Python Data Science Essentials - Learn the fundamentals of by Alberto Boschetti

By Alberto Boschetti

Key Features

  • Quickly get conversant in information technology utilizing Python
  • Save time - and energy - with the entire crucial instruments explained
  • Create potent facts technological know-how tasks and keep away from universal pitfalls with assistance from examples and tricks dictated through experience

Book Description

The publication starts off by means of introducing you to developing your crucial info technology toolbox. Then it's going to consultant you throughout all of the facts munging and preprocessing stages. this may be performed in a fashion that explains all of the center info technology actions regarding loading info, reworking and solving it for research, in addition to exploring and processing it. ultimately, it's going to entire the evaluate through featuring you with the most computing device studying algorithms, the graph research technicalities, and all of the visualization tools that could make your existence more straightforward in providing your results.

In this walkthrough, dependent as a knowledge technology venture, you'll always be observed through transparent code and simplified examples that will help you comprehend the underlying mechanics and real-world datasets.

What you are going to learn

  • Set up your facts technological know-how toolbox utilizing a Python medical surroundings on home windows, Mac, and Linux
  • Get information prepared on your information technology project
  • Manipulate, repair, and discover facts that allows you to resolve information technological know-how problems
  • Set up an experimental pipeline to check your info technology hypothesis
  • Choose the simplest and scalable studying set of rules in your facts technology tasks
  • Optimize your laptop studying types to get the simplest performance
  • Explore and cluster graphs, profiting from interconnections and hyperlinks on your data

About the Authors

Alberto Boschetti is a knowledge scientist with services in sign processing and facts. He holds a PhD in telecommunication engineering and at the moment lives and works in London. In his paintings tasks, he faces demanding situations concerning typical language processing (NLP), computer studying, and probabilistic graph versions everyday.

Luca Massaron is a knowledge scientist and advertising study director who makes a speciality of multivariate statistical research, computer studying, and client perception, with over a decade of expertise in fixing real-world difficulties and producing price for stakeholders via making use of reasoning, information, info mining, and algorithms.

Table of Contents

  1. First Steps
  2. Data Munging
  3. The information technological know-how Pipeline
  4. Machine Learning
  5. Social community Analysis
  6. Visualization

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Extra info for Python Data Science Essentials - Learn the fundamentals of Data Science with Python

Sample text

If the format of the dates is not very strange, you can try the autodetection routines that specify the column that contains the date data. csv', parse_dates=[0]) fake_dataset Out: Date Temperature_city_1 Temperature_city_2 Which_destination 0 2014-09-10 80 32 40 1 1 2014-09-11 100 50 36 2 2 2014-09-12 102 55 46 1 3 2014-09-13 60 20 4 2014-09-14 60 NaN 32 3 5 2014-09-15 NaN 57 42 2 35 Temperature_city_3 3 Now, to get rid of the missing data that is indicated as NaN, replace them with a more meaningful number (let's say 50 Fahrenheit, for example).

In line [3], we use the magic function timeit to a Python assignment (x=100). The timeit function runs this instruction many times and stores the computational time needed to execute it. Finally, it prints the average time that was taken to run the Python function. We complete the overview with a list of all the possible IPython special functions by running the helper function quickref, as shown in line [4]. [ 14 ] Chapter 1 As you noticed, each time we use IPython, we have an input cell and optionally, an output cell, if there is something that has to be printed on stdout.

The pandas library offers similar functionality (and functions) to load MS Excel, HDFS, SQL, JSON, HTML, and Stata datasets. Since they're not used often, the understanding of how one can load and handle them is left to you, who can refer to the verbose documentation available on the website. A basic example on how to load an SQL table is available in the code that accompanies the book. Finally, pandas DataFrames can be created by merging series or other list-like data. '}) my_own_dataset Out: 0 Col1 Col2 Col3 Col4 0 1 1 Hello World!

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