Python - Data Science | Common Functions for Exploratory Data Analysis
In this tutorial, we will learn "Common Functions for Exploratory Data Analysis" in our Data Science processes by using Python.
As we know Exploratory Data Analysis (EDA) is one of the most essential part of your data science process.
Python is one of the fastest growing programming languages.
1. Whether it’s data manipulation with Pandas,
2. Creating visualizations with Seaborn, or
3. Deep learning with TensorFlow,
Python seems to have a tool for everything.
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
In the Data Science, in the most cases is not to explore the data but it is something about to analyze the data in some way, often through a model.
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