The document discusses Python modules, emphasizing their importance in organizing code and functionality, with distinctions between built-in and user-defined modules. It also highlights how to import modules and their various capabilities, including data manipulation, statistical analysis, and data visualization using the pandas library. Additionally, it covers applications of pandas in data cleaning, exploration, preparation, and analysis, along with its integration with other libraries for enhanced data insights.

1. Unit-5

2. Modules
• In programming, a module is a piece of software that has a specific
functionality.
• Modules refer to the Python file.
• In Python, large code is divided into small modules
• contains Python code like Python statements, classes, functions,
variables, etc.
• A file with Python code is defined with extension.py
• For example: In Test.py, where the test is the module name.

3. Types of modules
• Built-in modules
• Built-in modules come with default Python installation
• Most significant advantages is its rich library support that contains lots of
built-in modules.
Ex: datetime, os, math, sys, random, etc.
• User-defined modules
• user defines or create modules
• We can create our own module, which contains classes, functions, variables, etc., as per our
requirements.

4. Import Modules
• import statement is used to import the whole module
• Syntax: import module name
• import keyword imports both built-in and user-defined modules.
• Example: module-math

5. Import multiple modules
• To use more than one module, then we can import multiple modules.
• Syntax: import module1,module2,.. moduleN

6. Import specific classes or functions from a module
• To import particular classes or functions, we can use the
from...import statement.
• Imports individual attributes and methods directly into the program.
• Syntax:
• from <module_name> import <name(s)>
Output:
120

7. Import with renaming a module
• To use the module with a different name, we can use
from..import…as statement.
• Syntax
• Example: random.randrange(start, stop, step)

8. Output: 41
Random Module Functions
https://pynative.com/python/random/

9. Import All Names
• Python Math Module Functions:
https://www.w3schools.com/python/module_math.asp
https://docs.python.org/3/library/math.html

10. Create Module
• Creating a module in python is similar to writing a simple python
script using the .py extension.
• Example:
• To make a program for a calculator. There will be operations like
addition, subtraction, multiplication, division, etc.

11. Example on creating module
• Calc.py

12. Use Python Modules created
• import keyword to incorporate the module into our program.
• from keyword is used to get only a few or specific function
import all the functions using the
asterisk(*) and we can simply mention the
function name to get the results.

13. Pandas
• Pandas is a powerful Python library that is specifically designed to
work on data frames that have "relational" or "labeled" data.
• Its aim aligns with doing real-world data analysis using Python.
• Hence, this Python package works well for data manipulation,
operating a dataset, exploring a data frame, data analysis, and
machine learning-related tasks
• it simplifies the task related to data frames

14. • Import datasets - available in the form of spreadsheets, comma-separated values (CSV) files, and
more.
• Data cleansing - dealing with missing values and representing them as NaN, NA, or NaT.
• Size mutability - columns can be added and removed from DataFrame and higher-dimensional
objects.
• Data normalization – normalize the data into a suitable format for analysis.
• Data alignment - objects can be explicitly aligned to a set of labels.
Intuitive merging and joining data sets – we can merge and join datasets.
• Reshaping and pivoting of datasets – datasets can be reshaped and pivoted as per the need.
• Efficient manipulation and extraction - manipulation and extraction of specific parts of extensive
datasets using intelligent label-based slicing, indexing, and subsetting techniques.
• Statistical analysis - to perform statistical operations on datasets.
• Data visualization - Visualize datasets and uncover insights.

15. Applications
The most common applications of Pandas are as follows:
• Data Cleaning: Pandas provides functionalities to clean messy data, deal with incomplete or inconsistent data, handle missing values,
remove duplicates, and standardise formats to do effective data analysis.
• Data Exploration: Pandas easily summarise statistics, find trends, and visualise data using built-in plotting functions, Matplotlib, or Seaborn
integration.
• Data Preparation: Pandas may pivot, melt, convert variables, and merge datasets based on common columns to prepare data for analysis.
• Data Analysis: Pandas supports descriptive statistics, time series analysis, group-by operations, and custom functions.
• Data Visualisation: Pandas itself has basic plotting capabilities; it integrates and supports data visualisation libraries like Matplotlib,
Seaborn, and Plotly to create innovative visualisations.
• Time Series Analysis: Pandas supports date/time indexing, resampling, frequency conversion, and rolling statistics for time series data.
• Data Aggregation and Grouping: Pandas groupby() function lets you aggregate data and compute group-wise summary statistics or apply
functions to groups.
• Data Input/Output: Pandas makes data input and export easy by reading and writing CSV, Excel, JSON, SQL databases, and more.
• Machine Learning: Pandas works well with Scikit-learn for data preparation, feature engineering, and model input data.
• Web Scraping: Pandas may be used with BeautifulSoup or Scrapy to parse and analyse structured web data for web scraping and data
extraction.
• Financial Analysis: Pandas is commonly used in finance for stock market data analysis, financial indicator calculation, and portfolio
optimisation.
• Text Data Analysis: Pandas' string manipulation, regular expressions, and text mining functions help analyse textual data.
• Experimental Data Analysis: Pandas makes manipulating and analysing large datasets, performing statistical tests, and visualising results