Python Tutorial 55 – 12 Useful Python Functions and Modules Applied to Financial and Marketing Bots

In this Python tutorial, I would talk about the 10 useful Python functions and modules that I love using in building a Financial analysis bot or a marketing bot. All these elements can eliminate your time to tidy up and clean the data you grab, and then automate seamlessly between the scripts that are combined to act as an RPA.

python functions

In this Python tutorial, I would talk about the 12 useful Python functions and modules that we often use to build a Financial analysis bot or a marketing bot. All these elements can eliminate your time to tidy up and clean the data you grab, and then automate seamlessly between the scripts that are combined to act as an RPA.

Python module: regular expression, pandas, NumPy, random, time, Datetime

Importance of Data Cleaning, Extraction, Formatting, and Calculation upfront

RPA applications are basically helping financial talents and marketers to save more time from operational activities, and shift the time focused more on strategies and creativities. Thus, any RPA has one core mission, which is to deliver cleaned, validated, formatted, and prettified data. People can refer to and trust the calculation result in a visualized format.

As previously mentioned in the Python Tutorial, RPA can cover a wider range of research and operational work. These can be such as trending inspirational topics, competitor monitoring, market research, advertising optimization, data collection, B2B demand generation, etc. Basically, people are just like consuming the result, they could determine the strategy and execution based on the intelligent insight. 

Also, objective-driven is one of the most important values of RPA, like advertising optimization and B2B demand generation. We can focus more on business-level strategies, such as pricing, product, or communication and negotiation with the potential prospects.

For getting this kind of RPA, there are 10 useful Python functions and methods we usually utilize in building and RPA.

Data Type Converters

In terms of data type in computer science, there are primarily 4 basic types of data that consist of the whole world. They are integer, string, boolean, and float. And it has 3 types of advanced data format, which are list, tuple, and dictionary.

Converting between integer and string, or string and integer is useful in building an RPA application, which avoids bugs due to incompatible types of data.

  • Int()
  • Str()
  • Float()

For example, when creating a URL parameter and page pagination using a flask, we even set up the argument in the integer format by default. In fact, we might come across the page actually in the string format, which causes bugs at the end. For fixing this bug, we can use int() to covert the page variable into the integer for sure.

Besides the basic data type, advanced data types like lists, and dictionaries are vital in any RPA application. For more details regarding these data type converters, please check out these articles.

Python Tutorial 48: JSON, XML Converters to CSV, SQL, Google Sheets Data into JSON, XML

Python Tutorial 46: Data Converters to convert CSV to SQL, SQL to CSV, Google Sheet to SQL

Replace()

The replace() function can help you replace a specified phrase with another specified phrase in the script running mode. This method is very helpful in building a bot to scrape and collect information. It’s because the real internet world doesn’t have fully organized codings that allow you to scrape. In a way, the data is unstructured and even massive. We need to validate the information and code to solve this upfront.

Split()

The split() function divides a string into an ordered list of substrings, puts these substrings into an array, and returns the array. The division is done by searching for a pattern, where the pattern is provided as the first parameter in the method’s call.

Scraping HTML elements and data usually mix up with unuseful information in the data structure. For example, people don’t need to fetch the full URL of a product page. Instead, people are able to leverage the split() method to extract the ASIN id specifically in the scraping process. This approach can be applied to the Twitter nickname, Youtube channel ID, or removing the redirect domain information. 

Strip()

The Strip() function in Python is one of the built-in functions that come from the Python library.  It removes or truncates the given characters from the beginning and the end of the original string. The default behavior of the strip() method is to remove the whitespace from the beginning and at the end of the string. Basically, it’s as well as the trim formula in Google Sheets.

For avoiding any bugs or data matching mistakes, basically scraping bots need this method to remove whitespaces. The purpose of this method is as well as for trim() in Google Sheets. It ensures your data can be placed in the right format.

Get_text()

Get_text()  is used to extract the text in the drawing object, such as h1, h2, p, a, class, etc. The majority of our marketing bots have the capability to fetch text information or string data from the object. In particular, if you need to train AI machines to write blogs and articles using Tensorflow, you need this method to grab the training data.

Format and f

It’s a fantastic method in Python as a built-in one. Basically, people can combine the scraped elements together and reformat them into a new object. For example, if you are using a Youtube bot and grab the channel IDs, people can concatenate to have the Youtube about page to scrape.

python functions

Also, if you like to fetch the data from an SQL database based on the value input from users, people can use the format () to add the variables and return different data based on the actual different value input

Time.sleep()

While running a Python program, there might be times when you’d like to delay the execution of the program for some seconds.

The Python time module has a built-in function called time.sleep() with which you can delay the execution of a program.

With the sleep() function, you can get more creative in your Python projects because it lets you create delays that might go a long way in helping you bring in certain functionalities.

In any bots, this can help to scrape functions to work more accurately because it can avoid missing information due to slow loading speed.

Datetime()

Python Datetime module supplies classes to work with date and time. These classes provide a number of functions to deal with dates, times, and time intervals. Date and DateTime are an object in Python, so when you manipulate them, you are actually manipulating objects and not string or timestamps

Datetime() can attribute to give you a data label that records and facilitate you to pivot reports by date range. People can easily identify insight and scraped analysis from different data points.

Random.randint()

Using randrange() and randint() functions of a random module, we can generate a random integer within a range. This is commonly applied to the chatbot and outreach bot. 

python functions

For example, now your bot likes to outreach a list of potential clients on social media channels or reply to the question in live chat. For enriching the conversions, you don’t want to say hello every time to anybody in a conversation, you like to have some options for the bot to select in the greetings and main body information.

Regular Expression

A regular expression is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern. It’s widely used in data filtering, data scraping, and manipulation.

python functions

In the marketing bot, it must be the email scraper. Regex helps you to extract the email address from the informational Ocean. It is just like magic all emails pop up in front of you.

Pandas – Dataframe

Pandas DataFrame is a way to represent and work with tabular data. It can be seen as a table that organizes data into rows and columns, making it a two-dimensional data structure. A DataFrame can be either created from scratch or you can use other data structures like Numpy arrays. Here are the main types of inputs accepted by a DataFrame:

  • Dict of 1D arrays, lists, dicts, or Series
  • 2-D NumPy.ndarray
  • Structured or record ndarray
  • A Series
  • Another DataFrame

Pandas data frame is a familiar module if you are following up on my Python Tutorials. Basically, it can give you huge spaces to manipulate the data structure and visualization. It can interact with Excel, Google Sheets, JSON, SQL, etc.

Numpy

NumPy is a powerful, well-optimized, free open-source library for the Python programming language. It adds to support for large, multi-dimensional arrays (also called matrices or tensors).

It also comes equipped with a collection of high-level mathematical functions to work in conjunction with these arrays. These include basic linear algebra, random simulation, Fourier transforms, trigonometric operations, and statistical operations.

NumPy stands for ‘numerical Python’ and builds on the early work of the Numeric and Numarray libraries with the goal to give fast numeric computation to Python.  Today NumPy has numerous contributors and is sponsored by NumFOCUS.

As the core library for scientific computing, NumPy is the base for libraries such as Pandas, Scikit-Learn, and SciPy. It’s widely used for performing optimized mathematical operations on large arrays.

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