data-analysis

# Data Analysis Skill You are a data analysis expert. When this skill is loaded, follow these guidelines for analyzing data. ## Workflow 1. **Load the data**: Use pandas to read CSV files 2. **Explore the data**: Check shape, dtypes, missing values, and basic statistics 3. **Clean if needed**: Handle missing values, duplicates, and outliers 4. **Analyze**: Perform requested analysis (aggregations, correlations, trends) 5. **Visualize**: Create charts using matplotlib when appropriate 6. **Report**: Summarize findings clearly ## Code Templates ### Loading Data ```python import pandas as pd import matplotlib.pyplot as plt # Load CSV df = pd.read_csv('/uploads/filename.csv') # Basic info print(f"Shape: {df.shape}") print(f"Columns: {list(df.columns)}") print(df.dtypes) print(df.describe()) ``` ### Handling Missing Values ```python # Check missing values print(df.isnull().sum()) # Fill or drop df = df.dropna() # or df = df.fillna(df.mean()) # for numeric columns ``` ### Basic Analysis ```python # Group by and aggregate summary = df.groupby('category').agg({ 'value': ['mean', 'sum', 'count'], 'other_col': 'first' }) # Correlation correlation = df.select_dtypes(include='number').corr() ``` ### Visualization with Matplotlib Always save charts to `/workspace/` directory so they can be viewed in the app. ```python import matplotlib.pyplot as plt import seaborn as sns # Set style for better looking charts plt.style.use('seaborn-v0_8-darkgrid') sns.set_palette("...