Tag: Programming

Python f-strings, available since Python 3.6, offer a concise way to embed expressions in string literals using curly braces {}. They improve readability and performance over older methods like %-formatting and str.format(). To use f-strings, prefix the string with “f” or “F” and enclose expressions within braces: f"My name is {name} and I am {age} years old."

In recent years, Python has seen the development and adoption of several new features, one of which is f-strings. Also known as formatted string literals, f-strings were introduced in Python 3.6 via PEP 498. They have quickly become popular, as they offer a simple and straightforward syntax for embedding expressions inside strings, with the output being evaluated at runtime.

As a Python developer, I’ve found f-strings to be immensely useful for improving the readability and efficiency of my code. Rather than using more cumbersome methods like concatenation or the str.format() function, f-strings allow me to inline expressions directly within a string by using curly braces {}, significantly simplifying parts of the code.

In my experience, f-strings have not only enhanced the overall development process but have also contributed to the increased adoption of Python as a go-to programming language for various applications. This powerful string formatting feature makes Python even more appealing to both beginners and experienced programmers alike.

But before we start, allow me to show you another beautiful birds pic:

Understanding Python F-Strings

As a Python enthusiast, I’m always excited to share the features that make Python so elegant and easy to use. One such feature is Python f-strings, introduced in Python 3.6 . They are a fantastic way to format strings and greatly enhance the readability of your code.

Basics of F-Strings

F-strings, also known as formatted string literals, are a more modern and efficient way of formatting strings compared to traditional methods like str.format(). The best part about using f-strings is their simplicity – you just need to use an “f” or “F” in front of your string, followed by the expressions enclosed in curly braces {} that you want to embed within the string .

For instance, let’s compare the old and new ways of formatting strings:

name = "Alice"
age = 30 # Using str.format()
formatted_old = "{} is {} years old".format(name, age) # Using f-string
formatted_new = f"{name} is {age} years old"

As you can see, f-strings not only make the code more readable but also more concise. Trust me, once you start using f-strings, there’s no going back!

F-Strings Syntax

F-strings follow a very straightforward syntax that makes them effortless to use in daily coding tasks. Let me show you how it works:

• Begin your string with an “f” or “F“: f"..." or F"...".
• Embed expressions in curly braces {}: f"My name is {name}".
• You can also use expressions inside the curly braces, like f"3 + 5 = {3 + 5}".
• Format specifiers can be added after the expression using ! and : symbols, such as f"pi rounded to 2 decimals: {3.14159:.2f}".

Here’s a quick example that demonstrates how powerful and versatile f-strings can be:

name = "Bob"
score = 87.345 result = f"{name}, your score is {score:.1f}%, which is {'good' if score >= 80 else 'average'}!"

In this example, I’ve used an f-string to embed the person’s name, round their score to one decimal place, and conditionally evaluate their performance based on the score – all within a single line of code!

Recommended: Are Python One-Liners Turing Complete?

F-strings truly are a game-changer in Python, and I hope you find them as useful and efficient as I do. Happy coding!

Advantages of Using F-Strings

Readability, performance and scalability, coding efficiency, and versatility are four main advantages of using f-strings! Before I show you the advanced capabilities of f-strings, let’s quickly discuss each of those advantages next!

F-String Advantage 1: Readability

Firstly, I’ve found that using f-strings in Python makes my code more readable.

F-strings allow me to embed expressions directly into the string itself, using curly braces {}. This not only makes it easier to understand the code at a glance, but also reduces the chance of errors due to the smooth flow of the text.

Furthermore, f-strings aren’t cluttered with special characters, unlike other formatting methods (Towards Dev).

F-String Advantage 2: Performance

Another advantage of using f-strings is their scalability and performance improvements.

Since they were introduced in Python 3.6, f-strings have proven to be faster than other string formatting methods because the expressions within the curly braces are evaluated at runtime (Towards Data Science). This can be crucial, especially in large-scale projects where every millisecond counts .

F-String Advantage 3: Coding Efficiency

Not only that, but f-strings can help improve my coding efficiency.

Their concise syntax saves me from dealing with overly verbose formatting code, which can become unwieldy in complex situations. With f-strings, it’s easier for me to grasp what the code is supposed to do without getting lost in a sea of parentheses and method calls.

F-String Advantage 4: Versatility

Finally, f-strings offer more versatility when it comes to mixing variable types.

In one line of code, I can include strings, integers, and even complex data structures such as dictionaries or lists. This flexibility makes f-strings invaluable for developers who work with diverse datasets and need to output multi-layered information in a streamlined format .

