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This article introduces you how to use Python to implement common SQL tasks, the content is very detailed, interested friends can refer to, hope to be helpful to you.

Data practitioners have many tools that can be used to split data. Some use Excel, some use SQL, and some use Python. For some tasks, the advantages of using Python are obvious. Process larger data sets at a faster speed. Use an open source machine learning library based on Python. You can easily import and export data in different formats.

Because of its versatility, Python can be an important part of any data analyst toolbox. But it's hard to get started. Most data analysts are probably familiar with SQL or Excel. This tutorial is about helping you transfer your skills and skills from EXcel and SQL to Python.

First, let's set up Python. The easiest way is to use JupyterNotebook and Anaconda. This visual interface will allow you to insert Python code and view the output immediately. This will also make it easy for you to follow the rest of this tutorial.

Let's start with the basics: open a dataset.

Import data

You can import .sql databases and process them in SQL queries. In Excel, you can double-click a file and start working on it in spreadsheet mode. In Python, there are more complex features, thanks to the ability to handle many different types of file formats and data sources.

Using a data processing library Pandas, you can use the read method to import a variety of file formats. The complete list of file formats that can be imported using this method is in the Pandas document. You can import everything from CSV and Excel files to HTML files!

One of the biggest advantages of using Python is the ability to get data from a wide range of networks, rather than just accessing files that are downloaded manually. The requests library in Python can help you classify different sites and get data from them, while the BeautifulSoup library can help you process and filter the data, so you can get exactly what you need. If you want to go to this route, please be careful of the right to use it.

First, import the libraries we need.

We need the Pandas library to process our data. The numpy library is required to perform numeric operations and transformations. We need a requests library to get HTML data from the website. BeautifulSoup is required to process the data. Finally, you need the regular expression library of Python (re) to change some of the strings that will appear when processing the data.

In Python, you don't need to know much about regular expressions, but they are a powerful tool for matching and replacing certain strings or substrings.

Trust some of the code on this site.

This is a more technical explanation that details how to use Python code to get HTML tables.

You can copy and paste the above code into your own Anaconda, and if you run it with some Python code, you can iterate over it!

Here is the output of the code, which is called a dictionary if you don't modify it.

You will notice a comma-separated key-value list of parentheses. Each list in parentheses represents a row in our dataframe, and each column is represented by key: we are dealing with a country's ranking, per capita GDP (in US dollars) and its name (in "country").

This tutorial will be helpful for more information about data structures, such as lists and dictionaries, and how to run them in Python.

Fortunately, in order to move the data into Pandasdataframe, we don't need to understand the data, which is a similar way to aggregate the data into an SQL table or an Excel spreadsheet. Using a single line of code, we have allocated and saved this data to Pandasdataframe-as it turns out, the dictionary is the perfect data format to convert to dataframe.

With this simple Python assignment to the variable gdp, we now have a dataframe that can be opened and browsed when we write gdp. We can add a Python method to the word to create a policy view of the data in it. As a little in-depth understanding of the use of equal signs and assignments in Python, the tutorial is very helpful.

On how to use Python to perform common SQL tasks to share here, I hope that the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

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