Python Notebook v1 - Octopus Data Analytics

Enterprise Data Analyst – Complete

Dashboard

Enterprise Data Analysis Platform

Connect to multiple data sources, analyze with Python, and create visualizations. Supports SharePoint, Azure, AWS, Google Cloud, SQL databases, and more.

Multiple Sources

Connect to cloud and local data sources

Python Analysis

Run pandas, numpy, sklearn in browser

Visualization

Interactive charts and dashboards

Upload Data (Fast)

File Upload

Paste Data

From URL

Fast File Upload

CSV, Excel, JSON supported (optimized for speed)

Tip: Use CSV for fastest uploads. Large files are sampled automatically.

Paste CSV/TSV Data

Current Dataset

No data loaded. Upload a file or connect to a source.

Connect Data Sources

SharePoint

Connect to SharePoint Online lists and document libraries

OAuth 2.0 Support

Azure Storage

Blob Storage, Data Lake, Azure SQL, Cosmos DB

SAS Token Support

AWS Services

S3, Redshift, RDS, Athena, DynamoDB

IAM Role Support

Google Cloud

BigQuery, Cloud Storage, Sheets, Firestore

Service Account

SQL Databases

MySQL, PostgreSQL, SQL Server, Oracle, SQLite

ODBC/JDBC

REST APIs

Connect to any REST API with authentication

OAuth2/Bearer

Data Explorer

No data loaded
Load data to preview

Data Statistics

Load data to view statistics

Python Analysis

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

print(“=== Python Data Analysis ===\n”)

# Check if data is loaded
if ‘df’ in globals() and df is not None:
    print(f”📊 Dataset Overview”)
    print(f”Rows: {len(df):,}”)
    print(f”Columns: {len(df.columns)}\n”)
    
    print(f”📈 Column Names:”)
    for i, col in enumerate(df.columns, 1):
        print(f”  {i}. {col}”)
    
    print(f”\n🔢 Data Types:”)
    print(df.dtypes.value_counts())
    
    print(f”\n📊 Summary Statistics:”)
    print(df.describe())
    
    print(f”\n❓ Missing Values:”)
    missing = df.isnull().sum()
    if missing.sum() > 0:
        for col in df.columns:
            if missing[col] > 0:
                pct = (missing[col] / len(df)) * 100
                print(f”  {col}: {missing[col]} ({pct:.1f}%)”)
    else:
        print(”  No missing values found!”)
    
    # Create visualizations
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    if len(numeric_cols) > 1:
        print(f”\n📈 Correlation Matrix (Top 10 numeric columns):”)
        top_cols = numeric_cols[:min(10, len(numeric_cols))]
        corr_matrix = df[top_cols].corr()
        print(corr_matrix.round(2))
        
        # Plot correlation heatmap
        plt.figure(figsize=(10, 8))
        plt.imshow(corr_matrix, cmap=’coolwarm’, vmin=-1, vmax=1)
        plt.colorbar()
        plt.xticks(range(len(top_cols)), top_cols, rotation=45)
        plt.yticks(range(len(top_cols)), top_cols)
        plt.title(‘Correlation Heatmap’)
        plt.tight_layout()
        plt.show()
    
    print(“\n✅ Analysis Complete!”)
    
else:
    print(“❌ No data loaded. Please upload data or connect to a source.”)
    print(“\n💡 Try loading sample data or check the Upload/Connections tabs.”)

# Python output will appear here # The ‘df’ variable contains your loaded data # All standard Python libraries are available

Visualization

Chart Type

X-Axis

Y-Axis

Group By

Visualization Code

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

print(“Creating visualization…”)

if ‘df’ in globals() and df is not None:
    # Get numeric columns
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    
    if len(numeric_cols) >= 2:
        # Create figure with subplots
        fig, axes = plt.subplots(2, 2, figsize=(14, 10))
        
        # Line plot of first numeric column
        if len(df) > 0:
            axes[0, 0].plot(df[numeric_cols[0]].values[:100], marker=’o’)
            axes[0, 0].set_title(f’Line Plot: {numeric_cols[0]}’)
            axes[0, 0].set_xlabel(‘Index’)
            axes[0, 0].set_ylabel(numeric_cols[0])
            axes[0, 0].grid(True, alpha=0.3)
        
        # Histogram
        if len(numeric_cols) > 0:
            axes[0, 1].hist(df[numeric_cols[0]].dropna(), bins=30, edgecolor=’black’, alpha=0.7)
            axes[0, 1].set_title(f’Histogram: {numeric_cols[0]}’)
            axes[0, 1].set_xlabel(numeric_cols[0])
            axes[0, 1].set_ylabel(‘Frequency’)
        
        # Scatter plot if we have at least 2 numeric columns
        if len(numeric_cols) >= 2:
            axes[1, 0].scatter(df[numeric_cols[0]], df[numeric_cols[1]], alpha=0.5)
            axes[1, 0].set_title(f’Scatter: {numeric_cols[0]} vs {numeric_cols[1]}’)
            axes[1, 0].set_xlabel(numeric_cols[0])
            axes[1, 0].set_ylabel(numeric_cols[1])
            axes[1, 0].grid(True, alpha=0.3)
        
        # Box plot
        if len(numeric_cols) > 0:
            numeric_sample = numeric_cols[:min(5, len(numeric_cols))]
            axes[1, 1].boxplot([df[col].dropna() for col in numeric_sample])
            axes[1, 1].set_title(‘Box Plot’)
            axes[1, 1].set_xticks(range(1, len(numeric_sample) + 1))
            axes[1, 1].set_xticklabels(numeric_sample, rotation=45)
        
        plt.tight_layout()
        plt.show()
        print(“✅ Visualization created successfully!”)
    else:
        print(“⚠️ Need at least 2 numeric columns for visualization”)
else:
    print(“❌ No data loaded”)

# Visualization output will appear here

Active Connections

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Recent Data Operations

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Dashboard

Enterprise Data Analysis Platform

Multiple Sources

Python Analysis

Visualization

Upload Data (Fast)

Fast File Upload

Paste CSV/TSV Data

Current Dataset

Connect Data Sources

SharePoint

Azure Storage

AWS Services

Google Cloud

SQL Databases

REST APIs

Configure Connection

Data Explorer

Data Statistics

Python Analysis

Visualization

Visualization Code

Active Connections

No Active Connections

Recent Data Operations