Creating Maps in Jupyter Notebook using GeoPandas - Revision history

Harithharison: /* Conclusion */

2025-10-07T03:26:45Z

Conclusion

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Final Result

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Part 1: Getting Started

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Step 2.

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Step 2.

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Step 1.

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Part 1: Getting Started

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Making a Join Key

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Making a Join Key

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Part 4: Merging

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 === Conclusion ===
- This tutorial demonstrated how to create and visualize maps in Jupyter Notebook using GeoPandas, combining data analysis and cartography within a single, script-based workflow. Through the guided steps, you learned how to install GeoPandas across different operating systems, import and clean both tabular and spatial datasets, merge them using shared attributes, and produce a final map with cartographic elements such as a legend and north arrow.
+This tutorial demonstrated how to create and visualize maps in Jupyter Notebook using GeoPandas, combining data analysis and cartography within a single, script-based workflow. Through the guided steps, you learned how to install GeoPandas across different operating systems, import and clean both tabular and spatial datasets, merge them using shared attributes, and produce a final map with cartographic elements such as a legend and north arrow.
 The addition of Ottawa’s 2021 census and ward data provided an up-to-date and locally relevant example, showing how GeoPandas can be applied to real Canadian datasets. With these updates, the tutorial now serves as a complete, cross-platform reference for students and GIS users interested in transitioning from traditional software to open-source, reproducible Python workflows.

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 ==Final Result==
 [[File:Population by Area in Vancouver.png]]<br>
+=== Conclusion ===
+ This tutorial demonstrated how to create and visualize maps in Jupyter Notebook using GeoPandas, combining data analysis and cartography within a single, script-based workflow. Through the guided steps, you learned how to install GeoPandas across different operating systems, import and clean both tabular and spatial datasets, merge them using shared attributes, and produce a final map with cartographic elements such as a legend and north arrow.
+The addition of Ottawa’s 2021 census and ward data provided an up-to-date and locally relevant example, showing how GeoPandas can be applied to real Canadian datasets. With these updates, the tutorial now serves as a complete, cross-platform reference for students and GIS users interested in transitioning from traditional software to open-source, reproducible Python workflows.
+Overall, this exercise highlights the efficiency and flexibility of using GeoPandas for spatial data management and visualization, empowering users to automate mapping tasks and build professional-quality maps directly within Jupyter Notebook.

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 === Step 1. ===
--Create a clean Conda env
+- Create a clean Conda env
 [[File:Conda env.png]]<br>
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 [[File:Geopandas.png]]<br>
+=== Step 3. ===
+- Launch JupyterLab from this env
+[[File:Install.png]]<br>
 ===Data===

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 === Step 2. ===
-Install GeoPandas (+ helpers)
+- Install GeoPandas (+ helpers)
 [[File:Geopandas.png]]<br>

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 Install GeoPandas (+ helpers)
-[[File:geopandas.png]]<br>
+[[File:Geopandas.png]]<br>
 ===Data===

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 ==Making a Join Key==
+- When using the updated Ottawa census data you can use the following method to merge/join data
 * If you have ward-level population already, align by a shared column (e.g., WARD_NUM ↔ a ward number/name in your table).

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 * If you only have CT/DA population, spatially join DAs/CTs to wards and sum:
-[[File:Joinkey.png|frameless|center]]
+[[File:Joinkey.png]]<br>
 ==Part 5: Displaying==