Resampling describes the process of frequency conversion over time series data. It is a helpful technique in various circumstances as it fosters understanding by grouping together and aggregating data. It is possible to create a new time series from daily temperature data that shows the average temperature per week or month. On the other hand, real-world data may not be taken in uniform intervals and it is required to map observations into uniform intervals or to fill in missing values for certain points in time. These are two of the main use directions of resampling: binning and aggregation, and filling in missing data. Downsampling and upsampling occur in other fields as well, such as digital signal processing. There, the process of downsampling is often called decimation and performs a reduction of the sample rate. The inverse process is called interpolation, where the sample rate is increased. We will look at both directions from a data analysis angle.
Getting Started with Python Data Analysis
Getting Started with Python Data Analysis
Overview of this book
Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It’s often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.
With this book, we will get you started with Python data analysis and show you what its advantages are.
The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.
Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.
Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.
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Table of Contents (15 chapters)
Getting Started with Python Data Analysis
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introducing Data Analysis and Libraries
NumPy Arrays and Vectorized Computation
Data Analysis with Pandas
Data Visualization
Time Series
Interacting with Databases
Data Analysis Application Examples
Machine Learning Models with scikit-learn
Index
Customer Reviews