## Matplotlib legend

Matplotlib has native support for legends. Legends can be placed in various positions: A legend can be placed inside or outside the chart and the position can be moved.

The legend() method adds the legend to the plot. In this article we will show you some examples of legends using matplotlib.

Related course
Data Visualization with Python and Matplotlib

Matplotlib legend inside
To place the legend inside, simply call legend():

```import matplotlib.pyplot as plt import numpy as np   y = [2,4,6,8,10,12,14,16,18,20] y2 = [10,11,12,13,14,15,16,17,18,19] x = np.arange(10) fig = plt.figure() ax = plt.subplot(111) ax.plot(x, y, label='\$y = numbers') ax.plot(x, y2, label='\$y2 = other numbers') plt.title('Legend inside') ax.legend() plt.show()```

Matplotlib legend on bottom
To place the legend on the bottom, change the legend() call to:

`ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),  shadow=True, ncol=2)`

Take into account that we set the number of columns two ncol=2 and set a shadow.

The complete code would be:

```import matplotlib.pyplot as plt import numpy as np   y = [2,4,6,8,10,12,14,16,18,20] y2 = [10,11,12,13,14,15,16,17,18,19] x = np.arange(10) fig = plt.figure() ax = plt.subplot(111) ax.plot(x, y, label='\$y = numbers') ax.plot(x, y2, label='\$y2 = other numbers') plt.title('Legend inside') ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),  shadow=True, ncol=2) plt.show()```

Matplotlib legend on top
To put the legend on top, change the bbox_to_anchor values:

`ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.00),  shadow=True, ncol=2)`

Code:

```import matplotlib.pyplot as plt import numpy as np   y = [2,4,6,8,10,12,14,16,18,20] y2 = [10,11,12,13,14,15,16,17,18,19] x = np.arange(10) fig = plt.figure() ax = plt.subplot(111) ax.plot(x, y, label='\$y = numbers') ax.plot(x, y2, label='\$y2 = other numbers') plt.title('Legend inside') ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.00), shadow=True, ncol=2) plt.show()```

Legend outside right
We can put the legend ouside by resizing the box and puting the legend relative to that:

```chartBox = ax.get_position() ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height]) ax.legend(loc='upper center', bbox_to_anchor=(1.45, 0.8), shadow=True, ncol=1)```

Code:

```import matplotlib.pyplot as plt import numpy as np   y = [2,4,6,8,10,12,14,16,18,20] y2 = [10,11,12,13,14,15,16,17,18,19] x = np.arange(10) fig = plt.figure() ax = plt.subplot(111) ax.plot(x, y, label='\$y = numbers') ax.plot(x, y2, label='\$y2 = other numbers') plt.title('Legend outside') chartBox = ax.get_position() ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height]) ax.legend(loc='upper center', bbox_to_anchor=(1.45, 0.8), shadow=True, ncol=1) plt.show()```

## Matplotlib save figure to image file

Related course
The course below is all about data visualization:

Save figure
Matplotlib can save plots directly to a file using savefig().
The method can be used like this:

`fig.savefig('plot.png')`

Complete example:

```import matplotlib import matplotlib.pyplot as plt import numpy as np   y = [2,4,6,8,10,12,14,16,18,20] x = np.arange(10) fig = plt.figure() ax = plt.subplot(111) ax.plot(x, y, label='\$y = numbers') plt.title('Legend inside') ax.legend() #plt.show()   fig.savefig('plot.png')```

To change the format, simply change the extension like so:

`fig.savefig('plot.pdf')`

You can open your file using

`display plot.png`

or open it in an image or pdf viewer,

## Matplotlib update plot

Updating a matplotlib plot is straightforward. Create the data, the plot and update in a loop.
Setting interactive mode on is essential: plt.ion(). This controls if the figure is redrawn every draw() command. If it is False (the default), then the figure does not update itself.

