Youssef-Mohamed1/African-Agricultural-Data-Cleaning-And-Testing-Project

# Maji Ndogo's Agriculture Integrated Project Welcome to the repository for the Maji Ndogo's Agriculture Integrated Project! This project is divided into three main parts, each focusing on different aspects of data analysis using Python and Pandas. ## Table of Contents - Project Overview - Part 1: Data Import and Cleaning - Part 2: Exploratory Data Analysis (EDA) and Visualization - Part 3: Data Validation and Hypothesis Testing - Setup and Installation - License ## Project Overview Maji Ndogo's Agriculture Integrated Project is a comprehensive data analysis project aimed at understanding and improving agricultural practices in the Maji Ndogo region. The project involves cleaning, analyzing, and validating agricultural data using Python. ### Part 1: Data Import and Cleaning #### Introduction The purpose of this part is to import the dataset from an SQLite database, clean it, and prepare it for analysis. Libraries Used: - pandas: For data manipulation and analysis. - sqlite3: For connecting to and importing data from the SQLite database. Content Data Import The dataset is imported from an SQLite database into a Pandas DataFrame. here are some functions: def load_data(db_path, query): conn = sqlite3.connect(db_path) df = pd.read_sql_query(query, conn) conn.close() return df #### Data Cleaning Steps taken to clean the data include handling missing values, correcting data types, and removing duplicates. def clean_data(df): # Handle missing values df = df.dropna() # Correct data types df['column_name'] = df['column_name'].astype('desired_type') # Remove duplicates df = df.drop_duplicates() return df #### File 1-Maji_Ndogo's_Agriculture_Integrated_Project_P1_Submission.ipynb ### Part 2: Exploratory Data Analysis (EDA) and Visualization #### Introduction The purpose of this part is to explore the dataset visually and uncover insights. ###### Libraries Used: - pandas: For data manipulation and analysis. - seaborn: For creating visualizations. …