Recommended: Python One-Liner Superpower

F-Strings Expressions

In this section, I’m going to discuss f-strings expressions, which are a powerful aspect of Python f-strings. They allow you to embed variables and even perform operations within the string. Let’s dive into the details.

Variables and Literals

One of the most useful features of f-strings is the ability to include variables directly within the string. To do this, simply include the variable inside curly braces {} within the f-string.

For example:

name = "Alice"
age = 25
my_string = f"Hello, my name is {name} and I am {age} years old."
print(my_string)

This code would output:

Hello, my name is Alice and I am 25 years old.

You can see how the variables are easily replaced within the f-string without the need for concatenation or complex formatting methods.

You can also include literals or expressions, like:

my_string = f"Hello, my name is {'Alice'} and I am {25 + 1} years old."
print(my_string)

This would output:

Hello, my name is Alice and I am 26 years old.

Escape Characters

Sometimes, you might need to include curly braces in your f-string. Since they’re used for expressions, you’ll need to escape them by doubling them up. This is quite simple, just use two curly braces, like {{ or }}:

my_string = f"Showing amount in dollars: {{100}}"
print(my_string)

This would output:

Showing amount in dollars: {100}

With this knowledge, I can now create more readable and concise f-strings in my Python code. Whether it’s injecting variables, using literals, or handling escape characters, f-strings make my life as a coder much easier.

Formatting Text with F-Strings

In this section, I’ll discuss how to format text using Python f-strings, a powerful feature introduced in Python 3.6. I’ll cover three key sub-topics: padding and alignment, formatting numbers, and date and time formatting. Let’s dive in!

Padding and Alignment

To pad and align text using f-strings, I’ll use the curly braces {} as placeholders within the f-string. To illustrate this, I’ll align a string to the left, right, and center. For left alignment, I can use the '<' sign, for right alignment the '>' sign, and for center alignment, I’ll use the '^' sign.

Here’s how it’s done:

name = "John"
print(f"{name:<10}") # Left align
print(f"{name:>10}") # Right align
print(f"{name:^10}") # Center align

These examples display the text 'John' with a width of 10 characters, aligned to the left, right, and center, respectively.

Formatting Numbers

Formatting numbers is a breeze with f-strings. I can specify the precision, add a thousand separator, and perform other formatting tasks.

For example, to round a number to two decimal places, I’ll use the ‘f‘ type and set the precision like so:

number = 3.14159265
formatted_number = f"{number:.2f}"
print(formatted_number) # Output: 3.14

Adding a thousand separator is simple using the ‘,‘ option:

big_number = 1234567
formatted_big_number = f"{big_number:,}"
print(formatted_big_number) # Output: 1,234,567

Neat, right?

Date and Time Formatting

Python f-strings also make it easy to format date and time values. To do this, I’ll import the datetime module and format a datetime object like so:

from datetime import datetime current_time = datetime.now()
formatted_time = f"{current_time:%Y-%m-%d %H:%M:%S}"
print(formatted_time) # Output: e.g., 2023-04-06 13:31:46

In this example, I used the format codes %Y, %m, %d, %H, %M, and %S to display the year, month, day, hour, minute, and second, respectively.

I hope these examples clarified how to use f-strings for formatting text, numbers, and dates in Python.

Recommended: Ten Python One-Liners to Get Today’s Date as YYYY-MM-DD

F-Strings and Security

When it comes to string formatting in Python, f-strings have some notable advantages, especially regarding security. I’ve found that f-strings are safer than other formatting options, like str.format() or %-formatting. Using f-strings helps protect code from potential security risks related to untrusted data in format strings.

When I use str.format() or %-formatting, it’s crucial to ensure that format strings are either static or sanitized. Thanks to f-strings, this concern is significantly reduced, making my code less prone to input-based vulnerabilities.

To illustrate this, let’s consider a simple example:

# Using %-formatting:
print("Hello, %s!" % user_input) # Using str.format():
print("Hello, {}!".format(user_input)) # Using f-strings:
print(f"Hello, {user_input}!")

In all three cases, the user_input variable is being inserted into the string. While %-formatting and str.format() can lead to unwanted behavior if the user_input contains unexpected format specifiers, f-strings don’t suffer from this issue. This makes them a more secure choice for handling user-provided data.

However, it’s essential to note that even though f-strings are generally more secure, I shouldn’t let my guard down completely. It’s always good to follow best practices for validating and sanitizing user input to ensure that my Python code remains secure and resistant to potential attacks.

