Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)

Introduction to the cbsodata Python package

To install the cbsodata Python package, perform the following steps:

  1. We can use one of the following commands:
conda install cbsodata 
pip install cbsodata 

For more detailed instructions about how to install the Python package, please see Chapter 6, Managing Packages:

  1. The next program shows one example of using the package:
import pandas as pd 
import cbsodata as cb 
name='82070ENG' 
data = pd.DataFrame(cb.get_data(name)) 
print(data.head()) 
info=cb.get_info(name) 
print(info['Title']) 
  1. The corresponding output is shown in the following screenshot:

The last line in the screenshot gives the name of the dataset. In the previous example, we used the dataset with the name 82070ENG.

  1. To find out all the names of lists, we use the get_table_list() function; see the following code:
import cbsodata as cb 
list=cb...