Python Advanced- DataFrame

Hope you have learnt Series data structure from my previous page ; if not then please read about that from the side bar icon located at top right most.
Today we will learn about the second important data structure- DataFrame

DataFrame is a tabular data structure in which data is laid out in rows and column format (similar to a CSV and SQL file), but it can also be used for higher dimensional data sets.
The DataFrame object can contain homogenous and heterogenous values, and can be thought of as a logical extension of Series data structures.
In contrast to Series, where there is one index, a DataFrame object has one index for column and one index for rows. This allows flexibility in accessing and manipulating data.
For more details about DataFrame please visit here pandas DataFrame

DataFrame example with code snippet:-

Please note here If a column is passed with no values, it will simply have NaN values.

Now let's do some search operations on our dataframe using loc/iloc and understand the difference between loc & iloc with below examples:-

I hope you have now understood the loc & iloc uses clearly.

In order to access a column, simply mention the column name as below-

In order to add additional columns follow below example:-

You can pass a number of data structures to DataFrame such as a ndarray, lists, dict, Series, and another DataFrame.
You can also reindex to confirm to data to a new index. Reindexing is a powerful feature that allows you to access data in a number of different ways, and also to confirm data to some new time series or other index.

You can use NumPy functions inside DataFrame objects also-

Sorting in DataFrame:-

Let us look at the example of  company stock price data from Yahoo Finance website . To extract data from these websites, you need to use pandas.io.data-

So you see there are lots of things that we can do with DataFrame. I have given the input with output also so that you can understand easily.
Please feel free to comment if you face any issue.