#### Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

## Extracting statistics from time series data

One of the main reasons that we want to analyze time series data is to extract interesting statistics from it. This provides a lot of information regarding the nature of the data. In this recipe, we will take a look at how to extract these stats.

### How to do it…

1. Create a new Python file, and import the following packages:

```import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from convert_to_timeseries import convert_data_to_timeseries```
2. We will use the same text file that we used in the previous recipes for analysis:

```# Input file containing data
input_file = 'data_timeseries.txt'```
3. Load both the data columns (third and fourth columns):

```# Load data
data1 = convert_data_to_timeseries(input_file, 2)
data2 = convert_data_to_timeseries(input_file, 3)```
4. Create a pandas data structure to hold this data. This dataframe is like a dictionary that has keys and values:

`dataframe = pd.DataFrame({'first': data1, 'second': data2})`
5. Let's start extracting some...