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.
<br> This type of chart is particularly useful for: </br>
* Comparing Data: Comparing multiple datasets on the same axes.
* Highlighting Changes: Illustrating changes and patterns in data.
* Visualizing Trends: Showing trends over time or other continuous variables.
## Prerequisites
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``.
```
import matplotlib.pyplot as plt
```
## Creating a simple Line Plot
First import matplotlib and numpy, these are useful for charting.
<br> You can use the ``plot(x,y)`` method to create a line chart.</br>
```
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1, 1, 50)
print(x)
y = 2*x + 1
plt.plot(x, y)
plt.show()
```
When executed, this will show the following line plot:
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``.
```
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1, 1, 50)
y1 = 2*x + 1
y2 = 2**x + 1
plt.figure()
plt.plot(x, y1)
plt.xlabel("I am x")
plt.ylabel("I am y")
plt.title("With Labels")
plt.show()
```
When executed, this will show the following line with labels plot:
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.
```
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1, 1, 50)
y1 = 2*x + 1
y2 = 2**x + 1
plt.figure(num = 3, figsize=(8, 5))
plt.plot(x, y2)
plt.plot(x, y1,
color='red',
linewidth=1.0,
linestyle='--'
)
plt.show()
```
When executed, this will show the following Multiple lines plot:
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.
```
import matplotlib.pyplot as plt
import numpy as np
n = 1024
X = np.random.normal(0, 1, n)
Y = np.random.normal(0, 1, n)
T = np.arctan2(X, Y)
plt.scatter(np.arange(5), np.arange(5))
plt.xticks(())
plt.yticks(())
plt.show()
```
When executed, this will show the following Dotted line plot:
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.
```
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1, 1, 50)
y = 2*x - 1
plt.figure(figsize=(12, 8))
plt.plot(x, y, color='r', linewidth=10.0, alpha=0.5)
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['bottom'].set_position(('data', 0))
ax.spines['left'].set_position(('data', 0))
for label in ax.get_xticklabels() + ax.get_yticklabels():
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.
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``.
