The evolution of going from SQL to Python is a logical move for data analysts and individuals who want to improve their data science skills. Analytics Insight on LinkedIn suggests that Python will be the de facto standard language in the field of data sciences, where 73% of data practitioners routinely work, immeasurably more than any other language. SQL is still going to be vital, particularly in the data querying process and in creating applications with relational databases, but Python will offer the all-important features of automation, graphical user interface design, and data manipulation based on logic. Therefore, learning the two tools will prepare you to venture into wider opportunities in the emerging data science job market.
Why Learn Python for Data Analysis?
The data science domain has adopted Python as its programming language. In contrast to SQL, which is focused on databases, Python allows an analyst to:
- Conduct an exploratory analysis of the data
- Develop forecasting models
- Automate the data pipelines
- Interactive data visualization
What is most important is that Python is a complement to SQL. You never stop working with SQL; you just become more capable of working with data in general.
Understanding Python’s Core Data Structures
Let us discover the main data structures in Python, that is, the main things that we use to work with data when doing data analysis. These constructs assist you with cleaning, arranging, and handling data in ways that SQL doesn’t help natively.
1. Lists ([]) — The Workhorse of Python Containers
An ordered, editable collection that permits duplicate values is called a list. It is frequently used to store elements from a column, row values, or query results.
Key Operations:
Typical Use Cases:
- Save the results of a SELECT query.
- Store the values that loops or functions return.
- Dynamically add or remove objects
SQL Analogy:
A list works similarly to a set of results from:
Lists let you update them as necessary and preserve the elements’ order.
2. Tuples (()) — Stable, Unchangeable Containers
Although they are immutable, tuples resemble lists. Their contents cannot be altered once they have been defined. For grouped data that should stay fixed, they are perfect.
Key Operations:
Common Use Cases:
- Displaying data with defined-size coordinates as RGB values
- Functions that return multiple values
- Because of hashability, they are used as keys in dictionaries.
SQL Analogy:
Tuples function similarly to rows that are not subject to update:
Although they don’t allow for any kind of alteration, tuples maintain order.
Note: Rows are returned as tuples by a number of database drivers, including sqlite3 and psycopg2.
3. Sets ({}) — Store Unique, Unordered Values
Sets are collections that lose all order and automatically remove duplicates. When working with discrete items or membership tests, they are helpful.
When to Use:
- Make sure the values are distinct.
- Execute operations such as intersection and union.
- Effectively check if a value is present
Key Operations:
SQL Analogy:
Sets align with queries like:
By design, sets do not prohibit duplicate values or maintain order.
4. Dictionaries ({key: value}) — Store Data with Labels
Key-value pairs are gathered in dictionaries. Dictionaries are perfect for organized data, such as records or configurations, because each key corresponds to a value.
What You Can Do:
- Access by key: employee[‘name’]
- Add or update: employee[‘title’] = ‘Data Analyst’
- Loop through all key-value pairs:
SQL Analogy:
A dictionary can be thought of as:
Like a row in a table, or even a record from WHERE id = 123
Key Operations:
Dictionaries can be changed, and as of Python 3.7, they preserve the order in which entries were added.
Why {} Can Be Confusing
Curly braces are used in both sets and dictionaries, but they serve different purposes.
Consider a dictionary as an organized combination of keys and values, and a set as an unordered collection of values.
SQL vs Python Structure Mapping
|
SQL Concept |
Python Equivalent |
|
A column or result set |
list |
|
A single, uneditable row |
tuple |
|
DISTINCT column values |
set |
|
A record from WHERE clause |
dictionary |
|
Full relational table |
pandas.DataFrame |
When Should You Use Python Over SQL?
SQL can never be beaten for extracting data in relational databases, but Python is more efficient with:
- Preprocessing and cleaning of data
- Working with non-relational data like JSON, XML, APIS
- Machine learning and statistical modeling
- Automation of routine work
- Data visualization
Best Practices for SQL Analysts Learning Python
- Learn Data Structures
Learn the tools first and then master libraries such as NumPy or Scikit-learn by dictionaries and DataFrames.
- Heavy Pandas Usage
Pandas constitutes the connector between the SQL thought process and the Python program. It will remind SQL users of their DataFrame object.
- Real Data Practice
Use Kaggle or CSV databases. Try writing your SQL queries in Python.
- Write Modular code
Functions perform good in Python when they are used to structure your logic. It assists you in reusing the code and creating neat scripts.
- Document Everything
Write comments and Markdown cells (in the case of Jupyter Notebooks) to explain how you thought about it.
Conclusion
If someone is supposed to consider being a complete data science professional, then Python has to be a must-learn skill along with SQL. Python, by no means, replaces SQL. Instead, it complements it. Unlocking the Python concepts of fundamental data structures and data manipulation using Pandas paves the way for more power, flexibility, and automation.
Demand for Python in the data science job market is rapidly moving upward; SQL queries data, but Python brings meaning to the data.
It is now the right time to put the gap aside-to go on from SQL to Python and enter into future data analytics.