Data Visualization using Python

[Neeraj Kaushik]

Dear Friends

I recently created a series of YouTube videos focused on Data Visualization using Python. 

In these videos, I explore Python's immense power and capabilities, demonstrating how its versatility, extensive libraries, and ease of use make it an incredible tool for Visualizations.

I discussed the following Python libraries:

1. plotnine

2. pandas

3. seaborn

4. matplotlib

5. plotly

Let's begin with plotnine first.

plotnine, developed by Hassan Kibirige, is a powerful Python library based on the Grammar of Graphics, enabling you to build complex, layered visualizations clearly and structurally. It allows the creation of elegant, publication-quality charts with minimal code, especially for users familiar with R’s ggplot2.

Here’s the structure of plotnine (based on Grammar of Graphics) in simple bullet points:

• Data: The dataset (usually a pandas DataFrame) that you want to visualize.
• Aesthetics (aes): Mapping of variables to visual properties like x, y, color, size, etc.
• Geometries (geom_*): The actual visual elements (e.g., geom_point, geom_line, geom_bar).
• Statistical transformations (stat_*): Summarize or transform data (e.g., counts, smoothing).
• Scales (scale_*): Control how data values map to aesthetics (colors, axes, sizes).
• Coordinates (coord_*): Define how data is displayed (e.g., Cartesian, polar).
• Facets (facet_*): Split data into multiple subplots based on variables.
• Themes (theme_*): Control overall appearance (fonts, backgrounds, gridlines).

All these components are combined using the + operator to build a complete visualization step by step.

Python Visualization (Plotnine-1 Three mandatory and three additional layers): https://youtu.be/3dfyVyKdLF0

Python Visualization (Plotnine-2 Three Advanced layers): https://youtu.be/7GVn-76_9so

Python Visualization  (Plotnine-3 Aesthetic components): https://youtu.be/Wn20ax7rzKs

Python Visualization (Plotnine-4 Annotation components): https://youtu.be/eN4PBktZO_0

Python Visualization (Plotnine-5 Univariate Charts- Histogram, Density plot, Boxplot): https://youtu.be/5vJH_ZR-6b4

Python Visualization  (Plotnine-6 Univariate Charts- Barchart, Column Chart): https://youtu.be/s83EUtQeagk

I hope you find these resources helpful. 

Happy Learning

Neeraj 

[Tarun Sengupta]

Truly a good initiative to help the researchers and python learner’s.

Thank you sir.

Dr. Tarun sengupta

+917586927094

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[Neeraj Kaushik]

Dear Friends,

Continuing our series on Data Visualization using Python, I have created a new YouTube video focusing on the pandas library.

Interesting Fact:

The Python pandas library name is derived from the term "panel data" (an econometrics term for multidimensional data sets) and is a play on the phrase "Python data analysis". Created by Wes McKinney in 2008, it was designed to provide high-performance, easy-to-use data structures, often used for data manipulation and analysis

While pandas is primarily known for data manipulation, its built-in visualization capabilities—powered by matplotlib—allow for quick and efficient data exploration directly from DataFrames. 

In this video, I demonstrate how to use the .plot() accessor to generate essential charts like line plots, bar charts, and histograms with minimal code. We explore how pandas simplifies the process by automatically handling axis labels and legends based on your data structure.

You can watch the video here:

Python Visualization (Pandas Visualization): https://youtu.be/5-QYeEZYOWM

Happy Learning,

Neeraj 

[kawaljitkaur sandhu]

Good morning sir

Please provide the videos on  how to use.  e-views software and data many more data analytical tools

Thanks & Regards

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[Neeraj Kaushik]

Plz refer to  Time Series Analysis Using EViews-12

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[Neeraj Kaushik]

Dear Friends

Continuing with the series of Data Visualization using Python, let's discuss about the Seaborn Python library. 

Seaborn offers powerful capabilities for creating statistical plots like heatmaps, pairplots, and regression charts with minimal code. It is built on top of Matplotlib, but provides a much cleaner and more user-friendly interface. The library also comes with attractive default styles, making visualizations look professional without much customization. Overall, Seaborn is an excellent tool for beginners and advanced users alike due to its ease of use and flexibility.

Do not forget to visit  seaborn: statistical data visualization

Before going ahead with the videos, let's discuss about the types of seaborn charts selected on the basis of number of variables and type of data:

One Variable (Non-metric):

1. Countplot

Piechart (Not available in seaborn but present in matplotlib)

One Variable (Metric):

2. Histogram

3. Density plot

4. Boxplot

5. Violin plot

6. Pointplot

Two Variables (One Non-metric and other metric):

Barchart

Boxplot

Violin Plot

7. Column Chart 

8. Stripplot

9. Swarmplot

10. catplot

Two Variables (Both Non-metric):

Countplot

Two Variables (Both Metric):

11. Scatterplot

12. regplot

13. relplot

14. lmplot

15. heatmap

16. pairplot

17. jointplot

I've explained its working in these videos:

Python Visualization (Seaborn-1 Introduction and Univariate charts): https://youtu.be/ast4VmNZ8L4

Python Visualization (Seaborn-2 Boxplot, Stripplot, Swarmplot, Violinplot, Countplot, Catplot): https://youtu.be/ycQY4g02wm0

Python Visualization (seaborn-3 scatterplot, lineplot, regplot, jointplot, pairplot, heatmap): https://youtu.be/RPC1lXlX2Ow

Happy Learning

Neeraj

[Neeraj Kaushik]

Dear Friends,

Continuing our series on Data Visualization using Python, I have created four new videos focusing on the Matplotlib library.

As the foundation for many other Python plotting tools, Matplotlib offers unparalleled control over every element of a figure, from axes and labels to complex layouts. Whether you need simple line plots or highly customized publication-quality graphics, Matplotlib remains an essential tool for any data analyst.

You can watch the videos here:

Python Visualization (Matplotlib-1 Introduction and Pie, Bar chart): https://youtu.be/Cug-fNk2Mbo

Python Visualization (Matplotlib-2 Histogram, Boxplot, Scatter plot with Regression Line): 
https://youtu.be/BfJ6EN6C-bo

Python Visualization (Matplotlib-3 Errorplot, Barplot, Boxplot, Violinplot): https://youtu.be/y9VxRCueaHY

Python Visualization Matplotlib-4 Bivariate charts: Barchart, Heatmap, imshow, 3D plot):

https://youtu.be/5Hw1PBrtRqw

Happy Learning,

Neeraj

[Neeraj Kaushik]

Dear Friends,

Continuing our series on Data Visualization using Python, I would like to introduce the Plotly library.

Plotly is a powerful, open-source graphing library that excels in creating interactive, web-based visualizations. Unlike static plots, Plotly allows users to hover over data points, zoom into specific regions, and toggle visibility of different traces for deeper exploration. It supports a wide array of chart types, from basic line and scatter plots to complex 3D charts and geographic maps. Its high-level interface, Plotly Express, makes it incredibly easy to build sophisticated and aesthetically pleasing dashboards with minimal code.

You can watch the videos here:

Python Visualization (plotly-1 Introduction, Bar, Pie, Hist, Box and Violin Plots):
https://youtu.be/VXWDY7-e4e4

Python Visualization (plotly-2 Scatter plot, Heatmap, Waterfall, Sunburst, Treemap, Funnel Chart): https://youtu.be/5a2417OUxAs

Python Visualization (plotly-3 Time Series, Area, Subplots, Choropleth, Scatter_geo, Update layout): https://youtu.be/Q-oesTdLlK8

Happy Learning,

Neeraj