data-analysislisted

# Data Analysis in EverFern ## MANDATORY WORKFLOW **Step 1**: First, detect the data file type and use the appropriate skill to read and understand the data structure: - For `.csv` files: use the `csv` skill - For `.xlsx` files: use the `xlsx` skill - For `.pdf` files: use the `pdf` skill - For `.json` files: use the `json` skill - For `.docx` files: use the `docx` skill - For plain text: use the `txt` skill **Step 2**: Then use this data-analysis skill to: - Perform statistical analysis - Clean and transform data - Generate visualizations **Step 3**: Finally, ALWAYS use the 'frontend-design' and 'charts' skills to: - Generate an interactive HTML dashboard directly - Present results using ApexCharts (as defined in the `charts` skill) - Create charts that the user can interact with This 3-step workflow is MANDATORY for any data visualization task. ## Overview EverFern leverages Python for high-performance data analysis. This allows for complex operations that exceed simple text-based processing. ## Key Libraries - **pandas**: Primary tool for tabular data manipulation (DataFrames). - **numpy**: Numerical calculations and array operations. - **matplotlib/seaborn**: Data visualization and plotting. ## Common Workflows ### 1. Data Loading and Cleaning ```python import pandas as pd import numpy as np # Load data (use absolute R-string paths on Windows) df = pd.read_csv(r'C:\Users\Username\Downloads\data.csv') # Handle missing values df['category'] = df['category'].fi