Here are the top 5 libraries you need to know for Data Analysis in 2022
Level up your data game!
Which language to opt for?
Data analysis has become the to go option to break into the tech field for new comers. R and Python are the choices available for data geeks and most of them go with Python because it has a power pack support of libraries as compared to R. Moreover, considering the popularity of python it is wise move to learn python as it gives you a edge over other. In flowing article top five libraries of Python will be discussed for Data analysis so familiarize your self with it to break into the world of data analysis .
1.Pandas and Numpy: Data manipulation and Analysis
Pandas and numpy can be considered as core fundamental in python for data analysis. Pandas enable you to perform basic operations on tabular data and has powerful features to manipulate the data just like we do in Google Sheet and Excel. In addition, it also supports the very basic level of plotting. While, Numpy is also known as Numerical Python and has rock solid support for arrays, matrix and numeric data types in python. Mathematical functions, random number generators, linear algebra routines, Fourier transforms and advance array operations are some of it core features it offers.
Resources:
- Numpy Docs: The official documentation for Numpy library where everything is available.
- Pandas Docs: The official documentation for Pandas library where you can find guides and examples.
- Practice Notebook Pandas:
- Practice Notebook Numpy
2.Matplotlib: Fundamental plotting and visualizations
Matplotlib is mainly used for plotting and visualizations in python. It offers wide rang of plots from bar chart to stream plot so it is comprehensive library for creating static, animated, and interactive visualizations in Python. It also has module named 'Pylab' by which MATLAB like plotting can be achieved.
Resources:
- Matplotlib Docs
- Practice Notebook Matplotlib -Data Analysis With Python: Course offered by freeCodeCamp
3.Statsmodels: Statistic models and tests
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. It has extensive support for descriptive statistics, inferential statistics and statistical tests. Basically, this module will full fill all statical needs for your project.
Resources:
- Statsmodels: Official documentation
4.Seaborn: Statistical plotting and visualizations
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. It is statistical oriented library which support key statical plots like heat maps, displot, violine plot and much more. It aims to make visualization a central part of exploring and understanding complex datasets.
Resources:
5.Plotly: Data Apps and Dashboards
It mainly aims at building, scaling, and deploying data apps in Python. Major simulation in some applications driven by AI/ML can be displayed in Dash Apps as conventional BI tools don't have the features for the AI and ML. All of the plots built using plotly are even the basic ones are interactive and by combining more such graphs interactive dashboard can also be build easily using python.
Resources:
Conclusion
To sum up, python has grate libraries to analyze the data and with libraries mentioned above clear and concise insights can easily be generated with out using and BI tools such as Power BI and Tableau.