We regularly encounter empty fields in data records. It's best that we accept this and learn how to handle this kind of issue in a robust manner. Real data can not only have gaps-it can also have wrong values, because of faulty measuring equipment, for example. In Pandas, missing numerical values will be designated as NaN
, objects as None
, and the datetime64
objects as NaT
. The outcome of arithmetic operations with NaN
values is also NaN
. Descriptive statistics methods, such as summation and average, behave differently. As we observed in an earlier example, in such a case, NaN
values are treated as zero values. However, if all the values are NaN
during, say, summation, the sum returned is still NaN
. In aggregation operations, NaN
values in the column that we group are ignored. We will again load the WHO_first9cols.csv
file into a DataFrame. Remember that this file contains empty fields. Let's only select the first three rows, including the headers of the Country
and...
Python Data Analysis - Second Edition
By :
Python Data Analysis - Second Edition
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Overview of this book
Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.
With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.
The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Python Libraries
NumPy Arrays
The Pandas Primer
Statistics and Linear Algebra
Retrieving, Processing, and Storing Data
Data Visualization
Signal Processing and Time Series
Working with Databases
Analyzing Textual Data and Social Media
Predictive Analytics and Machine Learning
Environments Outside the Python Ecosystem and Cloud Computing
Performance Tuning, Profiling, and Concurrency
Key Concepts
Useful Functions
Online Resources
Customer Reviews