Data analysts in modern data-driven Enterprises want to be empowered with powerful new-age tools and strategies to extract a wealth of actionable insights at the speed of business in near real-time. Python, with its diverse libraries, packages, and frameworks, can democratize data analysis & visualization and introduce more agility and self-service.
Python has a 27.91% share on the list of the most preferred programming
languages by data scientists in 2023 – Datacamp
The simplicity, flexibility, and robust data manipulation capabilities make Python best suited to slice and dice your voluminous and heterogeneous data for quick business-critical decisions in varying market environments. In this blog, we learn more about Python and explore how it can be leveraged to analyze and visualize data seamlessly.
Data Analysis and Visualization
Data analytics services involves processes to inspect, transform, and model data to discover pivotal insights for informed business decisions. The goal is to examine, interpret, and extract trapped value from the complex data estate and turn it into actionable insights by uncovering patterns, trends, and relationships quickly & easily. Data visualization simplifies the same. Visual storytelling through charts and interactive dashboards makes consuming and communicating findings easier.
Graphical representation makes the entire data analysis more inclusive and accessible. Data consumers (non-technical folks) become more involved, reducing the overdependence on IT and enabling a flexible analytical ecosystem. Visually-driven analytical processes empower resources through simplified data comprehension and more agility, giving organizations a competitive edge, driving innovation, and finding growth opportunities through quick & informed decision-making.
Data Visualization using Python
Python, a popular programming language, has acquired quite a reputation in the data analysis & visualization domain due to its versatility, simplicity, and extensive collection of visualization libraries. These libraries provide diverse visualization techniques to aesthetically represent complex data, enabling users to interact and consume insights easily.
Python lets you import, collate, clean, process, and present the data in the desired visualization technique. Plus, customize the same and export it in the desired format. Python provides various customization options, enabling data consumers to create stunning and informative visualizations that effectively relay insights and make data consumption exercises more seamless and fun.
Why Do Data Analysts Prefer Using Python – What advantages Python holds?
1. Easy to Learn and Use:
Python has a readable, lucid, and simple syntax, making it easy to learn and use. The simplicity, user-friendly nature, and accessibility lead to a shorter learning curve for beginners, making it a popular choice for data analysts who are getting started.
2. Flexibility and Versatility:
Python’s flexibility allows extensive customization and control. Data analysts can tailor their workflows to suit various needs and project requirements like data cleaning, manipulation, visualization, and modeling for structured and unstructured data.
3. Speed and Efficiency:
Python’s vast libraries like Pandas, NumPy, SciPy, SymPy, PyLearn2, PyMC Bokeh, ggplot, Plotly, and seaborn, automation framework (PYunit), and pre-made templates enable a fast and efficient programming timeline, allowing quick data processing and analysis. This is particularly useful for time-sensitive projects involving large datasets. link
4. Open Source Community:
A global pool of seasoned experts ready to share advice for any issues 24/7. Developers experienced in all facets of Python endorse, enrich, and enlighten to improve and make Python more accessible and valuable. The community boasts support, tutorials, guides, videos, and other relevant materials to shorten and streamline your project course.
5. Integration and Interoperability:
Python can easily integrate with other programming languages, technologies, and various systems & data sources. Python can integrate with Big Data like Hadoop and Apache Spark, databases like MySQL, PostgreSQL, and MongoDB, and tools like Jenkins, Git, and Docker. link
What is Numerical Computation: Python and NumPy Arrays
Numerical computation is a critical aspect of scientific computing and data analysis, and NumPy works as a reliable Python library equipped with robust data structures (ndarray or n-dimensional array) to support the same. NumPy provides fast and efficient processing of multidimensional arrays. Here’s more about NumPy:
• NumPy is implemented in C and optimized for numerical computations
• Supports various mathematical functions such as trigonometric, exponential, log, etc.
• Deals with complex numerical computations like statistical analysis, Fourier transforms, etc.
• Allows matrix multiplication & decomposition, probability distributions, & linear operations
• Integrates with other Python libraries like SciPy, Pandas, and matplotlib
• Provides tools that enable efficient selection and modification of array elements
• Array manipulation includes reshaping, transposing, slicing, and indexing
• Supports a wide range of 3rd-party libraries like NumPy-ML, NumPyro, and Dask
• Supports loading & saving array data to/from disk in various formats like binary, text, & CSV
How to Analyze Tabular Data Using Python
1. Read and View Data:
Load the data into the Pandas dataframe and preview the data. You can read the data from a CSV, SQL database, or any other data source and then use functions to understand the information about the dataframe.