Comparing F-Strings to Other Formatting Methods

As a Python programmer, I’ve come across several ways to format strings. In this section, I will dive into a comparison of f-strings with two other popular formatting methods: percent-style string formatting and the str.format() method.

Percent-Style String Formatting

Before f-strings and the str.format() method, percent-style formatting was commonly used. It resembled the way strings are formatted in C, using the percent symbol (%) as a placeholder. For example, to insert a variable into a string, I would write:

name = "Alice"
output = "Hello, %s!" % name
print(output) # Output: Hello, Alice!

While this method is easy to use for simple formatting, it can become difficult to read and maintain when dealing with multiple variables or complex string compositions.

Str.format() Method

Introduced in Python 2.6, the str.format() method offered a more readable approach compared to percent-style formatting. Instead of using the percent symbol, I would include placeholders in the form of curly braces {}:

name = "Alice"
output = "Hello, {}!".format(name)
print(output) # Output: Hello, Alice!

The str.format() method allows me to utilize advanced formatting options, such as specifying field widths and alignment. However, even though it is more powerful and flexible than percent-style formatting, it can still become cumbersome for complex strings.

Now, let’s see how f-strings compare to these two methods. With f-strings, introduced in Python 3.6, I can include expressions within the curly braces, and the syntax is more concise:

name = "Alice"
output = f"Hello, {name}!"
print(output) # Output: Hello, Alice!

Not only do f-strings make my code more readable, they also tend to be faster than the other two formatting methods!

As a Python programmer who values readability and performance, I find that f-strings are the way to go when it comes to string formatting. While percent-style and str.format() methods still have their place in older codebases, f-strings offer a cleaner and more efficient solution for my string formatting needs.

Recommended: String Formatting Comparison: format() | Percent | f-string

In this article, I will discuss a popular and efficient way to work with structured data in Python using DataFrames.

A DataFrame is a two-dimensional, size-mutable, and heterogeneous tabular data structure with labeled axes (rows and columns). It can be thought of as a table or a spreadsheet with rows and columns that can hold a variety of data types.

One common challenge is converting a Python list of dictionaries into a DataFrame.

To create a DataFrame from a Python list of dicts, you can use the pandas.DataFrame(list_of_dicts) constructor.

Here’s a minimal example:

import pandas as pd
list_of_dicts = [{'key1': 'value1', 'key2': 'value2'}, {'key1': 'value3', 'key2': 'value4'}]
df = pd.DataFrame(list_of_dicts) 

With this simple code, you can transform your list of dictionaries directly into a pandas DataFrame, giving you a clean and structured dataset to work with.

A similar problem is discussed in this Finxter blog post:

Recommended: How to Convert List of Lists to a Pandas Dataframe

Converting Python List of Dicts to DataFrame

Let’s go through various methods and techniques, including using the DataFrame constructor, handling missing data, and assigning column names and indexes.

Using DataFrame Constructor

The simplest way to convert a list of dictionaries to a DataFrame is by using the pandas DataFrame constructor. You can do this in just one line of code:

import pandas as pd
data = [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]
df = pd.DataFrame(data)

Now, df is a DataFrame with the contents of the list of dictionaries. Easy peasy!

Handling Missing Data

When your list of dictionaries contains missing keys or values, pandas automatically fills in the gaps with NaN values. Let’s see an example:

data = [{'a': 1, 'b': 2}, {'a': 3, 'c': 4}]
df = pd.DataFrame(data)

The resulting DataFrame will have NaN values in the missing spots:

 a b c
0 1 2.0 NaN
1 3 NaN 4.0

No need to manually handle missing data!

Assigning Column Names and Indexes

You may want to assign custom column names or indexes when creating the DataFrame. To do this, use the columns and index parameters:

column_names = ['col_1', 'col_2', 'col_3']
index_names = ['row_1', 'row_2']
df = pd.DataFrame(data, columns=column_names, index=index_names)

This will create a DataFrame with the specified column names and index labels:

 col_1 col_2 col_3
row_1 1.0 2.0 NaN
row_2 3.0 NaN 4.0

Working with the Resulting DataFrame

Once you’ve converted your Python list of dictionaries into a pandas DataFrame, you can work with the data in a more structured and efficient way.

In this section, I will discuss three common operations you may want to perform with a DataFrame:

• filtering and selecting data,
• sorting and grouping data, and
• applying functions and calculations.

Let’s dive into each of these sub-sections!

Filtering and Selecting Data

Working with data in a DataFrame allows you to easily filter and select specific data using various techniques. To select specific columns, you can use either DataFrame column names or the loc and iloc methods.

