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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Expanding on panda data frames in Jupyter

There are more functions built-in for working with data frames than we have used so far. If we were to take one of the data frames from a prior example in this chapter, the Titanic dataset from an Excel file, we could use additional functions to help portray and work with the dataset.

As a repeat, we load the dataset using the script:

import pandas as pd
df = pd.read_excel('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.xls')

We can then inspect the data frame using the info function, which displays the characteristics of the data frame:

df.info()  

Some of the interesting points are as follows:

  • 1309 entries
  • 14 columns
  • Not many fields with valid data in the body column—most were lost
  • Does give a good overview of the types of data involved

We can also use the describe function, which gives us a statistical...

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