"A beginner's guide to Data Visualization with Python"

What is Data Visualization?

• Data Visualization is one of the main tools or concepts used to analyze and study relationships between different variables. Often, a data scientist needs to "play with data", visualizing it many times and looking for some relationships.

• Data visualization (e.g., scatter plots, line graphs, bar plots, histograms, Q-Q plots, smooth densities, boxplots, pair plots, heat maps, etc.) can be used for descriptive analytics.

• Data visualization is also used in machine learning for data preprocessing and analysis, feature selection, model building, model testing, and model evaluation.

Popular Libraries

• Matplotlib - https://www.w3schools.com/python/matplotlib_pyplot.asp

• Seaborn - https://www.w3schools.com/python/numpy/numpy_random_seaborn.asp

The above links redirect to the vital context of the libraries respectively.

Printing and Plotting

• A Data Scientist/Analyst often has to explore the data, thus it is important to be able to visualize it. When DataFrame is big, many times we want to make sure we are doing everything correctly by printing out the first few rows. This can be done by calling [df.head()] if you are running it from Jupyter Notebook, it will print out the DataFrame in a nice tabular form.

• One of the basic skills of a data scientist/analyst is the ability to create a meaningful Data Visualization that helps answer questions you might have. Before visualizing your data, you need to ensure that it has been cleaned and prepared. After that, you can start deciding how best to present the data.

Types of Charts and their Significance

The link to every chart mentioned above are as follows:-

1. Line chart - https://www.w3schools.com/python/matplotlib_line.asp

2. Bar chart - https://www.w3schools.com/python/matplotlib_bars.asp

3. Pie Chart - https://www.w3schools.com/python/matplotlib_pie_charts.asp

4. Stacked bar chart - https://matplotlib.org/stable/gallery/lines_bars_and_markers/bar_stacked.html

5. Scatter Chart - https://www.w3schools.com/python/python_ml_scatterplot.asp

6. Dual Line Chart - https://www.w3resource.com/graphics/matplotlib/basic/matplotlib-basic-exercise-5.php

7. Donut Chart - https://www.python-graph-gallery.com/donut-plot/

8. Histogram chart - https://www.w3schools.com/python/matplotlib_histograms.asp

Color and their meaning

1. Red - Power

2. Blue - Trust, loyalty

3. Yellow - Happiness, caution

4. Green - Ecology, Luck, Envy

5. Purple - Happiness

6. Orange - Vibrance

If you are asked to build a chart with custom colors, ensure that your charts are both accessible and the color you choose coincides with the meaning you are trying to convey.

Industries that perform data visualization on daily routine

• Public: news, journalism, publishing, GovTech, law enforcement, public safety

• Industry: software, analytics, medical, geospatial, business intelligence, FinTech health, InsurTech, HR

• Science: higher education, e-learning, STEM education, neuroscience

  

Conclusion

The information shared above is a beginner's guide for anyone to understand the basics of Data visualization using python. All Links mentioned above will get you to the context of every parameter required to understand and perform data visualization using python. The intermediate part of data Visualization is making visualization interactive and getting insights from it convenient and creating value for the organization.

Thank you for reading.