Data handling using Pandas-I Series -11 important tasks

In this article, Data handling using Pandas-I you will learn about Python Pandas data structure series.

Introduction to Python Libraries

Python libraries are in-built python modules which allows to perform system related operations, IO operations, data analysis and some other standard operations. Pandas library is used for data analysis.

Introduction to Data handling using Pandas-I

Pandas word derived from PANel Data System. It becomes popular for data analysis. It provides highly optimized performance with back end source code is purely written in C or Python. It makes a simple and easy process for data analysis. 

Pandas offers two basic data structures:

1. Series
2. DataFrame

To work with pandas import pandas library and create one object like this:

import pandas as pd

Data handling using Pandas-I Series

Series is an important data structure of pandas. It represents one dimensional array, containing an array of data. It can any type of NumPy data. Basically series has two main components:

1. An Array
2. An index associated with array

Example:

Task 1 Creating Series

Series() function is used to create a series in Pandas. 

Example:

import pandas as pd
ser1=pd.Series()

An empty panda series has float64 data type.

Creating non-empty series

In non-empty series data and index will be supplied while creating series. Here data can be one of these data types:

1. A python sequence
2. An ndarray
3. A dictionary
4. A scalar value

Creating series with a python sequence (Data handling using Pandas-I)

Range function is used to generate a series with python pandas.

In the above screenshot, a series is created with float numbers.

Creating Series with ndarray (Data handling using Pandas-I)

Creating series from ndarray named nda. Array of odd number between 1 to created through range.

Creating series with dictionary

Crating series from Dictionary object and stored first three days of week in series.

Creating series with scalar value

Series created with scalar value 5.

Task 2 Specifying NaN values in the series

Specified NaN at the index 1.   

Task 3 creating series and specifying index

In the above example, two lists created for train numbers and train names. Train no list assigned as data and train name assigned as indexes.

Task 4 Creating series using arithmetic operation

In this example, series is created with a * 3 as data.

Data handling using Pandas-I Common Series attributes

Attribute	Description
Series.index	Retrieves index of a series
Series.values	Return series as ndarray
Series.dtype	Return data type of series
Series.shape	Return tuples (no.of rows) of the shape
Series.nbytes	Return no. of bytes
Series.ndim	Return no. of dimension
Series.size	Return no. of elements
Series.hasnans	Return true is there are any NaN value else false
Series.empty	Return true if the series is empty, else false

Task 5 Common series attribute Example

Task 6 Accessing elements from series

In above screenshot, I have accessed element by using its index value such as ser[2] and ser[3]. For accessing all the values using indexes you can use for loop.

Task 7 Modifying series elements

In above code, I have changed the element value with a scalar value. In python, series objects are value mutable i.e. values can be changed but size immutable i.e. can’t be changed.

Task 8 Slicing in Series (Data handling using Pandas-I)

Task 9 head() and tail() function in series (Data handling using Pandas-I)

The head() function displays n number of elements from the top in the series. In the above example, I have accessed top 3 elements. If no value is passed in the parameter then by default it will display 5 elements from the top. Similarly, the tail function will work and display n number of elements from the bottom.

Task 10 Vector and arithmetic operations on series

Here I have used different vector operations and operators to perform various tasks on series.

Task 11 reindex() and drop() methods – (Data handling using Pandas-I)

reindex() : Create a similar object but with a different order of same indexes.

drop(): Remove any entry from series.

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