Python: Mastering Data Visualization with PyPlot Library in 12 Minutes
DescriptionIn this tutorial, we will learn how to create charts in Python using the PyPlot library. We will be using Google Colab for Python programming.
To begin, open Google Drive and create a new notebook by clicking on the "New" button. From the options, select Google Colaboratory. Rename and save the notebook in your Google Drive for future access.
Before creating visualizations, we need to import the PyPlot library. This can be done by adding the necessary import statement at the beginning of your code.
Next, we will define some lists of integers that we will use in our charts. For example, we can define a variable n containing a list of numbers from one to nine, and a variable x containing the same numbers but not in a sequence.
To create a simple line chart, we can use the plot function and provide the data as input. Running the script will display the resulting chart. However, it is recommended to use the figure function from the PyPlot library to create charts.
To use the figure function, we need to define it in our code and also specify a subplot on which to draw our chart. This provides more flexibility as we can define additional features such as labels, legends, and colors.
We can simplify the code by defining the figure and subplot in the same line.
To add a title to our chart, we can use the set_title function. Similarly, we can use the set_xlabel and set_ylabel functions to define labels for the x and y axes.
To display a legend, we can use the legend function and define the items we want to display.
We can convert a line chart into an area chart by using the stackplot function. For a bar chart, we can use the bar function. Adding 'h' at the end of 'bar' will create a horizontal bar chart.
It is also possible to display a line chart and a bar chart in a single chart.
To create a step chart, we can use the step function. And for a pie chart, we can use the pie function. By commenting out the axis labels, we can remove them from the chart.
A scatter chart can be created using the scatter function. Adding a legend can be done by defining the legend as 'x'.
We can also create a box plot using the boxplot function. This function takes data defined in a variable, such as 'b', which contains a multidimensional list of integers representing minimum, average, and maximum values.
By plotting a line over our box plot, we can compare the two variables.
Similarly, a violin plot can be created using the violinplot function. This plot shows the upper and lower limits of the data.
To display a spectrogram, we can use the specgram function. This function creates a color-coded representation of the data's intensity.
A histogram can be created using the hist function. Define a variable 'h' containing a list of integers and their frequency of occurrence. The repetition of an integer represents its frequency.
You can also define the bucket size in a histogram, which adjusts according to the number of buckets specified.
The hexbin function is used to create a hexbin chart, which is useful for displaying a large number of overlapping data points.
We can generate a large number of data points using NumPy's random function and display them using hexbin.
Finally, we conclude the tutorial and invite viewers to like and subscribe for more tutorials and updates.
Thank you for watching, and we hope you found this tutorial helpful.
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00:17 - Creating a Python Notebook in Google Drive
00:44 - Creating a Chart using Pyplot Library
02:00 - Using Figure and Subplots
03:07 - Adding Titles, Labels and Legend
04:58 - Displaying Bar Chart, Horizontal Bar Chart and Line & Bar Chart
05:39 - Displaying a Step Chart
05:52 - Displaying a Pie Chart
06:40 - Displaying a Scatter Chart
07:27 - Displaying Boxplot and Violinplot
08:58 - Displaying a Spectogram
09:16 - Displaying a Histogram
10:14 - Displaying a Hexbin
Charts in Python,
Data visualization tutorial,