Recommended: Pandas loc() and iloc() – A Simple Guide with Video

For example, if you need to select columns A and B from your DataFrame, you can use the following approach:

selected_columns = df[['A', 'B']]

If you want to filter rows based on certain conditions, you can use boolean indexing:

filtered_data = df[(df['A'] > 5) & (df['B'] < 10)]

This will return all the rows where column A contains values greater than 5 and column B contains values less than 10.

Sorting and Grouping Data

Sorting your DataFrame can make it easier to analyze and visualize the data. You can sort the data using the sort_values method, specifying the column(s) to sort by and the sorting order:

sorted_data = df.sort_values(by=['A'], ascending=True)

Grouping data is also a powerful operation to perform statistical analysis or data aggregation. You can use the groupby method to group the data by a specific column:

grouped_data = df.groupby(['A']).sum()

In this case, I’m grouping the data by column A and aggregating the values using the sum function. These operations can help you better understand patterns and trends in your data.

Applying Functions and Calculations

DataFrames allow you to easily apply functions and calculations on your data. You can use the apply and applymap methods to apply functions to columns, rows, or individual cells.

For example, if you want to calculate the square of each value in column A, you can use the apply method:

df['A_squared'] = df['A'].apply(lambda x: x**2)

Alternatively, if you need to apply a function to all cells in the DataFrame, you can use the applymap method:

df_cleaned = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)

In this example, I’m using applymap to strip all strings in the DataFrame, removing any unnecessary whitespace. Utilizing these methods will make your data processing and analysis tasks more efficient and easier to manage.

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To keep improving your data science skills, make sure you know what you’re going yourself into:

Recommended: Data Scientist – Income and Opportunity

Many businesses will agree that it takes a lot more time, money, and resources to get new customers than to keep existing ones. Hence, they are very much interested in knowing how many existing customers are leaving their business. This is known as churn.

Churn tells business owners how many customers are no longer using their products and services. It is also the rate at which an amount of money is lost as a result of customers or employers leaving the company. The churn rate gives companies an idea of business performance. If the churn rate is higher than the growth rate, it means that the business is not growing.

There are many reasons offered to explain customer churn. These include poor customer satisfaction, finance issues, customers not feeling appreciated, and customers’ need for a change. Understandably, companies have no absolute control over churn. But they can work to reduce to the barest minimum churn rate as regards the ones they have greater control.

As data scientists, your role is to assist these companies by building a churn model tailored to the company’s goals and expectations to predict customer churn. Due to the lack of data available to meet a company’s specific needs, it becomes challenging for data scientists to design an effective churn model.

However, we will make do with sample data for a fictional telecommunication company. You know, it is membership-based businesses performing subscription-based services that are mostly affected by customer churn. This data sourced by the IBM Developer Platform is available on my GitHub page.

The dataset has 7043 rows and 21 columns which comprise 17 categorical features, 3 numerical features, and the prediction feature. Check my GitHub page for more information about the dataset.

Data Preprocessing

This step will be taken to make the data suitable for machine learning. We will start by getting an overview of the dataset.

import pandas as pd
df = pd.read_csv('churn.csv') # get the shape of the dataset
df.shape
(7043, 21) # print the columns
df.columns
Index('customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'], dtype='object') # check for missing values df.isna().sum() '''
customerID 0
gender 0
SeniorCitizen 0
Partner 0
Dependents 0
tenure 0
PhoneService 0
MultipleLines 0
InternetService 0
OnlineSecurity 0
OnlineBackup 0
DeviceProtection 0
TechSupport 0
StreamingTV 0
StreamingMovies 0
Contract 0
PaperlessBilling 0
PaymentMethod 0
MonthlyCharges 0
TotalCharges 0
Churn 0
dtype: int64 ''' #check for duplicates
df.customerID.nunique()
7043

Next, we drop the customerID column which was just there for identification purposes.

df.drop(['customerID'], axis=1, inplace=True)

The axis=1 means the columns. The inplace parameter is directly applied to the dataset.

