Skip to main content
Engineering LibreTexts

15: Exploratory Data Analysis- univariate

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)

    The fancy term “Exploratory Data Analysis” (EDA) basically just means getting acquainted with your data. After importing a new data set into Python, the first thing you normally do is poke around to get an idea of what it contains. You may not even know what questions you eventually want to ask – let alone what the answers are – but sizing up the data is a necessary precursor to those activities.

    In this chapter, we’ll learn some basic EDA techniques for univariate data, which is really all we’ve studied so far. “Univariate” means to consider just one variable at a time, rather than possible relationships between variables. A single (one-dimensional) NumPy array or Pandas Series is a univariate data set, if you treat it in isolation. As it turns out, there’s quite a few interesting things you can do with even something that simple.

    First, we’ll look at summary statistics, which are a way to capture the general features of a data set so you can see the forest instead of just a bunch of trees. Which type of summary information is appropriate depends on whether you’re dealing with categorical or numeric data.

    15: Exploratory Data Analysis- univariate is shared under a CC BY-SA license and was authored, remixed, and/or curated by Stephen Davies ( .

    • Was this article helpful?