Python Data Science Tutorial
Python Data Processing
Python Data Visualization
Statistical Data Analysis
Selected Reading
- Python Data Science - Matplotlib
- Python Data Science - SciPy
- Python Data Science - Numpy
- Python Data Science - Pandas
- Python Data Science - Environment Setup
- Python Data Science - Getting Started
- Python Data Science - Home
Python Data Processing
- Python Stemming and Lemmatization
- Python word tokenization
- Python Processing Unstructured Data
- Python Reading HTML Pages
- Python Data Aggregation
- Python Data Wrangling
- Python Date and Time
- Python NoSQL Databases
- Python Relational databases
- Python Processing XLS Data
- Python Processing JSON Data
- Python Processing CSV Data
- Python Data cleansing
- Python Data Operations
Python Data Visualization
- Python Graph Data
- Python Geographical Data
- Python Time Series
- Python 3D Charts
- Python Bubble Charts
- Python Scatter Plots
- Python Heat Maps
- Python Box Plots
- Python Chart Styling
- Python Chart Properties
Statistical Data Analysis
- Python Linear Regression
- Python Chi-square Test
- Python Correlation
- Python P-Value
- Python Bernoulli Distribution
- Python Poisson Distribution
- Python Binomial Distribution
- Python Normal Distribution
- Python Measuring Variance
- Python Measuring Central Tendency
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Python Geographical Data
Python - Geographical Data
Many open source python pbraries now have been created to represent the geographical maps. They are highly customizable and offer a varierty of maps depicting areas in different shapes and colours. One such package is Cartopy. You can download and install this package in your local environment from
. You can find numerous examples in its gallery.In the below example we show a portion of the world map showing parts of Asia and Austrapa. You can adjust the values of the parameters in the method set_extent to locate different areas of world map.
import matplotpb.pyplot as plt import cartopy.crs as ccrs fig = plt.figure(figsize=(15, 10)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) # make the map global rather than have it zoom in to # the extents of any plotted data ax.set_extent((60, 150, 55, -25)) ax.stock_img() ax.coastpnes() ax.tissot(facecolor= purple , alpha=0.8) plt.show()
Its output is as follows −
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