If you take a look at the dataset using the head() method, you will notice that many features including the target feature have rows with values of Yes and No. We will transform them to 0 and 1 using LabelEncoder from the Scikit-learn library. We will also do the same with columns that have more than two categories.

from sklearn.preprocessing import LabelEncoder label_encoder = LabelEncoder()
obj = (df.dtypes == 'object')
for col in list(obj[obj].index): df[col] = label_encoder.fit_transform(df[col])

Model Building

It’s now time to train our data using Machine Learning algorithms. As we don’t know which model will perform well on our dataset, we will first test using different models.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier X = df.drop([‘Churn’], axis=1)
Y = df.Churn X_train, X_test Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=7) models = [LogisticRegression(), RandomForestClassifier(),AdaBoostClassifier(), SVC(), DecisionTreeClassifier(), KNeighborsClassifier(), GaussianNB(), ExtraTreesClassifier(), LinearDiscriminantAnalysis(), GradientBoostingClassifier(), ] scaler = StandardScaler()
rescaledX = scaler.fit_transform(x_train) for model in models: model.fit(rescaledX, Y_train.values) preds = model.predict(X_test.values) results = accuracy_score(Y_test, preds) print(f'{results}') '''
0.2753726046841732
0.7388218594748048
0.7388218594748048
0.7388218594748048
0.2753726046841732
0.26330731014904185
0.47906316536550747
0.27324343506032645
0.7388218594748048
0.30376153300212916
0.6593328601845281
0.7402413058907026 '''

The results show that XGBoost performed better than the other models in this dataset. Therefore, we will use XGBoost as our Machine Learning algorithm to predict customer churn.

Tuning XGBoost

The XGBoost algorithm achieved a 74% accuracy score. Can it do better? Let’s try tuning the model using learning curves. To understand what we meant by the learning curve, please read this article.

models = [LogisticRegression(), RandomForestClassifier(),AdaBoostClassifier(), SVC(), DecisionTreeClassifier(), KNeighborsClassifier(), GaussianNB(), ExtraTreesClassifier(), LinearDiscriminantAnalysis(), GradientBoostingClassifier(), ] scaler = StandardScaler()
rescaledX = scaler.fit_transform(x_train) for model in models: model.fit(rescaledX, Y_train.values) preds = model.predict(X_test.values) results = accuracy_score(Y_test, preds) print(f'{results}')

The results show that XGBoost performed better than the other models in this dataset. Therefore, we will use XGBoost as our Machine Learning algorithm to predict customer churn.

Tuning XGBoost

The XGBoost algorithm achieved a 74% accuracy score. Can it do better? Let’s try tuning the model using learning curves. To understand what we meant by the learning curve, please read this article.

# define the model
model = XGBClassifier() # define the datasets to evaluate each iteration
evalset = [(X_train, Y_train), (X_test, Y_test)] # fit the model
model.fit(X_train, Y_train, eval_metric='logloss', eval_set=evalset) # evaluate performance
preds = model.predict(X_test)
score = accuracy_score(y_test, preds) print(f'Accuracy: {round(score*100, 1)}%')
# Accuracy: 77.9%

Wow, the model has improved with 77.9% accuracy score. Can it still do better? Let’s increase the number of iterations from 100 (default) to 200 and reduce the eta hyperparameter to 0.05 (default is 0.3) to slow down the learning rate.

model = XGBClassifier(n_estimators=200, eta=0.05) # fit the model
model.fit(X_train, Y_train, eval_metric='logloss', eval_set=evalset) preds = model.predict(x_test) score = accuracy_score(y_test,preds) print(f'Accuracy: {round(score*100, 1)}%')
# Accuracy: 78.6%

This is the extent we can go. Of course, we can go on tuning the model to achieve a higher score. An accuracy score of 78.6% is not bad.

Create a new folder and save the following to a file named model.py.

#Import libraries
import pandas as pd
from xgboost import XGBClassifier
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder df = pd.read_csv('churn.csv') # Drop customerID
df.drop(['customerID'], axis=1, inplace=True) # Convert to int datatype label_encoder = LabelEncoder()
obj = (df.dtypes == ‘object’)
for col in list(obj[obj].index): df[col] = label_encoder.fit_transform(df[col]) X = df.drop(['Churn'], axis=1)
Y = df.Churn # splitting the dataset
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=7) model = XGBClassifier(n_estimators=200, eta=0.05) # define the datasets to evaluate each iteration
evalset = [(X_train, Y_train), (X_test, Y_test)] # fit the model
model.fit(X_train, Y_train, eval_metric='logloss', eval_set=evalset) # saving the trained model
pickle.dump(model, open('lg_model.pkl', 'wb'))

Notice we save the trained model as a pickle object to be used later. We want the model to be running on Streamlit local server. So, we will create a Streamlit application for this. Create other files called app.py and predict.py in your current folder. Check my GitHub page to see the full content of the files.

