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Importing data with pandas

There are lots of libraries out there in Python that you can use to read, transform, or write data. One of these libraries is pandas (http://pandas.pydata.org/). Pandas is an open source library and has great functionality and tools for data analysis as well as very easy-to-use data structures.

You can easily get pandas in many different ways. The best way to get pandas is to install it via conda (http://pandas.pydata.org/pandas-docs/stable/install.html#installing-pandas-with-anaconda).

“conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.” – conda website.

You can easily get conda by installing Anaconda, which is an open data science platform.

So, let's have a look and see how to use pandas in order to read advertising data samples. First off, we need to import pandas:

import pandas as pd

Next up, we can use the pandas.read_csv method in order to load our data into an easy-to-use pandas data structure called DataFrame. For more information about pandas.read_csv and its parameters, you can refer to the pandas documentation for this method (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html):

# read advertising data samples into a DataFrame

advertising_data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)

The first argument passed to the pandas.read_csv method is a string value representing the file path. The string can be a URL that includes http, ftp, s3, and file. The second argument passed is the index of the column that will be used as a label/name for the data rows.

Now, we have the data DataFrame, which contains the advertising data provided in the URL and each row is labeled by the first column. As mentioned earlier, pandas provides easy-to-use data structures that you can use as containers for your data. These data structures have some methods associated with them and you will be using these methods to transform and/or operate on your data.

Now, let's have a look at the first five rows of the advertising data:

# DataFrame.head method shows the first n rows of the data where the   
# default value of n is 5, DataFrame.head(n=5)
advertising_data.head()

Output: