Data visualization is the visual presentation of data or information for quick and impactful communication. We all have heard the phrase “Picture speaks louder than words”. According to research, our brain can recall 65% of visual data, 10 to 20% of spoken words, and only 10% of written data. Thus data visualization communicates data or information clearly and effectively to readers in the form of graphs, charts, maps, plots, and many more. Various plots and charts help us to analyze data from all angles and efficiently extract valuable insights from data. We will be learning different types of bar charts and their customization in this article.
Who Can Benefit from Comparing Python Plotting Packages? All aspiring Data Scientists, Machine Learning Engineers, and Artificial intelligence enthusiasts must have hands-on knowledge of Data Visualization. As it is the first step for data understanding and exploration. How does it benefit? Data Visualization helps in better data understanding Helps in identifying and highlighting key values of data Helps in Identifying various trends and outliers Displays complex data correlation with ease Attracts and engages users Helps in data exploration and making the right decisions DataSet Introduction We will take an ‘Adult Income Census’ dataset to explore and visualize with different libraries. In this article, we will explore the selected ‘Adult Income Census’ dataset using different types of bar charts and their customization. Link for the dataset: /kaggle/input/adult-income-census/adult income1.csv
It is an income prediction dataset originated from the 1994 Census Bureau database The dataset has 31947 data entries and the following 12 columns:
age: Age of the individual. workclass: The type of employment (e.g., Private, Self-emp-not-inc, Local-gov, Federal-gov, Never-worked, etc.). fnlwgt: Final weight used in statistical sampling. education: The highest level of education of the individual (e.g., Bachelors, Doctorate, HS-grad, 11th, 10th Masters, etc.). education. num: The numerical representation of education, often corresponding to the years of education. marital.status: Marital status of the individual (e.g., Married-AF-spouse, Divorced, Never-married, Separated, etc.). occupation: The individual's occupation (e.g., Armed-Forces, craft repair, Prof-specialty, Sales, etc.). relationship: Relationship status of the individual (e.g., Wife, Child, Husband, Not-in-family, Other-relative, Unmarried). race: Ethnicity or race of the individual (e.g., White, Black, Other, etc.) sex: Gender of the individual (e.g., Male, Female). native.country: Country of origin or native country of the individual (e.g., United States, Canada, England, Puerto Rico, etc.). income: The target variable to be predicted indicates whether an individual's annual income is below or exceeds $50K. The value is either ">50K" or "<=50K."
We are using this dataset for data visualization as a first step in the machine learning task.
- Bar Chart
- Grouped Bar chart
- Stacked Chart
- 100% Stacked Chart
In the Final Scorecard, we will compare all packages on the below parameters:
No doubt Matplotlib, Seaborn, and Plotly all three are top Python packages for data visualizations. Matplotlib might require more lines of code to implement but it is highly interactive and simple and fast to execute. When Seaborn combined with Matplotlib both did a wonderful job for different graphs. On the other hand, Plotly might seem a little complex at first to understand, but once engineers get their hands on it, it will be like an eye-blinking job for huge data visualizations.
You can choose the Data Visualization package wisely according to the dataset, the type of chart to be prepared, and your understanding and command of the library.