2. Load the data:
You can explore the loaded data using the head(), info(), and describe() methods. You could also extract the data from the different columns and rows (loc and iloc functions) or apply conditional filtering to obtain a specific dataset.
3. Data Cleaning:
You may need to clean it up before analysis to ensure accuracy and consistency. This involves removing ambiguity, null values, duplicates, outliers, inconsistent values, adding missing values, and addressing any other unwanted data.
4. Data Manipulation:
You can manipulate it through Regression, Classification, Clustering, etc. You can use the groupby() method to group the data, use the sort_values() method to sort data, aggregate data using the sum(), min(), max(), etc., methods, or perform other operations.
5. Data Visualization:
Finally, you can visualize the available data. You can use Matplotlib, seaborn, or other libraries to create various plots and charts to make it easier to understand the data.
Which data visualization tool is best for Python?
1. Matplotlib:
Matplotlib is a widely-used plotting library for creating animated, static, and interactive visualizations. Develop publication-quality plots, build interactive figures that pan, zoom, and update, customize layouts and visual styles, export to multiple file formats, embed in JupyterLab & GUI, and leverage a rich array of 3rd-party packages for all data visualization needs.
2. Seaborn:
Seaborn is built on top of matplotlib and provides a higher-level interface for creating attractive statistical visualizations. Get in-built themes for designing matplotlib graphs, visualize univariate & bivariate data, plot time-series data stylistically, and visualize linear regression models. Seaborn is compatible with Pandas and NumPy.
3. Plotly:
Plotly is a powerful plotting library for creating interactive charts & maps and rendering dynamic visualizations. Plotly lets you create line, box, & scatter plots, area, polar, bubble, & bar charts, error bars, multiple-axes, histograms, heatmaps, and subplots. From statistical charts to scientific charts, animations, financial and 3D charts, easily translate your data into visuals.
4. Bokeh:
Bokeh lets you create interactive visualizations & build beautiful graphics. Create scatter, categorical, time-series, statistical, & contour plots, grids & layouts, bar, pie, & donut charts, area glyphs, and lines & curves. Furthermore, build hex tiles, geographical data, and network graphs to develop, customize, and reimagine your data in an easy-to-use interface.
5. Altair:
Altair is a declarative statistical data visualization library based on Vega and Vega-Lite visualization grammars that can be used to create beautiful data visualizations such as bar, error, & pie charts, histograms, scatterplots, power spectra, stemplots, etc. using little coding of concise and intuitive syntax to develop a range of sophisticated and stylish visualizations.
What else is used for Data Visualization other than Python?
1. Tableau:
Pre-built dashboards, accelerators, data stories, unparalleled support, fast actionable insights, and other rich analytical features make Tableau a reliable and user-friendly tool to slice & dice data and visually explore the same in an intuitive interface. Data experts and Line of Business users use Tableau to uncover insights with ML, NLP, statistics, and smart data prep.
2. Power BI:
A powerful analytics tool to unlock valuable insights from structured and unstructured data with in-built AI, ML, and self-service capabilities. Build data models of complex datasets, pull in near-real-time data, collaborate with peers, automate workflows, integrate with 3rd Party apps, design stunning dashboards, and share drilled-down reports for agile decisions.
3. Excel:
Excel is another versatile, cost-effective, and easy-to-use analysis tool containing diverse functions and features to manipulate and visualize data. Cleanse data, create charts, graphs, pivot tables, & dynamic reports, use the pre-built templates, and leverage the built-in data validation, complex formulas, & error checking features to dissect data and build visualization workflows.
4. Qlik:
A complete analytics solution with sophisticated AI, predictive analytics, self-service visualization, search & conversational analytics, visually-rich reporting, GeoSpatial analysis, data-driven alerts, and spectacular visualization capabilities to simplify & accelerate your analysis processes for a faster data-to-action funnel for your ad-hoc organizational data-reliant outcomes.
Conclusion
Python’s vast library of tools and packages makes it an excellent choice for data analysis and visualization. Furthermore, the flexibility, ease of use, detailed documentation hub, community support, and open-source nature make Python the most reliable language and an all-in-one solution for complex data sets and insightful visualizations. If you want to leverage Python for data analytics to uncover hidden insights, streamline your BI, and drive better outcomes, contact the data analysis experts in ISHIR.