what APIs are, why they exist, and how we use them in Python?

🔹 1. Reading Apps in Python

When you say “read apps”, I assume you mean:

Reading data from different sources (S3, Oracle DB, etc.)
Creating a simple API/app to expose or process that data.

In Python, “apps” often means either:

Command-line scripts (basic programs you run from terminal).
APIs (using frameworks like Flask or FastAPI).
Data apps/dashboards (using Streamlit, Dash, etc.).

🔹 2. Reading Data from S3 & Oracle in Python

✅ Reading from AWS S3

import boto3

# Create S3 client
s3 = boto3.client('s3')

# Download file from S3
bucket_name = "my-bucket"
key = "data/sample.csv"
local_file = "sample.csv"

s3.download_file(bucket_name, key, local_file)

print("File downloaded:", local_file)

You’ll need AWS credentials (~/.aws/credentials or environment variables).
You can also use s3fs or pandas directly for reading CSV/Parquet.

import pandas as pd

df = pd.read_csv("s3://my-bucket/data/sample.csv")
print(df.head())

✅ Reading from Oracle Database

You can use cx_Oracle or oracledb package.

import oracledb

# Connect to Oracle
conn = oracledb.connect(user="username", password="password", dsn="host:1521/service_name")

cursor = conn.cursor()
cursor.execute("SELECT * FROM employees WHERE ROWNUM <= 5")

for row in cursor:
    print(row)

cursor.close()
conn.close()

🔹 3. Creating a Simple API in Python

✅ Using Flask

from flask import Flask, jsonify
import pandas as pd

app = Flask(__name__)

@app.route("/s3data")
def get_s3_data():
    # Example: read CSV from S3 (requires credentials setup)
    df = pd.read_csv("s3://my-bucket/data/sample.csv")
    return jsonify(df.head(5).to_dict(orient="records"))

@app.route("/oracledata")
def get_oracle_data():
    import oracledb
    conn = oracledb.connect(user="username", password="password", dsn="host:1521/service_name")
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM employees WHERE ROWNUM <= 5")
    rows = [dict(zip([d[0] for d in cursor.description], row)) for row in cursor]
    conn.close()
    return jsonify(rows)

if __name__ == "__main__":
    app.run(debug=True)

Run with: python app.py
Open in browser: http://127.0.0.1:5000/s3data or /oracledata.

✅ Using FastAPI (better for modern apps & async)

from fastapi import FastAPI
import pandas as pd

app = FastAPI()

@app.get("/s3data")
def read_s3_data():
    df = pd.read_csv("s3://my-bucket/data/sample.csv")
    return df.head(5).to_dict(orient="records")

Run:

uvicorn app:app --reload

Open in browser: http://127.0.0.1:8000/s3data

✅ So the steps are:

Read data (S3 → boto3/pandas, Oracle → cx_Oracle/oracledb).
Create an API layer (Flask/FastAPI).
Return JSON to consumers.

“where is the API name in the script?”

Let’s clarify with examples:

🔹 1. In Flask

When you write:

@app.route("/s3data")
def get_s3_data():
    ...

The API name (or endpoint path) is "/s3data".
The function name (get_s3_data) is the Python handler.
Full URL will be: http://127.0.0.1:5000/s3data

🔹 2. In FastAPI

When you write:

@app.get("/s3data")
def read_s3_data():
    ...

The API endpoint name is also "/s3data".
The method type is GET (you could also have @app.post, @app.put, etc.).
Full URL will be: http://127.0.0.1:8000/s3data

🔹 3. General Rule

In Python API frameworks:

The decorator (@app.route("/...") or @app.get("/...")) defines the API name / endpoint.
You can read it directly in the script to know what API is available.
Example: @app.get("/oracledata") means you can call: http://localhost:8000/oracledata

✅ So: the API name is the string inside the route decorator ("/s3data", "/oracledata", etc.), which maps a URL path to a function.

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