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Data Analysis with Python

You're reading from   Data Analysis with Python A Modern Approach

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789950069
Length 490 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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David Taieb David Taieb
Author Profile Icon David Taieb
David Taieb
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Table of Contents (14) Chapters Close

Preface 1. Programming and Data Science – A New Toolset FREE CHAPTER 2. Python and Jupyter Notebooks to Power your Data Analysis 3. Accelerate your Data Analysis with Python Libraries 4. Publish your Data Analysis to the Web - the PixieApp Tool 5. Python and PixieDust Best Practices and Advanced Concepts 6. Analytics Study: AI and Image Recognition with TensorFlow 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis 10. The Future of Data Analysis and Where to Develop your Skills A. PixieApp Quick-Reference Other Books You May Enjoy Index

SampleData – a simple API for loading data

Loading data into a Notebook is one of the most repetitive tasks a data scientist can do, yet depending on the framework or data source being used, writing the code can be difficult and time-consuming.

Let's take a concrete example of trying to load a CSV file from an open data site (say https://data.cityofnewyork.us) into both a pandas and Apache Spark DataFrame.

Note

Note: Going forward, all the code is assumed to run in a Jupyter Notebook.

For pandas, the code is pretty straightforward as it provides an API to directly load from URL:

import pandas
data_url = "https://data.cityofnewyork.us/api/views/e98g-f8hy/rows.csv?accessType=DOWNLOAD"
building_df = pandas.read_csv(data_url)
building_df

The last statement, calling building_df, will print its contents in the output cell. This is possible without a print because Jupyter is interpreting the last statement of a cell calling a variable as a directive to print it...

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