Pandas 和 PostgreSQL 的区别

熊猫： Python支持内置库Pandas ，执行数据分析和操作是一种快速有效的方式。 Pandas库处理一维数组(称为系列)和多维数组(称为数据框)中可用的数据。它提供了多种功能和实用程序来执行数据转换和操作。使用numpy模块进行统计建模、过滤、文件操作、排序以及导入或导出是Pandas库的一些关键功能。以更加用户友好的方式处理和挖掘大数据。

PostgreSQL：它是一个开源的关系型数据库管理系统，主要用于各种应用程序的数据存储。 PostgreSQL使用较小的数据集执行数据操作，例如以更简化和更快的方式排序、插入、更新、删除。它通过SQL查询模拟数据分析和转换。它提供灵活的数据存储和复制，具有更高的安全性和完整性。它确保的主要功能是处理并发事务的原子性、一致性、隔离性和持久性 (ACID)。

表现

为了比较两个模块的性能，我们将对以下数据集执行一些操作：

该数据集可以加载到相应的帧中，然后可以针对不同的操作计算它们的性能：

• Select：显示数据集的所有行

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)

输出：

• 排序：按升序对数据进行排序。

蟒蛇3

# 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)

输出：

• 过滤器：从数据集中提取一些行。

蟒蛇3

# 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)

输出：

• 加载：加载数据集。

蟒蛇3

# 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)

输出：

下表说明了执行这些操作所需的时间：

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

因此，我们可以得出结论，除了加载操作之外，与PostgreSQL相比， pandas模块在几乎所有操作中都很慢。

熊猫 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.

因此，在进行数据检索、处理、连接、过滤等简单数据操作的地方，可以认为 PostgreSQL 更好且易于使用。但是，对于大型数据挖掘和操作，查询优化，争用大于其简单性，因此 Pandas 的性能要好得多。