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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. The Pandas Primer 4. Statistics and Linear Algebra 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

Basic descriptive statistics with NumPy

In this book, we will try to use as many varied datasets as possible. This depends on the availability of the data. Unfortunately, this means that the subject of the data might not exactly match your interests. Every dataset has its own quirks, but the general skills you acquire in this book should transfer to your own field. In this chapter, we will load datasets from the statsmodels library into NumPy arrays in order to analyze the data.

Mauna Loa CO2 measurements is the first dataset we shall use from the statsmodels datasets package. The following code loads the dataset and prints basics descriptive statistical values:

import numpy as np
from scipy.stats import scoreatpercentile
import pandas as pd

data = pd.read_csv("co2.csv", index_col=0, parse_dates=True) 
co2 = np.array(data.co2) 
 
print("The statistical values for amounts of co2 in atmosphere: \n") 
print("Max method", co2.max()) 
print("Max function&quot...
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