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Exploratory Data Analysis with Python — Part 1
A template to follow to get you started analyzing data with Python and Pandas
Data Science has no recipe. Don’t think that there’s a template you can follow for each and every dataset. There is not.
What I am going to present in this series of posts is just a suggestion, a place to start. From there, obviously, you will be dragged by your data to perform other checks that will fit the needs of your project. It should not be understood as a model to follow or a set of rules, but simply something to get you moving and helping you to extract the first insights from your data.
Summary
Exploratory Data Analysis (EDA) is the art of understanding your dataset and extracting the first insights from it, so you can prepare it for the next phases of the Data Science flow — e.g. data cleaning, data formatting, feature engineering and modeling.
These are the topics included in this series:
- Libraries import and loading the data
- Checking data types
- Looking for null or missing values
- Descriptive statistics profiling
- Univariate Analysis
- Correlations
