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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Statistics with pandas DataFrames


The pandas DataFrame has a dozen statistical methods. The following table lists these methods along with a short description:

Method

Description

describe

This method returns a small table with descriptive statistics.

count

This method returns the number of non-NaN items.

mad

This method calculates the mean absolute deviation, which is a robust measure similar to the standard deviation.

median

This method returns the median. This is equivalent to the value at the 50th percentile.

min

This method returns the lowest value.

max

This method returns the highest value.

mode

This method returns the mode, which is the most frequently occurring value.

std

This method returns the standard deviation, which measures dispersion. It is the square root of the variance.

var

This method returns the variance.

skew

This method returns skewness. Skewness is indicative of the distribution symmetry.

kurt

This method returns kurtosis...

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