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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Idris
4 (4)
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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: Idris

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 (16 chapters)
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13
A. Key Concepts
15
C. Online Resources

Comparing Bottleneck to NumPy functions

Bottleneck is a set of functions inspired by NumPy and SciPy, but written in Cython with high performance in mind. Bottleneck provides separate Cython functions for each combination of array dimensions, axis, and data type. This is not shown to the end user, and the limiting factor for Bottleneck is to determine which Cython function to execute. Install Bottleneck as follows:

$ pip3 install bottleneck

We will compare the execution times for the numpy.median() and scipy.stats.rankdata() functions in relation to their Bottleneck counterparts. It can be useful to determine the Cython function manually before using it in a tight loop or frequently called function.

This program is given in the bn_demo.py file in this book's code bundle:

import bottleneck as bn 
import numpy as np 
import timeit 
 
setup = ''' 
import numpy as np 
import bottleneck as bn 
from scipy.stats import rankdata 
 
np.random.seed(42) 
a = np.random.randn(30) ...

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