Please remember to manually run the model.py to generate the pickle file as I won’t be pushing it to GitHub. After running the model.py file, the accuracy was 80.4% showing the model learned the data very well.

Conclusion

In this tutorial, we created a customer churn prediction app to help businesses deal with some of the challenges facing them. We use the XGBoost model to train the data and generate the model. There are many things we didn’t do. Data visualization, feature engineering, and dealing with imbalance classification are some of them.

You may wish to try them out and see if they can improve the model’s performance. Unfortunately, I wasn’t able to deploy the app because I couldn’t push the heavy pickle file to GitHub. Try pushing yours and then, you deploy it on Streamlit Cloud. Alright, enjoy your day.

I recently started a new website and to rank up in the search engines I hired an SEO expert. He suggested several activities out of which one was building lots of backlinks. We agreed on the budget and the timeframe in which he delivers 200 links in a month. These links are a mix of social bookmarks, profile pages, and guest articles. After he delivers the Links, I have to verify the work done and give feedback. The process of checking a URL containing the backlink is as follows –

• Open a URL from a list of URLs in the sheet
• Search for my “mydomain.com” in the source code or click on the visible link pointing to my domain
• In the sheet, mark it as LINK FOUND or LINK NOT FOUND

Only if it was this easy.

The link-building project will go on for several months. He will be building several hundred links so I must have some tool to do the checking for me. So, I came up with the idea of asking ChatGPT to give me a code in Python. Here is my prompt:

Prompt

Give me a Python code that scrapes all given URLs and looks for any link that contains "mydomain.com" and prints out the URL and the link it found

ChatGPT reply:

import requests
from bs4 import BeautifulSoup # list of URLs to scrape
urls = ['https://example.com', 'https://example.net', 'https://example.org'] # loop through each URL
for url in urls: # send a request to the URL response = requests.get(url) # parse the HTML content with BeautifulSoup soup = BeautifulSoup(response.content, 'html.parser') # find all links in the page links = soup.find_all('a') # loop through each link for link in links: # check if the link contains "mydomain.com" if 'partymap.in' in link.get('href', ''): # print the URL and link found print(f"{url} - {link.get('href')}")

Choosing Google Colab as Web-Based Python Interface

Now I have to run this code in a web interface. The interface should have an input box and an output box.

I find Google Collab to be the best option to do this in a quick, easy, and time-efficient manner. But I need the code to read the list of URLs from an input widget box. So I returned to ChatGPT and defined my environment along with many improvisations. There were several trials and errors.

Here are some of the prompts

Improvisation Prompt 1:

Also add the following Display domains that are duplicate
Display unique list of domains in which the string was not found

Improvisation Prompt 2:

I got this error ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:992)

Improvisation Prompt 3:

Check for Redirection, if the URL redirects, print "E:REDIRECTED" and skip iteration

Improvisation Prompt 4:

I got a mod_security error in request.get, how can I fix it

Improvisation Prompt 5:

Add a try catch block around request and beautiful soup

Improvisation Prompt 6:

If there are no Links found, print "E:ZERO LINKS" and skip iteration

Improvisation Prompt 7:

The list of URLs will come from a google collab input box can you make the change

And there were many more prompts to achieve the final results. But, since I am a Python coder, I could exit the back and forth with ChatGPT and change the code my way.

ERROR/STATUS CODES

Explanation of error codes is as follows

Errors found in URL given in the sheet

• UNRESOLVED – The URL in the sheet is malformed
• DUPLICATE DOMAIN – There are multiple URLs from the same domain
• REDIRECTED – The URL redirected to another URL, if this happens ask the SEO analyst to post the final URL in the sheet

Errors found in Links found in the source code of the URL

• FOUND – Our domain backlink was found
• NOT FOUND – Our domain backlink was not found
• BAD LINK – Our domain backlink was not found
• ZERO LINKS – No links were found in the source code

I begin each error code with ‘E:’ to easily identify them in sheet for conditional formatting process.

