Difference between Pandas and PostgreSQL

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.