Last updated on July 12, 2020
Wow, you’re here for the top Python libraries for data analysis; I sure you are. Especially if you just finished with the basic python syntax; if-else-elif statement, dictionaries, list, for loops, while loops, dictionaries, lists, and so on.
If you are aiming for the top as a data scientist or data analyst or even as a machine learning engineer, you need the knowledge of data analysis to help you with analyzing your data and finding insights from it without explicitly building a model. And this is also required as a skill by employers as they look for a data analyst to analyze their data for them.
Pay Attention: Check out our article comparing R and Python
Today, I will be talking about some top Python libraries for data analysis for you which you should try learning if you’re a beginner; But not most important to learn all these libraries if you want to become a machine learning engineer.
Top Python Libraries for Data Analysis
Numpy stands for Numerical Python, which is a library consisting of multidimensional array objects and a list of useful operations to define any mathematical equations or expressions you want. It is useful for declaring arrays and also for adding two arrays together which can’t be done in the normal python list.
Scipy stands for Scientific Python which is a library that uses NumPy for more mathematical functions. It uses NumPy arrays as the basic data structure and comes with modules for various commonly used tasks in scientific programming, including linear algebra, integration (calculus), ordinary differential equation solving, and signal processing.
Pandas stand for “Python Data Analysis Library ”. According to the Wikipedia page on Pandas. It uses a data frame for storing and manipulating datasets in order to reduce noise and also incomplete values in it. It is surely worth learning for Machine Learning Engineers.
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. It is mostly okay for data analysts to learn as it can be used to plot data that is understandable for non-professionals.
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 used for creating complex and beautiful charts from which insights can be drawn.
Now, you have the top python libraries you can use for analyzing your data before building a model for it. Data analysis is really a fun thing to do especially when you draw all these beautiful charts and generate insights from them.
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