From 839211767809e4c7d53f38dd21dafd90ccd9bd5a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:56:26 +0530 Subject: [PATCH] Update matplotlib-line-plot.md --- .../matplotlib-line-plot.md | 56 +++++++++++-------- 1 file changed, 33 insertions(+), 23 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md index 566fdbb..b7488e6 100644 --- a/contrib/plotting-visualization/matplotlib-line-plot.md +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -1,24 +1,27 @@ # Line Chart in Matplotlib 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. -
This type of chart is particularly useful for:
-* 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. + +This type of chart is particularly useful for: +- 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 +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`. + +```python +import matplotlib.pyplot as plt ``` ## Creating a simple Line Plot First import matplotlib and numpy, these are useful for charting. -
You can use the ``plot(x,y)`` method to create a line chart.
-``` +You can use the `plot(x,y)` method to create a line chart. + +```python import matplotlib.pyplot as plt import numpy as np @@ -29,6 +32,7 @@ y = 2*x + 1 plt.plot(x, y) plt.show() ``` + When executed, this will show the following line plot: ![Basic line Chart](images/simple_line.png) @@ -36,8 +40,9 @@ When executed, this will show the following line plot: ## Curved line -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. -``` +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. + +```python import matplotlib.pyplot as plt import numpy as np @@ -47,6 +52,7 @@ y = 2**x + 1 plt.plot(x, y) plt.show() ``` + When executed, this will show the following Curved line plot: ![Curved line](images/line-curve.png) @@ -54,8 +60,9 @@ When executed, this will show the following Curved line plot: ## Line with Labels -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``. -``` +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`. + +```python import matplotlib.pyplot as plt import numpy as np @@ -72,15 +79,16 @@ plt.title("With Labels") plt.show() ``` + When executed, this will show the following line with labels plot: ![line with labels](images/line-labels.png) ## Multiple lines -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. +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. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -98,6 +106,7 @@ plt.plot(x, y1, plt.show() ``` + When executed, this will show the following Multiple lines plot: ![multiple lines](images/two-lines.png) @@ -105,9 +114,9 @@ When executed, this will show the following Multiple lines plot: ## Dotted line -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. +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. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -130,9 +139,9 @@ When executed, this will show the following Dotted line plot: ## Line ticks -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. +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. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -166,9 +175,9 @@ When executed, this will show the following line ticks plot: ## Line with asymptote -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. +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. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -222,9 +231,9 @@ When executed, this will show the following Line with asymptote plot: ## Line with text scale -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``. +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`. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -261,6 +270,7 @@ ax.spines['left'].set_position(('data', 0)) plt.show() ``` + When executed, this will show the following Line with text scale plot: ![Line with text scale](images/line-with-text-scale.png)