Goketech / Intermediate-Python

Intermediate Python is a repository that provides resources and examples aimed at developers who have a basic understanding of Python and want to advance their skills to an intermediate level. The repository covers a wide range of topics, including data manipulation, visualization, file handling, regular expressions, and error handling.

Intermediate Python Concepts

The concepts covered in this repository, including Pandas, Matplotlib, NumPy, dictionaries, control flow, functions, classes, file handling, regular expressions, and error handling, are all essential tools in the toolkit of any data scientist or developer working with Python. Pandas is a powerful library for data manipulation and analysis, while Matplotlib provides a wide range of options for data visualization. NumPy is a critical library for numerical computing, and dictionaries are a useful data structure for storing and manipulating data in key-value pairs. Control flow structures, including loops and conditional statements, allow developers to control the flow of execution in their programs, while functions and classes provide a modular approach to programming. File handling is an essential skill for working with data stored on a computer's file system, and regular expressions provide a powerful way to search for and manipulate text. Finally, error handling is crucial for ensuring that programs run smoothly and handle unexpected issues gracefully. By mastering these concepts, developers can build robust, efficient, and flexible Python programs for a wide range of applications.

Getting Started

To get started with these concepts, you will need to have Python installed on your computer. You can download the latest version of Python from the official website:

         https://www.python.org/downloads/. Additionally, you will need to install the following libraries:

1. Pandas :

     https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html
   

2. Matplotlib:

     https://matplotlib.org/stable/users/installing.html
   

Once you have installed Python and the required libraries, you can clone this repository to your local machine using the following command:

Copy code to get clone

         git clone https://github.com/<your-username>/intermediate-python-concepts.git

Concepts Covered

The following intermediate level Python concepts are covered in this repository:

Concepts Covered in This Repository

Concept	Description
Pandas	A data manipulation library that provides data structures for efficiently storing and analyzing data.
Matplotlib	A data visualization library that provides a wide range of graphs, charts, and plots.
NumPy	A numerical computing library that provides support for large, multi-dimensional arrays and matrices.
Dictionaries	A collection data type that stores data as key-value pairs.
Control Flow	The order in which statements are executed in a program, including loops, conditional statements, and functions.
Functions	A block of code that performs a specific task and can be called multiple times throughout a program.
Classes	A blueprint for creating objects that define the properties and methods of a particular type of object.
File Handling	The process of reading from or writing to files on a computer's file system.
Regular Expressions	A pattern used to match and manipulate text in strings.
Error Handling	The process of anticipating, detecting, and handling errors that may occur in a program.

Table of Functions for Pandas and Matplotlib

Pandas	Matplotlib
pd.read_csv(): Reads data from a CSV file and returns a DataFrame object.	plt.plot(): Creates a line plot.
pd.DataFrame(): Creates a new DataFrame object from a dictionary, list of dictionaries, or other data structures.	plt.scatter(): Creates a scatter plot.
df.head(): Returns the first n rows of a DataFrame.	plt.bar(): Creates a bar plot.
df.tail(): Returns the last n rows of a DataFrame.	plt.hist(): Creates a histogram.
df.info(): Returns a summary of a DataFrame, including the data types of each column and the number of non-null values.	plt.pie(): Creates a pie chart.
df.describe(): Returns descriptive statistics for each column in a DataFrame.	plt.boxplot(): Creates a box and whisker plot.
df.groupby(): Groups a DataFrame by one or more columns and applies a function to each group.	plt.table(): Creates a table object in the current figure.
df.merge(): Merges two DataFrames on one or more columns.	plt.subplot(): Creates a subplot.

Dictionaries:

1. Creating dictionaries
2. Accessing and modifying dictionary items
3. Looping through dictionaries

Contributing

If you would like to contribute to this repository, feel free to create a pull request. Contributions are welcome, whether it's adding more concepts or improving the existing ones.

License

This repository is licensed under the MIT license. See LICENSE for more information.

Acknowledgments

1. The Pandas documentation: https://pandas.pydata.org/docs/
2. The Matplotlib documentation: https://matplotlib.org/stable/contents.html
3. The Python documentation: https://docs.python.org/3/