> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nikaplanet.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Supported Python Libraries

> Learn about the Python libraries supported in NikaWorkspace for data analysis and geospatial processing

NikaWorkspace supports almost all open-source Python libraries as long as Jupyter supports them. You can install any library you need using `!pip install` in code blocks.

## Step 1: Installing Libraries

### Using pip install

```python theme={null}
# 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
```

### 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

## Step 2: Popular Libraries

### Data Analysis

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

### Geospatial Libraries

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

### Machine Learning

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

### Visualization

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

## Step 3: Geospatial Libraries

### Earth Engine Integration

```python theme={null}
# 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
```

### Interactive Mapping

```python theme={null}
# Install and use leafmap
!pip install leafmap

import leafmap

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

### Geospatial Analysis

```python theme={null}
# Install and use geopandas
!pip install geopandas

import geopandas as gpd

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

## Step 4: Machine Learning Libraries

### Deep Learning

```python theme={null}
# 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')}")
```

### Traditional ML

```python theme={null}
# 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)
```

## Step 5: Data Visualization

### Static Plots

```python theme={null}
# 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()
```

### Interactive Plots

```python theme={null}
# 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()
```

## Step 6: Specialized Libraries

### Image Processing

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

### Statistical Analysis

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

### Database Integration

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

### Cloud Services

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

## Step 7: Library Management

### Version Management

```python theme={null}
# Check installed versions
!pip list

# Install specific versions
!pip install pandas==1.5.3

# Upgrade libraries
!pip install --upgrade library_name
```

### 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

### 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

### 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

## Step 8: Troubleshooting

### 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

### 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

### 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

## Next Steps

Now that you know about supported libraries:

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](/guides/data-analysis/run-code-in-notebook) with your libraries
4. **Publish**: [Publish Your Notebook](/guides/data-analysis/publish-notebook) with your analysis

Happy coding!