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Update plot example
Copy the code below to test an interactive plot.

```import matplotlib.pyplot as plt import numpy as np   x = np.linspace(0, 10*np.pi, 100) y = np.sin(x)   plt.ion() fig = plt.figure() ax = fig.add_subplot(111) line1, = ax.plot(x, y, 'b-')   for phase in np.linspace(0, 10*np.pi, 100): line1.set_ydata(np.sin(0.5 * x + phase)) fig.canvas.draw()```

Explanation
We create the data to plot using:

```x = np.linspace(0, 10*np.pi, 100) y = np.sin(x)```

Turn on interacive mode using:

`plt.ion()`

Configure the plot (the ‘b-‘ indicates a blue line):

```fig = plt.figure() ax = fig.add_subplot(111) line1, = ax.plot(x, y, 'b-')```

And finally update in a loop:

```for phase in np.linspace(0, 10*np.pi, 100): line1.set_ydata(np.sin(0.5 * x + phase)) fig.canvas.draw()```

## Plot time with matplotlib

Matplotlib supports plots with time on the horizontal (x) axis. The data values will be put on the vertical (y) axis. In this article we’ll demonstrate that using a few examples.

It is required to use the Python datetime module, a standard module.

Related course
Data Visualization with Python and Matplotlib

Plot time
You can plot time using a timestamp:

```import matplotlib import matplotlib.pyplot as plt import numpy as np import datetime   # create data y = [ 2,4,6,8,10,12,14,16,18,20 ] x = [datetime.datetime.now() + datetime.timedelta(hours=i) for i in range(len(y))]   # plot plt.plot(x,y) plt.gcf().autofmt_xdate() plt.show()```

If you want to change the interval use one of the lines below:

```# minutes x = [datetime.datetime.now() + datetime.timedelta(minutes=i) for i in range(len(y))]```

Time plot from specific hour/minute

To start from a specific date, create a new timestamp using datetime.datetime(year, month, day, hour, minute).
Full example:

```import matplotlib import matplotlib.pyplot as plt import numpy as np import datetime   # create data customdate = datetime.datetime(2016, 1, 1, 13, 30) y = [ 2,4,6,8,10,12,14,16,18,20 ] x = [customdate + datetime.timedelta(hours=i) for i in range(len(y))]   # plot plt.plot(x,y) plt.gcf().autofmt_xdate() plt.show()```

## Generate heatmap in Matplotlib

A heatmap can be created using Matplotlib and numpy.

Related courses
If you want to learn more on data visualization, these courses are good:

## Heatmap example

The histogram2d function can be used to generate a heatmap.

We create some random data arrays (x,y) to use in the program. We set bins to 64, the resulting heatmap will be 64×64. If you want another size change the number of bins.

```import numpy as np import numpy.random import matplotlib.pyplot as plt   # Create data x = np.random.randn(4096) y = np.random.randn(4096)   # Create heatmap heatmap, xedges, yedges = np.histogram2d(x, y, bins=(64,64)) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]   # Plot heatmap plt.clf() plt.title('Pythonspot.com heatmap example') plt.ylabel('y') plt.xlabel('x') plt.imshow(heatmap, extent=extent) plt.show()```

Result:

The datapoints in this example are totally random and generated using np.random.randn()

## Matplotlib Subplot

The Matplotlib subplot() function can be called to plot two or more plots in one figure. Matplotlib supports all kind of subplots including 2×1 vertical, 2×1 horizontal or a 2×2 grid.

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Horizontal subplot
Use the code below to create a horizontal subplot

```from pylab import *   t = arange(0.0, 20.0, 1) s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]   subplot(2,1,1) xticks([]), yticks([]) title('subplot(2,1,1)') plot(t,s)   subplot(2,1,2) xticks([]), yticks([]) title('subplot(2,1,2)') plot(t,s,'r-')   show()```

Vertical subplot
By changing the subplot parameters we can create a vertical plot

```  from pylab import *   t = arange(0.0, 20.0, 1) s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]   subplot(1,2,1) xticks([]), yticks([]) title('subplot(1,2,1)') plot(t,s)   subplot(1,2,2) xticks([]), yticks([]) title('subplot(1,2,2)') plot(t,s,'r-')   show()```

Subplot grid
To create a 2×2 grid of plots, you can use this code:

```from pylab import *   t = arange(0.0, 20.0, 1) s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]   subplot(2,2,1) xticks([]), yticks([]) title('subplot(2,2,1)') plot(t,s)   subplot(2,2,2) xticks([]), yticks([]) title('subplot(2,2,2)') plot(t,s,'r-')   subplot(2,2,3) xticks([]), yticks([]) title('subplot(2,2,3)') plot(t,s,'g-')   subplot(2,2,4) xticks([]), yticks([]) title('subplot(2,2,4)') plot(t,s,'y-')   show()```