Supported Python Libraries

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Step 1: Installing Libraries

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Using pip install

# Install any library you need
!pip install library_name

# Example installations
!pip install pandas numpy matplotlib
!pip install scikit-learn tensorflow
!pip install geemap leafmap

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Installation Process

1. Run Installation: Execute the pip install command in a code cell
2. Wait for Installation: Installation completes automatically
3. Restart Kernel: Restart kernel if needed for some libraries
4. Import and Use: Import and use the library in subsequent cells

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Step 2: Popular Libraries

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Data Analysis

• pandas: Data manipulation and analysis
• numpy: Numerical computing
• scipy: Scientific computing
• scikit-learn: Machine learning

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Geospatial Libraries

• geemap: Google Earth Engine integration
• leafmap: Interactive mapping
• geopandas: Geospatial data analysis
• rasterio: Raster data processing
• folium: Interactive maps
• pyproj: Coordinate transformations

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Machine Learning

• tensorflow: Deep learning framework
• pytorch: Machine learning library
• xgboost: Gradient boosting
• lightgbm: Light gradient boosting

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Visualization

• matplotlib: Basic plotting
• seaborn: Statistical visualization
• plotly: Interactive plots
• bokeh: Interactive visualization

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Step 3: Geospatial Libraries

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Earth Engine Integration

# Install and use geemap
!pip install geemap

import geemap
import ee

# Initialize Earth Engine
ee.Initialize()

# Create a map
Map = geemap.Map()
Map.addLayer(ee.Image('USGS/SRTMGL1_003'), {}, 'SRTM')
Map

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Interactive Mapping

# Install and use leafmap
!pip install leafmap

import leafmap

# Create interactive map
m = leafmap.Map()
m.add_basemap('OpenStreetMap')
m

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Geospatial Analysis

# Install and use geopandas
!pip install geopandas

import geopandas as gpd

# Load geospatial data
gdf = gpd.read_file('/workspace/data/shapefile.shp')
gdf.plot()

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Step 4: Machine Learning Libraries

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Deep Learning

# Install and use TensorFlow
!pip install tensorflow

import tensorflow as tf
print(f"TensorFlow version: {tf.__version__}")

# Check GPU availability
print(f"GPU available: {tf.config.list_physical_devices('GPU')}")

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Traditional ML

# Install and use scikit-learn
!pip install scikit-learn

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

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Step 5: Data Visualization

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Static Plots

# Install and use matplotlib/seaborn
!pip install matplotlib seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Create plots
plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='x', y='y')
plt.show()

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Interactive Plots

# Install and use plotly
!pip install plotly

import plotly.express as px

# Create interactive plot
fig = px.scatter(df, x='x', y='y', color='category')
fig.show()

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Step 6: Specialized Libraries

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Image Processing

• opencv-python: Computer vision
• PIL/Pillow: Image processing
• scikit-image: Image processing

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Statistical Analysis

• statsmodels: Statistical modeling
• pingouin: Statistical testing
• scipy.stats: Statistical functions

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Database Integration

• sqlalchemy: Database ORM
• psycopg2: PostgreSQL adapter
• pymongo: MongoDB driver

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Cloud Services

• boto3: AWS SDK
• google-cloud-storage: Google Cloud
• azure-storage-blob: Azure Storage

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Step 7: Library Management

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Version Management

# Check installed versions
!pip list

# Install specific versions
!pip install pandas==1.5.3

# Upgrade libraries
!pip install --upgrade library_name

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Environment Management

• Automatic Installation: Libraries install automatically
• Version Compatibility: System handles version conflicts
• Dependency Resolution: Automatic dependency management
• Clean Environment: Fresh environment for each session

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Best Practices

• Install as Needed: Install libraries when you need them
• Check Compatibility: Verify library compatibility
• Use Virtual Environments: Consider virtual environments for complex setups
• Document Dependencies: Keep track of required libraries

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Enterprise Feature: Library Persistence (Coming Soon)

• Persistent Libraries: Enterprise users can persist libraries across kernel sessions
• GUI Management: Manage persistent libraries through a user-friendly interface
• Custom Environments: Create and maintain custom library environments
• Contact Support: Email support request for early access to this feature

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Step 8: Troubleshooting

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Common Issues

• Installation Fails: Check library name and internet connection
• Import Errors: Restart kernel after installation
• Version Conflicts: Install compatible versions
• Memory Issues: Use smaller libraries or optimize code

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Solutions

• Restart Kernel: Often resolves import issues
• Check Documentation: Verify installation instructions
• Alternative Libraries: Use alternative libraries if needed
• Contact Support: Get help for persistent issues

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Performance Tips

• Lazy Loading: Import libraries only when needed
• Memory Management: Monitor memory usage
• Efficient Libraries: Use efficient library alternatives
• Caching: Cache results to avoid recomputation

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Next Steps

1. Install Libraries: Start installing the libraries you need
2. GDAL Setup: GDAL is pre-installed — no setup required
3. Run Code: Run Code in Notebook with your libraries
4. Publish: Publish Your Notebook with your analysis