Python Project

This repository contains multiple Python exercises focusing on data analysis, Pandas, and Jupyter notebooks. Each problem statement is solved in a separate Jupyter notebook, along with real-world datasets.

Project Structure bash Copy Edit /Python_Project/

│── README.md

│── .gitignore

│── Dataset/

│ ├── top50spotify.csv # Dataset of 50 Spotify songs

│ ├── cereal.csv # Dataset of cereals and manufacturers

│── Notebooks/

│ ├── Problem_1_Spotify.ipynb # Analysis of Spotify songs dataset

│ ├── Problem_2_Pandas_Series.ipynb # Creating and manipulating Pandas series

│ ├── Problem_3_Multiples_7_17.ipynb # Generating series with multiples

│ ├── Problem_4_Cereal_Analysis.ipynb # Cereal dataset visualization

│── requirements.txt # Required Python libraries

│── LICENSE # License details

Datasets

1. Spotify Songs Dataset (top50spotify.csv) Description: Contains 50 top songs from Spotify with multiple attributes.

Columns:

SerialNo. - Serial number of the song TrackName - Name of the track ArtistName - Name of the artist Genre - Genre of the song Energy - Energy index of the song Length - Length of the song Popularity - Popularity score 3. Cereal Dataset (cereal.csv) Description: Contains details of various cereal brands and their manufacturers.

Columns: name - Brand name of the cereal MFR - Manufacturer of the cereal rating - Quality rating of the cereal Notebook Descriptions

1. Problem 1 - Spotify Songs Analysis (Problem_1_Spotify.ipynb)
2. Objective: Analyze Spotify's top 50 songs dataset to extract insights.

Tasks Performed:

✔ Import the dataset and drop unnecessary columns

✔ Calculate the average Energy and Length of the first 10 songs

✔ Group songs by Genre and calculate total length

✔ Identify the artist with the most tracks in a single genre

3. Problem 2 - Pandas Series (Problem_2_Pandas_Series.ipynb)

Objective: Create and manipulate Pandas Series from a dictionary.

Tasks Performed:

✔ Convert the given dictionary into Pandas Series

✔ Handle missing values by replacing them with zeros

✔ Transpose the DataFrame and calculate the average for each subject

6. Problem 3 - Series of Multiples (Problem_3_Multiples_7_17.ipynb)

Objective: Generate a Pandas Series from 1 to 1000 and extract numbers divisible by 7 and 17.

Tasks Performed:

✔ Create a range of numbers from 1 to 1000

✔ Filter numbers divisible by both 7 and 17

✔ Convert the filtered values into a new Pandas Series

9. Problem 4 - Cereal Data Analysis (Problem_4_Cereal_Analysis.ipynb)

Objective: Visualize cereal quality based on manufacturer ratings.

Tasks Performed:

✔ Import the dataset and clean data if necessary

✔ Plot ratings for different manufacturers

✔ Use ticks to standardize rating range from 0-100

✔ Apply Seaborn styling for better visualization

Requirements To run this project, install the required libraries using:

bash Copy Edit pip install -r requirements.txt Main Libraries Used:

pandas numpy matplotlib seaborn How to Use This Repository Clone the repository: bash Copy Edit git clone https://github.com/YourUsername/Python_Project.git Navigate to the project directory: bash Copy Edit cd Python_Project Open Jupyter Notebook: bash Copy Edit jupyter notebook Run the required notebook inside the Notebooks/ folder.