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Scientific Computing with Python - Second Edition

You're reading from  Scientific Computing with Python - Second Edition

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Pages 392 pages
Edition 2nd Edition
Languages
Authors (3):
Claus Führer Claus Führer
Profile icon Claus Führer
Jan Erik Solem Jan Erik Solem
Olivier Verdier Olivier Verdier
View More author details

Table of Contents (23) Chapters

Preface Getting Started Variables and Basic Types Container Types Linear Algebra - Arrays Advanced Array Concepts Plotting Functions Classes Iterating Series and Dataframes - Working with Pandas Communication by a Graphical User Interface Error and Exception Handling Namespaces, Scopes, and Modules Input and Output Testing Symbolic Computations - SymPy Interacting with the Operating System Python for Parallel Computing Comprehensive Examples About Packt Other Books You May Enjoy References

10.3.1 Creating a dataframe from imported data

We want to organize the dataframe in such a way that the dates are used as the index of the dataframe. To be better prepared for operating with dates, we also want that the data import process automatically converts date strings to a pandas Timestamp object. Finally, you might have noted that the way the date is written in the data files is in the ISO-format YY-MM-DD format and not in the American MM-DD-YY or the European DD-MM-YY format. We can put on our wishlist that pandas automatically recognizes the date format and performs the correct conversion:

solarWatts = pd.read_csv("solarWatts.dat", 
sep=';',
index_col='Date',
parse_dates=[0], infer_datetime_format=True)

The pandas command read_csv is the central tool. It has many more parameters than we used here and carefully studying their functionalities saves a lot of programming...

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