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‎contrib/plotting-visualization/matplotlib-line-plot.md

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# Line Chart in Matplotlib
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A line chart is a simple way to visualize data where we connect individual data points. It helps us to see trends and patterns over time or across categories.
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<br> This type of chart is particularly useful for: </br>
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* Comparing Data: Comparing multiple datasets on the same axes.
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* Highlighting Changes: Illustrating changes and patterns in data.
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* Visualizing Trends: Showing trends over time or other continuous variables.
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## Prerequisites
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Line plots can be created in Python with Matplotlib's ``pyplot`` library. To build a line plot, first import ``Matplotlib``. It is a standard convention to import Matplotlib's pyplot library as ``plt``.
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```
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import matplotlib.pyplot as plt
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```
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## Creating a simple Line Plot
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First import matplotlib and numpy, these are useful for charting.
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<br> You can use the ``plot(x,y)`` method to create a line chart.</br>
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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print(x)
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y = 2*x + 1
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plt.plot(x, y)
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plt.show()
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```
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When executed, this will show the following line plot:
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![Basic line Chart](contrib/plotting-visualization/images/simple_line.png)
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## Curved line
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The ``plot()`` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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y = 2**x + 1
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plt.plot(x, y)
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plt.show()
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```
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When executed, this will show the following Curved line plot:
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![Curved line](contrib/plotting-visualization/images/line-curve.png)
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## Line with Labels
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To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an ``x label``, ``y label`` and ``title``.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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y1 = 2*x + 1
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y2 = 2**x + 1
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plt.figure()
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plt.plot(x, y1)
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plt.xlabel("I am x")
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plt.ylabel("I am y")
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plt.title("With Labels")
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plt.show()
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```
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When executed, this will show the following line with labels plot:
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![line with labels](contrib/plotting-visualization/images/line-labels.png)
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## Multiple lines
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More than one line can be in the plot. To add another line, just call the ``plot(x,y)`` function again. In the example below we have two different values for ``y(y1,y2)`` that are plotted onto the chart.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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y1 = 2*x + 1
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y2 = 2**x + 1
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plt.figure(num = 3, figsize=(8, 5))
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plt.plot(x, y2)
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plt.plot(x, y1,
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color='red',
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linewidth=1.0,
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linestyle='--'
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)
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plt.show()
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```
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When executed, this will show the following Multiple lines plot:
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![multiple lines](contrib/plotting-visualization/images/two-lines.png)
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## Dotted line
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Lines can be in the form of dots like the image below. Instead of calling ``plot(x,y)`` call the ``scatter(x,y)`` method. The ``scatter(x,y)`` method can also be used to (randomly) plot points onto the chart.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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n = 1024
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X = np.random.normal(0, 1, n)
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Y = np.random.normal(0, 1, n)
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T = np.arctan2(X, Y)
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plt.scatter(np.arange(5), np.arange(5))
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plt.xticks(())
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plt.yticks(())
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plt.show()
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```
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When executed, this will show the following Dotted line plot:
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![dotted lines](contrib/plotting-visualization/images/dot-line.png)
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## Line ticks
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You can change the ticks on the plot. Set them on the ``x-axis``, ``y-axis`` or even change their color. The line can be more thick and have an alpha value.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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y = 2*x - 1
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plt.figure(figsize=(12, 8))
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plt.plot(x, y, color='r', linewidth=10.0, alpha=0.5)
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ax = plt.gca()
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ax.spines['right'].set_color('none')
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ax.spines['top'].set_color('none')
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ax.xaxis.set_ticks_position('bottom')
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ax.yaxis.set_ticks_position('left')
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ax.spines['bottom'].set_position(('data', 0))
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ax.spines['left'].set_position(('data', 0))
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for label in ax.get_xticklabels() + ax.get_yticklabels():
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label.set_fontsize(12)
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label.set_bbox(dict(facecolor='y', edgecolor='None', alpha=0.7))
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plt.show()
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```
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When executed, this will show the following line ticks plot:
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![line ticks](contrib/plotting-visualization/images/line-ticks.png)
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## Line with asymptote
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An asymptote can be added to the plot. To do that, use ``plt.annotate()``. There’s lso a dotted line in the plot below. You can play around with the code to see how it works.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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y1 = 2*x + 1
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y2 = 2**x + 1
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plt.figure(figsize=(12, 8))
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plt.plot(x, y2)
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plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
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ax = plt.gca()
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ax.spines['right'].set_color('none')
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ax.spines['top'].set_color('none')
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ax.xaxis.set_ticks_position('bottom')
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ax.yaxis.set_ticks_position('left')
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ax.spines['bottom'].set_position(('data', 0))
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ax.spines['left'].set_position(('data', 0))
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x0 = 1
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y0 = 2*x0 + 1
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plt.scatter(x0, y0, s = 66, color = 'b')
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plt.plot([x0, x0], [y0, 0], 'k-.', lw= 2.5)
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plt.annotate(r'$2x+1=%s$' %
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y0,
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xy=(x0, y0),
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xycoords='data',
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xytext=(+30, -30),
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textcoords='offset points',
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fontsize=16,
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arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=.2')
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)
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plt.text(0, 3,
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r'$This\ is\ a\ good\ idea.\ \mu\ \sigma_i\ \alpha_t$',
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fontdict={'size':16,'color':'r'})
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plt.show()
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```
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When executed, this will show the following Line with asymptote plot:
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![Line with asymptote](contrib/plotting-visualization/images/line-asymptote.png)
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## Line with text scale
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It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In ``plt.yticks()`` we just pass a list with text values. These values are then show against the ``y axis``.
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```
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(-1, 1, 50)
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y1 = 2*x + 1
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y2 = 2**x + 1
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plt.figure(num = 3, figsize=(8, 5))
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plt.plot(x, y2)
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plt.plot(x, y1,
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color='red',
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linewidth=1.0,
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linestyle='--'
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)
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plt.xlim((-1, 2))
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plt.ylim((1, 3))
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new_ticks = np.linspace(-1, 2, 5)
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plt.xticks(new_ticks)
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plt.yticks([-2, -1.8, -1, 1.22, 3],
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[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$readly\ good$'])
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ax = plt.gca()
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ax.spines['right'].set_color('none')
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ax.spines['top'].set_color('none')
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ax.xaxis.set_ticks_position('bottom')
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ax.yaxis.set_ticks_position('left')
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ax.spines['bottom'].set_position(('data', 0))
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ax.spines['left'].set_position(('data', 0))
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plt.show()
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```
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When executed, this will show the following Line with text scale plot:
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![Line with text scale](contrib/plotting-visualization/images/line-with-text-scale.png)
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