Exploratory Data Analysis (EDA) is an approach to analyzing datasets to summarize their main characteristics, often with visual methods. It is used to understand the data, get some context regarding it, understand the variables and the relationships between them, and formulate hypotheses that could be useful when building predictive models.
EDA with seaborn and pandas
Understanding the seaborn library
Seaborn is a library that makes attractive and informative graphics that include statistics in Python. Matplotlib was used to build this library. It is also integrated with Python's data science stack. It also has support for NumPy and pandas, as well as SciPy's statistical routines and stats models.
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