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Python Integration

This is s aguide of accessing data from the warehouse using Python programming language. These examples will help you get started with reading the API's and working with health data from the warehouse.

What You Can Do With Python

Using Python with CEMA Warehouse allows you to:

  • Download datasets directly into your analysis environment
  • Combine multiple datasets for comprehensive analysis
  • Automate data collection for regular reports
  • Create visualizations and statistical analyses
  • Build reproducible research workflows

Getting Started

Required Python Libraries

Before using these examples, install these common data science libraries:

pip install pandas requests

Basic data access

This is the simplest way to access data using the API:

import pandas as pd

# Read directly using read_csv
births_url = f"https://warehouse.cema.africa/api/data/Demography/table/2019_birth_rates?format=csv"

# load data and inspect
births_2019 - pd.read_csv(births_url)
print(births_2019.head())

Create reusable function

You can also opt to create a reusable function:

import pandas as pd
import requests

# Base URL for API
BASE_URL = "https://warehouse.cema.africa/"

# Create  afunction
def get_dataset(category, category_name ,table_name):
    url = f"{BASE_URL}api/data/{category}/{category_name}/table/{table_name}?format=csv"
    return pd.read_csv(url)

# Load demography data
birth_data = get_dataset("category","Demography", "2019_birth_rates")
print(birth_data.head())

Error Handling

Sometimes data requests fail. Here's how to handle that:

def get_dataset(category, category_name, table_name):
    """Get dataset with error handling"""
    try:
        url = f"{BASE_URL}api/data/{category}/{category_name}/table/{table_name}?format=csv"
        data = pd.read_csv(url)
        print(f"Successfully loaded: {table_name}")
        return data
    except Exception as e:
        print(f"Error loading {table_name}: {e}")
        return None

# Use it safely
data = get_dataset("category","Demography", "2019_birth_rates")
if data is not None:
    # Continue with analysis
    print(data.head())

Next Steps

Once you can load data successfully:

  • Explore statistical analysis
  • Create advanced visualizations
  • Build interactive dashboards
  • Perform geospatial analysis

Getting Help

If you run into issues:

  • Check the error message - it usually tells you what went wrong
  • Verify dataset names - use CAIA Assistant to find correct names
  • Test with simple examples first - make sure basic access works
  • Contact support if you need help with specific datasets