So here is the final code:

The Code

This goes in the first code cell of Google Colab

from IPython.display import display
import ipywidgets as widgets url_box = widgets.Textarea( placeholder='Enter URLs here', description='URLs:', layout=widgets.Layout(width='70%')
) # display the text box widget
display(url_box)

This goes in the second code cell of Google Colab

/enl

import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse # disable SSL certificate verification
requests.packages.urllib3.disable_warnings() # get the input URLs as a list
urls = url_box.value.split()
# create lists to store URLs and domains
scraped_urls = []
unique_domains = []
duplicate_domains = []
notfound_domains = []
inputstring = "" headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
} # loop through each URL
for url in urls: parsed_url = urlparse(url) domain = parsed_url.netloc
# add the domain to the list of unique domains if domain not in unique_domains: unique_domains.append(domain) else: # add the domain to the list of duplicate domains if domain not in duplicate_domains: duplicate_domains.append(domain) print("Duplicate domains:", len(duplicate_domains))
print(duplicate_domains)
print() # loop through each URL and check if the backlink exists
for url in urls: inputstring = "" parsed_url = urlparse(url) domain = parsed_url.netloc if not domain: print('E:UNRESOLVED',',',domain) continue if domain in duplicate_domains: print("E:DUPLICATE DOMAIN") continue # send a request to the URL try: response = requests.get(url, headers=headers, verify=False) except Exception as e: print('REQ:',str(e)) # check if the URL is redirecting to "mydomain.com" # check if the response is a redirect if hasattr(response, 'is_redirect') and response.is_redirect: print("E:REDIRECTED",',',domain) continue # parse the HTML content with BeautifulSoup try: soup = BeautifulSoup(response.content, 'html.parser') except Exception as e: print('BS:',str(e)) # find all links in the page links = soup.find_all('a') # print(links) #if no links found if len(links) == 0: print('E:ZERO LINKS',',',domain) continue # loop through each link for link in links: # Get the domain name from the link parsed_url = urlparse(link.get('href', '')) domain_name = parsed_url.netloc # print(domain_name) # domain_name = link.get('href', '') if domain_name: # Check if the domain name is "mydomain.com" if 'mydomain.com' in domain_name: # print(domain_name) inputstring = "FOUND" break else: inputstring = "E:NOT FOUND" # if domain not in notfound_domains: # notfound_domains.append(domain) else: inputstring = "E:BAD LINK" # add the URL to the list of scraped URLs # scraped_urls.append(inputstring) print(inputstring,',',domain)

See the CELL setup in the image. Press play in the first cell. You will get a URL input box. Paste your URLs in it.

Input Box:

https://sketchfab.tld/mydomain https://30seconds.tld/mydomain/
https://speakerdeck.tld/mydomainus
https://www.ted.tld/profiles/<some page>/about
https://dzone.tld/users/mydomainindia.html
https://www.reddit.tld/user/mydomainusa
https://medium.tld/@mydomainusa/about
https://www.pinterest.tld/mydomainusa/
https://www.intensedebate.tld/people/mydomainusa
https://www.growkudos.tld/profile/<some page>
https://www.universe.tld/users/<some page>
https://www.dostally.tld/post/<some page>
https://www.socialbookmarkzone.info/<some page>
https://app.raindrop.io/my/-1/item/<somepage>/web
https://www.tamaiaz.tld/posts/<somepage>
https://www.socialbookmarkzone.info/<some page>/
https://gab.tld/mydomain/posts/<some page>

Now press Play in the second cell and watch output panel

Output:

Duplicate domains: 5
['www.socialbookmarkzone.tld, 'www.reddit.tld', 'www.instapaper.tld', 'www.wibki.tld', 'diigo.tld'] FOUND , sketchfab.tld
E:BAD LINK , 30seconds.tld
FOUND , speakerdeck.tld
E:BAD LINK , www.ted.tld
FOUND , dzone.tld
E:DUPLICATE DOMAIN
FOUND , medium.tld
FOUND , www.pinterest.tld
FOUND , www.intensedebate.tld
FOUND , www.growkudos.tld
E:ZERO LINKS , www.universe.tld
FOUND , www.dostally.tld
E:DUPLICATE DOMAIN
E:ZERO LINKS , app.raindrop.io
FOUND , www.tamaiaz.tld
E:DUPLICATE DOMAIN
E:NOT FOUND , gab.tld

INPUT BOX CODE [GOOGLE COLLAB]

GOOGLE COLLAB CODE CELL SETUP

PASTE THE OUTPUT IN YOUR SEO TRACKER SHEET in the same line as the URLs & APPLY SPLIT TEXT TO COLUMN

STEPS TO APPLY CONDITIONAL FORMATTING

FINAL OUTPUT

Based on the above output the SEO analyst can rework on the links or drop these sites completely.

If you like the code leave a comment and I am available on Upwork for Prompt Engineering, AI Art jobs. I use ChatGPT, Midjourney, Python and many more tools for my client jobs.

My Upwork profile is https://www.upwork.com/freelancers/~018645334d3b757e4d

---

Recommended: 7 Effective Prompting Tricks for ChatGPT

Playing with any large language model (LLM), such as GPT-4, is fascinating. 

