Post #1: Getting Started with Python for GIS

 

A friendly and practical intro for readers from a geospatial background—especially if they’re used to tools like ArcGIS or QGIS and want to automate workflows.

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✍️ Here's your full post content (ready to copy-paste into Google Blogger):

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Title: Getting Started with Python for GIS: A Simple Guide

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📍Introduction

Python has become a must-know language for GIS professionals. Whether you're working with shapefiles, running spatial joins, or analyzing satellite imagery, Python helps automate repetitive tasks and expand your analysis toolkit beyond desktop GIS software.

In this post, we’ll walk through the basic setup for using Python in GIS projects—and run your first spatial script using GeoPandas!

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🧰 What You’ll Need

Install Python and the necessary libraries using either pip or conda:

bash
# With pip
pip install geopandas rasterio shapely fiona matplotlib

# Or with conda (recommended for spatial work)
conda install geopandas rasterio shapely fiona matplotlib -c conda-forge

💡 Tip: Use Anaconda or Miniconda to manage your environments easily.

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📁 Step 1: Load a Shapefile

Let’s read a shapefile using GeoPandas:

python
import geopandas as gpd

# Load shapefile
gdf = gpd.read_file("data/roads.shp")

# Display first few rows
print(gdf.head())

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🗺️ Step 2: Plot Your Spatial Data

You can quickly visualize the layer:

python
gdf.plot()

To make it a bit fancier with color and size:

python
gdf.plot(color="blue", linewidth=1.5, edgecolor="black")

Add labels and title using matplotlib:

python
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 8))
gdf.plot(ax=ax, color="skyblue", edgecolor="black")
ax.set_title("Road Network")
plt.show()

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🔄 Step 3: Save Your Output

Let’s write the data to a new shapefile:

python
gdf.to_file("output/roads_output.shp")

Or export it as GeoJSON:

python
gdf.to_file("output/roads.geojson", driver="GeoJSON")

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🎯 Conclusion

That’s it! You’ve just run a complete GIS workflow in Python:

• Loaded shapefiles

• Visualized spatial data

• Exported results

In future posts, we’ll cover spatial joins, raster processing, interactive mapping, and automating complex workflows using Python.

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📌 Stay Tuned:

Coming up next → "Reading and Writing Shapefiles using GeoPandas"