Python as an etl tool

certainly! etl stands for extract, transform, load, and it's a process used to integrate data from different sources into a single database or data warehouse. python is a popular choice for etl processes due to its simplicity and the vast ecosystem of libraries available for data manipulation, extraction, and loading.

### overview of etl process

1. **extract**: retrieve data from various sources such as databases, apis, or flat files.
2. **transform**: clean, transform, and prepare the data for analysis.
3. **load**: insert the transformed data into a target database or data warehouse.

### libraries used in python for etl

- **pandas**: for data manipulation and analysis.
- **sqlalchemy**: for database connection and operations.
- **requests**: for making http requests to apis.
- **numpy**: for numerical operations (if needed).
- **pyodbc** or **psycopg2**: for connecting to databases like sql server or postgresql, respectively.

### etl example

let's create a simple etl process that extracts data from a csv file, transforms it, and then loads it into a sqlite database.

#### step 1: install required libraries

if you haven't already, install the required libraries:

#### step 2: create sample data

#### step 3: etl code example

here's a simple etl script that performs the operations outlined above:

### explanation of the code:

1. **extract**:
- the `extract_data` function reads data from a csv file into a pandas dataframe.

2. **transform**:
- the `transform_data` function performs a simple transformation on the data, increasing the salary by 10% and ensuring the age is an integer.

3. **load**:
- the `load_data` function uses sqlalchemy to create a connection to the sqlite database and loads the dataframe into a specified table.

4. **run etl**:
- the `run_etl` function orchestrates the entire etl process.

### running the example

1. save the above code in a python file, e.g., `etl_example ...

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