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Table of contents
1.
Introduction
2.
What is SQL?
2.1.
What is Pandas?
3.
Comparison
3.1.
Syntax
3.2.
Data Size
3.3.
Flexibility
3.4.
Data Analysis
4.
Frequently Asked Questions
4.1.
Can pandas and SQL be used in tandem?
4.2.
If I know pandas, do I still need to learn SQL for data manipulation?
4.3.
With the rising popularity of pandas, is SQL becoming obsolete?
5.
Conclusion
Last Updated: Mar 27, 2024
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SQL vs Pandas

Author Sinki Kumari
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Ashwin Goyal
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Introduction

Both SQL and pandas play a pivotal role in data analysis and manipulation. SQL, known for its capacity to interact with large relational databases, and pandas, a Python library that makes data manipulation and analysis effortless, are tools that all data scientists should be familiar with. To give a better understanding of these two tools, we'll delve into their characteristics and use cases, supplemented by examples.

SQL vs Pandas

What is SQL?

SQL, which stands for Structured Query Language, is a well-established standard for managing relational database management systems (RDBMS). It is designed to handle the extraction, updating, and deletion of data within databases.

One of SQL's main strengths is its ability to execute complex queries, which makes it easy to combine data from multiple tables and aggregate data. Moreover, as SQL operates directly in a database system, it can process operations swiftly, especially when working with indexed columns
 

For instance, if you want to select employees who are older than 25 from an employees table, the SQL query would be:

SELECT name, age FROM employees WHERE age > 25;

What is Pandas?

Pandas, on the other hand, is a Python library that excels at handling and analyzing data. Its data structures, Series and DataFrame, are efficient and easy to use.

Pandas can handle a variety of data types, missing data, and also provides methods for filtering, aggregating, and visualizing data. This makes pandas a fantastic tool for data cleaning, transformation, and exploratory analysis.

For example, to filter rows from a DataFrame in pandas where the employee's age is over 25, you would write:

df = pandas.read_csv('employees.csv')
df_filtered = df[df['age'] > 25]
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Comparison

SQL and pandas, despite having overlapping functionalities, have different use cases.

Syntax

SQL adopts a declarative syntax where you state what you want without specifying how to get it. On the contrary, pandas uses a more procedural approach common to Python, where you write step-by-step operations.

Data Size

When dealing with large datasets, SQL can be more efficient since it only fetches a subset of data as needed. Pandas, however, stores the entire DataFrame in memory, which could limit its ability to process larger datasets.

Flexibility

Pandas has an edge over SQL in terms of flexibility as it can read data from a wide array of sources such as CSV, Excel, SQL databases, and even web pages. SQL is mainly used to query data from relational databases.

Data Analysis

SQL is designed for data extraction and filtering, whereas pandas excels at data cleaning, manipulation, and visualization. Plus, pandas can seamlessly integrate with other Python libraries like NumPy, Matplotlib, and Seaborn, giving you a more comprehensive data analysis toolkit.

Also See, difference between sql and nosql

Frequently Asked Questions

Can pandas and SQL be used in tandem?

Absolutely. Pandas can fetch data from SQL queries and SQL databases, which means you can leverage the best of both worlds: SQL's interaction with databases and pandas' data manipulation abilities.

If I know pandas, do I still need to learn SQL for data manipulation?

While pandas is indeed a powerful tool, knowing SQL is crucial when working with relational databases, especially for executing complex queries and handling large datasets.

With the rising popularity of pandas, is SQL becoming obsolete?

Not at all. SQL is still an essential tool for data analysis, especially when dealing with complex queries and large-scale data in relational databases.

Conclusion

SQL and pandas, both with their unique strengths, are pivotal tools in the domain of data analysis. SQL is your go-to choice for managing large datasets and conducting intricate queries on relational databases. However, for tasks involving data from various sources, and those that require data cleaning, manipulation, and visualization, pandas is the more suitable option. The key to being an effective data scientist lies in knowing how to tap into the potential of both these tools, sometimes even using them together for more robust and efficient data analysis.

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