Pandas: Python supports an in-built library Pandas, to perform data analysis and manipulation is a fast and efficient way. Pandas library handles data available in uni-dimensional arrays, called series, and multi-dimensional arrays called data frames. It provides a large variety of functions and utilities to perform data transforming and manipulations. Statistical modeling, filtering, file operations, sorting, and import or export with the numpy module are some key features of the Pandas library. Large data is handled and mined in a much more user-friendly way.
PostgreSQL: It is an open-source, relational database management system, which is primarily used for data storage for various applications. PostgreSQL performs data manipulation with a smaller set of data, like sorting, insertion, update, deletion in a much simplified and faster way. It simulates data analysis and transformation through SQL queries. It provides flexible storage and replication of data with much more security and integrity. The major features it ensures are Atomicity, Consistency, Isolation, and Durability (ACID) to handle concurrent transactions.
Performance
To compare the performance of both the modules, we will perform some operations on the below dataset:
This dataset can be loaded into the respective frames and then their performance can be computed for different operations:
- Select: Displaying all the rows of the dataset
Python3
# import required modules import time import psycopg2 import pandas # connect to server and load SQL database db = psycopg2.connect(database="postgres", user="postgres", password="12345", host="127.0.0.1", port="5432") db = conn.cursor() # load pandas dataset df = pandas.read_csv('gfg.csv') print('\nUsing PostgreSQL:') # computing time taken by PostgreSQL begin = time.time() db.execute("SELECT * FROM gfg") print(db.fetchall()) end = time.time() print('Time Taken:', end-begin) print('\nUsing Pandas:') # computing time taken by Pandas begin = time.time() print(df) end = time.time() print('Time Taken:', end-begin) |
Output:
- Sort: Sorting the data in ascending order.
Python3
# import required modules import time import psycopg2 import pandas # connect to server and load SQL database db = psycopg2.connect(database="postgres", user="postgres", password="12345", host="127.0.0.1", port="5432") cur = db.cursor() # load pandas dataset df = pandas.read_csv('gfg.csv') print('\nUsing PostgreSQL:') # computing time taken by PostgreSQL begin = time.time() print('Sorting data...') cur.execute("SELECT * FROM gfg order by ESTABLISHED") print(cur.fetchall()) end = time.time() print('Time Taken:', end-begin) print('\nUsing Pandas:') # computing time taken by Pandas begin = time.time() print('Sorting data...') df.sort_values(by=['ESTABLISHED'], inplace=True) print(df) end = time.time() print('Time Taken:', end-begin) |
Output:
- Filter: Extracting some rows from the dataset.
Python3
# import required modules import time import psycopg2 import pandas # connect to server and load SQL database db = psycopg2.connect(database="postgres", user="postgres", password="12345", host="127.0.0.1", port="5432") cur = db.cursor() # load pandas dataset df = pandas.read_csv('gfg.csv') print('\nUsing PostgreSQL:') # computing time taken by PostgreSQL begin = time.time() cur.execute("SELECT * FROM gfg where ESTABLISHED < 2000") print(cur.fetchall()) end = time.time() print('Time Taken:', end-begin) print('\nUsing Pandas:') # computing time taken by Pandas begin = time.time() print(df[df['ESTABLISHED'] < 2000]) end = time.time() print('Time Taken:', end-begin) |
Output:
- Load: Loading the dataset.
Python3
# import required modules import time import psycopg2 import pandas print('\nUsing PostgreSQL:') # computing time taken by PostgreSQL begin = time.time() # connect to server and load SQL database print('Loading SQL dataset...') db = psycopg2.connect(database="postgres", user="postgres", password="12345", host="127.0.0.1", port="5432") cur = db.cursor() end = time.time() print('Time Taken:', end-begin) print('\nUsing Pandas:') # computing time taken by Pandas begin = time.time() print('Loading pandas dataset...') # load pandas dataset df = pandas.read_csv('gfg.csv') end = time.time() print('Time Taken:', end-begin) |
Output:
The following table illustrates the time required for performing these operations:
| Query |
PostgreSQL (Time in seconds) |
Pandas (Time in seconds) |
|---|---|---|
| Select | 0.0019 | 0.0109 |
| Sort | 0.0009 | 0.0069 |
| Filter | 0.0019 | 0.0109 |
| Load | 0.0728 | 0.0059 |
Hence, we can conclude that pandas module is slow in almost every operation as compared to PostgreSQL except for the load operation.
Pandas VS PostgreSQL
|
Pandas |
PostgreSQL |
|---|---|
| Setup is easy. | Setup requires tuning and optimization of the query. |
| Complexity is less since it is just a package that needs to be imported. | Configuration and database configurations increase the complexity and time of execution. |
| Math, statistics, and procedural approaches like UDF are handled efficiently. | Math, statistics, and procedural approaches like UDF are not performed well enough. |
| Reliability and scalability are less. | Reliability and scalability are much better. |
| Only technically knowledgeable individuals can perform data manipulation operations. | Easy to read, understand since SQL is a structured language. |
| Cannot be easily integrated with other languages and applications. | Can be easily integrated to provide support with all languages. |
| Security is compromised. | Security is higher due to ACID properties. |
Therefore, at places, where simple data manipulations, like data retrieval, handling, join, filtering is performed, PostgreSQL can be considered much better and easy to use. But, for large data mining and manipulations, the query optimizations, the contention outweigh its simplicity, and therefore, Pandas perform much better.
