Explore data with exploratory data analysis techniques from "summary" of Python for Data Analysis by Wes McKinney
Exploratory data analysis (EDA) is an essential step in the data analysis process. It involves examining and understanding the structure of a dataset, uncovering patterns, identifying anomalies, and generating hypotheses. EDA helps to gain insights into the data and inform the next steps in the analysis.
There are various techniques and tools available for conducting exploratory data analysis. One common approach is to start by summarizing the data using descriptive statistics such as mean, median, standard deviation, and percentiles. These summary statistics provide an overview of the data distribution and help in identifying outliers or missing values.
Visualization is another powerful tool for exploring data. Plots such as histograms, scatter plots, box plots, and heat maps can reveal patterns, trends, and relationships in the data that may not be apparent from summary statist...
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