The Top 5 Data Analysis Libraries You Need To Know In 2022
Which language to opt for?
Data analysis has become the go option to break into the tech field for newcomers. R and Python are the choices available for data geeks. Most 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 an edge over R. Let’s take a look at the top 5 libraries you need to know in 2022.
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 in addition to powerful features. 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 its 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 fulfill all the 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. Seaborn 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. By combining such graphs, interactive dashboard can also be build easily using python.
Resources:
Conclusion
Python has great libraries to analyze data. With the libraries we’ve mentioned, clear and concise insights can easily be generated. Check out our data analysis learning plan to see which order to lean these in.
