6 Pandas Functions for a Quick Exploratory Data Analysis
The first step into exploring your data
The most important ingredient of a data science project or product is the data. The better we understand the data the more accurate and robust the end product is.
This is the reason why a substantial amount of time in a project is spent on data cleaning and exploratory data analysis.
Pandas being one of the most widely used data analysis and manipulation libraries provides a rich selection of functions to help explore the data.
An exploratory data analysis process is likely to be very detailed depending on the task. How you approach the data changes according to its dynamics. However, there are some fundamental operations being done in most cases.
In this article, we will go over 5 Pandas functions to perform a quick exploratory data analysis.
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I have previously created a sales dataset filled with mock data. Let’s start with creating a Pandas data frame from this dataset. Feel free to download it and follow along.
import pandas as pdsales = pd.read_csv("sales.csv")sales.head()
