig, ax = plt.subplots() >>> im = ax.imshow(img, Colormapped or RGB arrays cmap='gist_earth', interpolation='nearest', vmin=-2, vmax=2) 2D Data or Images Vector Fields >>> axes[0,1].arrow(0,0,0.5,0.5) Add an arrow to the axes >>> axes[1,1].quiver(y,z) Plot a 2D field of arrows >>> axes[0,1].streamplot(X,Y,U,V) Plot 2D vector fields Data Distributions >>> ax1.hist(y) Plot a histogram >>> ax3.boxplot(y) Make a box and whisker plot >>> ax3.violinplot(z) Make a violin plot >>> axes2[0].pcolor(data2) Pseudocolor plot of 2D array >>> axes2[0].pcolormesh(data) Pseudocolor plot of 2D array >>> CS = plt.contour(Y,X,U) Plot contours >>> axes2[2].contourf(data1) Plot filled contours >>> axes2[2]= ax.clabel(CS) Label a contour plot Figure Axes/Subplot Y-axis X-axis 1D Data 2D Data or Images >>> plt.plot(x, x, x, x**2, x, x**3) >>> ax.plot(x, y, alpha = 0.4) >>> ax.plot(x, y, c='k') >>> fig.colorbar(im, orientation='horizontal') >>> im = ax.imshow(img, cmap='seismic') >>> fig, ax = plt.subplots() >>> ax.scatter(x,y,marker=".") >>> ax.plot(x,y,marker="o") >>> plt.title(r'$sigma_i=15$', fontsize=20) >>> ax.text(1, -2.1, 'Example Graph', style='italic') >>> ax.annotate("Sine", xy=(8, 0), xycoords='data', xytext=(10.5, 0), textcoords='data', arrowprops=dict(arrowstyle="->", connectionstyle="arc3"),) >>> plt.plot(x,y,linewidth=4.0) >>> plt.plot(x,y,ls='solid') >>> plt.plot(x,y,ls='--') >>> plt.plot(x,y,'--',x**2,y**2,'-.') >>> plt.setp(lines,color='r',linewidth=4.0) >>> import matplotlib.pyplot as plt Close & Clear >>> plt.cla() Clear an axis >>> plt.clf() Clear the entire figure >>> plt.close() Close a window