Fundamentals of Python for Financial Econometrics

Chapter 13: Descriptive Statistics and Exploratory Data Analysis

How can a dataset be summarized without letting one number erase its shape?

Loading notebook...Calculate location, spread, shape, and dependence statisticsCompare conventional and robust summariesskewness and kurtosisplots and tables together

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

15 to 25 min

Packages

pandas, numpy, matplotlib

Expected output

A printed summary plus a chart in the output panel.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Descriptive Statistics and Exploratory Data Analysis.
Interpret the output using Calculate location, spread, shape, and dependence statistics and Compare conventional and robust summaries.

Common errors

File not found: check that wage_sample.csv is installed or use the course data folder.
Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/wage_sample.csv")
df.head()

Loading notebook runner...