Any developers or programmers, or even marketers would not feel strange to a data type – JSON. It’s one of the most popular and awesome data serialization language. In fact, there is an alternative called YAML. I believe people who is famiilar with Google ads API must know this data type. In this Python knowledge hub, I would elaborate what are their pros and cons respectively, and how you can better leverage them as a developer and marketer.
The random forest algorithm has been applied across a number of industries, allowing them to make better business decisions. Some use cases include high credit risk analysis and product recommendation for cross-sell purposes.
In this piece, I would briefly walk you through several methods of generating feature importance by using classic red wine quality validator dataset. By the end of this chapter, you can have a basic concept to use Random forest applied to your projects and compare the result amongst different methods.
In this python tutorial, I’ll walk you through how to create a script that can convert CSV, SQL or Google Sheets data into JSON or XML. The main modules of this tutorial are the JSON and CSV.
Data converters help convert data inventory between different formats into an expected format you like to use, such as SQL, CSV, JSON, XML, etc. If you are looking for ways to monetize data by selling contactable data like B2B prospects through an API or a SaaS, I believe this piece can help manage your data inventory in SQL and CSV.
An objective-oriented scraping project consists of many standalone Python bot scripts which can connect and function together. One of the most useful data used to scrape potential leads’ data must be the brand web domains. Basically we learn and know a brand from there. The question is how we are able to automatically grab in bulk instead of using Google search. This article tells how to make a bot with Python, Clearbit and Sqlite3
In the previous Python Tutorial, we talked about how to scrape more than 50 videos from a Youtube search query keyword, and also grab the performance of each video, such as view, comment, like, etc. However, it’s not the end of automation power, like saying you aim to research, filter Youtubers, and automate the collaboration invitation process. At least, the fetched list of Youtubers should be saved and managed in a datasheet on a cloud drive instead of in the CSV file, that can be set up and easily integrated with other platforms.
So in this Python Tutorial, I will continue to use the Python script from the Python Tutorial Chapter 6, and walk you through how to create a Robot user account, leverage Google Sheet API to save all fetched data in a Google Sheet In your web scraping python script. By the end of this Python Tutorial, you can learn what modules you need to set up, and experience just looking at a spreadsheet that is automatically listing all videos in a preset format.
In the previous Python Tutorial for digital marketers 2, we talked about how to install beautifulsoup4, requests, lxml, html5lib and sublime text, and then scraping web data by them. But the data is not saved in a file or a database yet, so it’s not convenient for you to use for your business purpose and work operation.
So in this Python Tutorial,we would talk about how to write Python scripts to parse and save the data into CSV files in local, and read the CSV files in a Python environment.
By the end of this Python Tutorial, you can master what CSV read, parse and write methods you can use to open and save CSV files in a readable format, although we are not going to deep dive into a specific scraping methods script writing which we would talk about in the next chapter of Python Tutorial.
Google Sheets is one of the most popular platforms to manage data, such as performance trackers, financial models, and so on. In this article, I would walk through how to integrate with Easy2Digital API with Google Apps scripts to create a function. I would take the financial ratio TTM API as an example. By the end of this article, you can use this function to fetch API data based on your needs.
In this piece, I will share introduce Pandas GroupBy(), and go through how to combine the value into one set with a shared key, or column value. For example, if your Google advertising campaign name is shared with different data sets such as data from daily, weekly or monthly, and so on and so forth, here is a way to consolidate them into one set for easy fetching, using, and applying them in web application interactions.
Pandas pivot_table() is super powerful for developers to manipulate the data, such as data visualization, data inventory, API development, etc. In terms of dashboard development or data visualization, transposing specific data objectives from column order to row sequence is very common. So in this article, I’ll go through how to transpose specific data in bulk in a second using Pandas pivot_table() and Python
Using Pandas to manipulate the data is a set of fundamental skills used in so many applications. This article shares how to convert a column into a row using Set_index().T given by Pandas. By the end of this piece, you can learn skills applied to data visualization, API development and some sections of machine learning.
Data is the blood of machine learning, but it’s not the quantity of data. So, proper and optimal data preprocessing is super important before starting developing machine learning models. So in this piece, I would walk through 1 plus 2 critical data preprocessing steps using Python and Scikit Learn. By the end of this piece, you can start working out on your own machine learning, data analysis projects with practical tips and tricks
In this piece, I would walk you through brieflyf how to predict a variant pricing based on having considered multiple variables that might be correlated to the pricing change. By the end of this piece, you can apply this module to your business actual cases using Python and Scikit learn for generating a score to predict the pricing.
Determination is likely being affected by one variant to one variable, or one variant to multiple variables. Machine makes decision based on maths. So in this article, I would walk through how to generate a price prediction score between a stock ticker and NASDAQ price correlation. I would show the methods using Python and Scikit Linear Regression model.