Introduction to Pandas library

Pandas is a newer package built on top of Numpy and provides efficient implementation of a Dataframe. Dataframes are essentially multidimensional arrays with attached row and column labels, and often with heterogenous types and/or missing data.

Installing pandas on windows system:

We will start the coding session by first importing the libraries:

The Pandas Series Object:

A Pandas Series is a one-dimensional array of indexed data. It can be created from a list of array as follows:

The Series wraps both a sequence of values and a sequence of indices, which we can access with the values and index attributes. Like a Numpy array, data can be accessed by the associated index via the square bracket.

The main difference between Numpy and Pandas is that, Numpy array has an implicitly defined integer index used to access the values, the Pandas Series has an explicitly defined index associated with the values.

The above statement can be explained in a detailed way with the example below:

Here, the indices are explicitly declared which are different than the normal numeric used.

Series as specialized dictionary:

A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a Series is a structure that maps types keys to a set of typed values.

By default, a Series will be created where the index is drawn from the sorted keys.

Pandas DataFrame object:

A DataFrame is an analog of a two-dimensional array with both flexible row indices and column indices.

Data Selection in Series:

2. Series as one-dimensional array:

There are two types of indexing. 1. Implicit 2. Explicit

Let us understand with an example

Because of this potential confusion in the case of integer indexes, pandas provides some special indexer attributes that explicitly expose certain indexing schemas.

First, the loc attribute allows indexing and slicing that always references the explicit index:

The iloc attribute allows indexing and slicing that always references the implicit Python-style index

In this tutorial, we studied about the basics of Pandas.

In order to access the jupyter notebook of this tutorial, kindly follow the below link:

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