Data Wrangling with Python. Jacqueline Kazil, Katharine Jarmul

Data Wrangling with Python


Data.Wrangling.with.Python.pdf
ISBN: 9781491948811 | 486 pages | 13 Mb


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Data Wrangling with Python Jacqueline Kazil, Katharine Jarmul
Publisher: O'Reilly Media, Incorporated



Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython eBook: Wes McKinney: Amazon.fr: Boutique Kindle. Previous Previous Module: Python I - Programming and Language basics Next Next: Python Connecting to MySQL. Use thousands=',' argument for numbers that contain a comma. Python basics Now that you are all setup to run Python on your computer, let's go over some basics. Good Tables is a Python package for validating tabular data through a processing pipeline. In [1]: from pandas import read_csv In [2]: d = read_csv('data.csv', thousands=','). Python Reading and Writing CSVs. If you work with huge quantities of data, I would encourage you to explore PyTables and h5py to see how they can suit your needs. Trifacta's self-service data preparation platform helps data scientists and analysts discover, wrangle, and visualize complex data quickly and intuitively. PrefaceWho should read this book Who should not read this book How this book is organized What is data wrangling? Python data structures (lists and dicts). But we're not - we're having another fantastic Python North East Meetup! Data Wrangling Outline because the data, even coming from DB-backed sites, is often “dirty” Python's urllib and urllib2 are pure-python libraries for doing. Digging into data does not have to be painful. Let us mix Python analytics tools, add a dash of Machine Learning Algorithmics & work on Data Science Analytics competitions hosted by Kaggle.





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