了解如何使用 Python 自動執行從 SQL 數據庫生成報告的過程。使用 Python 連接到數據庫、檢索數據並創建報告，我們可以節省時間並降低出錯的風險。
從 SQL 數據庫生成報告是許多組織中的一項常見任務。但是這個過程可能非常耗時且容易出錯，尤其是當它涉及手動數據提取、轉換和格式化時。
在本文中，我們將探討如何使用 Python 自動執行從 SQL 數據庫生成報告的過程，從而減少創建和分發報告所需的時間和精力。
第一步是使用 Python 連接到 SQL 數據庫。我們將使用 psycopg2 庫連接到 PostgreSQL 數據庫。
import psycopg2 conn = psycopg2.connect( host="localhost", database="mydatabase", user="myusername", password="mypassword" )
一旦我們建立了與 SQL 數據庫的連接，我們就可以執行 SQL 查詢來檢索報告所需的數據。
以下是用於從 PostgreSQL 數據庫檢索數據的示例代碼片段：
cur = conn.cursor() cur.execute("SELECT name, email, phone FROM customers") rows = cur.fetchall()
接下來，我們需要使用 ReportLab 或 PyPDF2 等 Python 庫創建報告。以下是使用 ReportLab 創建 PDF 報告的示例代碼片段：
from reportlab.pdfgen import canvas # Create a new PDF document pdf = canvas.Canvas("report.pdf") # Write the report title pdf.setFont("Helvetica-Bold", 16) pdf.drawString(50, 750, "Customer Report") # Write the report content pdf.setFont("Helvetica", 12) y = 700 for row in rows: pdf.drawString(50, y, "Name: " + row) pdf.drawString(50, y - 20, "Email: " + row) pdf.drawString(50, y - 40, "Phone: " + row) y -= 60 # Save the PDF document pdf.save()
此代碼創建一個新的 PDF 文檔，寫入報告標題，並循環遍歷從 SQL 數據庫檢索的數據以寫入報告內容。最終的 PDF 報告另存為report.pdf.
現在我們有了連接到 SQL 數據庫、檢索數據和創建報告的代碼，我們可以使用 Python 腳本自動化報告生成過程。
import psycopg2 from reportlab.pdfgen import canvas # Connect to the SQL database conn = psycopg2.connect( host="localhost", database="mydatabase", user="myusername", password="mypassword" ) # Retrieve the data from the SQL database cur = conn.cursor() cur.execute("SELECT name, email, phone FROM customers") rows = cur.fetchall() # Create the report pdf = canvas.Canvas("report.pdf") pdf.setFont("Helvetica-Bold", 16) pdf.drawString(50, 750, "Customer Report") pdf.setFont("Helvetica", 12) y = 700 for row in rows: pdf.drawString(50, y, "Name: " + row) pdf.drawString(50, y - 20, "Email: " + row) pdf.drawString(50, y - 40, "Phone: " + row) y -= 60 pdf.save() #close the database connection cur.close() conn.close()
此代碼連接到 SQL 數據庫、檢索數據、創建報告並將其另存為report.pdf. 然後您可以定期運行此腳本以自動生成報告。
在本文中，我們探討瞭如何使用 Python 自動執行從 SQL 數據庫生成報告的過程。通過使用 Python 連接數據庫、檢索數據和創建報告，我們可以節省時間並降低出錯風險。
我們還看到瞭如何使用 Python 庫（例如 psycopg2 和 ReportLab）來使該過程更加高效。使用這些技術，您可以輕鬆地從 SQL 數據庫自動生成報告，並專注於其他重要任務。
資料來源： https: //www.freecodecamp.org
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
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Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
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Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
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Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
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