Explore Data

SAS

Code examples, templates, and projects for SAS data analysis.

      /* Example SAS code to import and analyze data */
      proc import datafile="data.csv" 
          out=mydata 
          dbms=csv replace;
          getnames=yes;
      run;

      proc means data=mydata;
          var age income;
      run;

      proc freq data=mydata;
          tables gender education;
      run;
    

Data import and export templates
Statistical analysis procedures
Data visualization macros
Custom reporting templates

Browse SAS Resources

Python

Python scripts, data analysis, and machine learning examples.

      # Example Python data analysis with pandas
      import pandas as pd
      import matplotlib.pyplot as plt
      
      # Load data
      df = pd.read_csv('data.csv')
      
      # Quick data exploration
      print(df.describe())
      
      # Simple visualization
      df.plot(kind='bar', x='category', y='value')
      plt.title('Value by Category')
      plt.show()
    

Data cleaning and preprocessing scripts
Pandas and NumPy analysis examples
Scikit-learn machine learning templates
Data visualization with Matplotlib and Seaborn

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Django

Django projects and web development tutorials for data applications.

      # views.py example for a data dashboard
      from django.shortcuts import render
      import pandas as pd
      
      def dashboard(request):
          # Load and process data
          data = pd.read_csv('data/sales.csv')
          summary = data.groupby('region').sum().reset_index()
          
          context = {
              'regions': summary['region'].tolist(),
              'sales': summary['sales'].tolist(),
          }
          return render(request, 'dashboard.html', context)
    

Data-driven web application templates
Dashboard creation tutorials
API development for data services
Database integration examples

More Django resources coming soon!

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JupyterLab

Notebooks and visualization examples using JupyterLab.

      # Cell in a Jupyter notebook
      import pandas as pd
      import seaborn as sns
      
      # Load dataset
      df = pd.read_csv('survey_results.csv')
      
      # Create visualization
      plt.figure(figsize=(12, 6))
      sns.boxplot(x='department', y='satisfaction_score', data=df)
      plt.title('Employee Satisfaction by Department')
      plt.xticks(rotation=45)
      plt.tight_layout()
    

Interactive data exploration notebooks
Statistical analysis templates
Advanced visualization notebooks
Integrated reporting examples

Browse Jupyter Notebooks

Results

Templates and best practices for building HTML data tables and visualizations.

      
      <table class="data-table">
        <thead>
          <tr>
            <th>ID</th>
            <th>Name</th>
            <th>Category</th>
            <th>Value</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>001</td>
            <td>Product A</td>
            <td>Electronics</td>
            <td>$1,245.00</td>
          </tr>
          <!-- More rows -->
        </tbody>
      </table>
    

Responsive data table templates
Interactive chart examples with D3.js
Export options for reports
Accessibility-focused data presentations

View Combined HTML Table