But it doesn’t give you an accurate understanding of where AGI is heading because one isolated snapshot provides limited information. You can gain more insight into the growth and dynamicity of LLMs by comparing two subsequent snapshots.

Roughly speaking, it’s less interesting to see where baby AGI is and more interesting to look at how it evolves. 

To gain more insight on this, Emily has just contributed another interesting Finxter blog article:

Recommended: [Blog] 10 High-IQ Things GPT-4 Can Do That GPT-3.5 Can’t

Check it out. It’s a solid read!

It’s fascinating to observe how the concept of transformers introduced in the 2017 paper “Attention is all you need” has scaled so remarkably well. 

In essence, the significant advancements made in AI over the past four years have mostly come from scaling up the transformer approach to an incredible magnitude. The concept of GPT (Generative Pre-trained Transformers) has remained largely unchanged for around six years.

They just threw more data and more hardware on the same algorithm. This was possible due to the higher amount of scalability and degree of parallelization unlocked by the transformer idea.

From the paper (highlights by me):

“In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization … the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.”

My main takeaway from comparing GPT-3.5 to GPT-4 is that the limits of performance improvements are not yet reached by simply throwing more and more data and hardware on these models. And when the performance (=IQ) of transformer models ultimately converges — probably at a super-human IQ level — we’ll still be able to change and improve on the underlying abstractions to eke out additional IQ.

Likely, transformers will not remain the last and best-performing model for all future AI research. We have tried only the tip of the iceberg on what scale these models go. I wouldn’t be surprised if the data sets and computational power of future GPT models increased by 1,000,000x.

Truly an exciting time to be alive!  

I’m scared and fascinated at the same time. It’s so new and so dangerous. Ubiquitous disruption of the work marketplace is already happening fast. I’d estimate that in our economy, we already have north of one billion “zombie jobs”, i.e., job descriptions that could be fully automated with ChatGPT and code. I know of closed-loop AI models under government review that classify cancer with almost zero error rate. Medical doctors with lower accuracy are still doing the classification – but for how long? 

A new era is starting. When we went from 99% to 1% farmers, we accomplished a massive leap of free work energy that led to an explosion of collective intelligence. The same is happening now: 99% of the jobs will be gone sooner than we expect. A massive amount of free energy will catapult humanity forward like we’ve never experienced in the history of humanity.

Buckle up for the ride. I’ll be here to help you navigate the waters until my job will be disrupted too and AGI will help you more effectively than I ever could. 

The future is bright!

Chris

---

This was part of my free newsletter on technology and exponential technologies. You can join us by downloading our cheat sheets here:

In this article, I will cover accessing multiple matches of a regex group in Python.

Regular expressions (regex) are a powerful tool for text processing and pattern matching, making it easier to work with strings. When working with regular expressions in Python, we often need to access multiple matches of a single regex group. This can be particularly useful when parsing large amounts of text or extracting specific information from a string.

To access multiple matches of a regex group in Python, you can use the re.finditer() or the re.findall() method.

• The re.finditer() method finds all matches and returns an iterator yielding match objects that match the regex pattern. Next, you can iterate over each match object and extract its value.
• The re.findall() method returns all matches in a list, which can be a more convenient option if you want to work with lists directly.

Problem Formulation: Given a regex pattern and a text string, how can you access multiple matches of a regex group in Python?

Understanding Regex in Python

In this section, I’ll introduce you to the basics of regular expressions and how we can work with them in Python using the ‘re‘ module. So, buckle up, and let’s get started!

Basics of Regular Expressions

Regular expressions are sequences of characters that define a search pattern. These patterns can match strings or perform various operations like search, replace, and split into text data.

Some common regex elements include:

• Literals: Regular characters like 'a', 'b', or '1' that match themselves.
• Metacharacters: Special characters like '.', '*', or '+' that have a special meaning in regex.
• Character classes: A set of characters enclosed in square brackets (e.g., '[a-z]' or '[0-9]').
• Quantifiers: Specify how many times an element should repeat (e.g., '{3}', '{2,5}', or '?').

These elements can be combined to create complex search patterns. For example, the pattern '\d{3}-\d{2}-\d{4}' would match a string like '123-45-6789'.

Remember, practice makes perfect, and the more you work with regex, the more powerful your text processing skills will become.

The Python ‘re’ Module

Python comes with a built-in module called ‘re‘ that makes it easy to work with regular expressions. To start using regex in Python, simply import the ‘re‘ module like this:

import re

Once imported, the ‘re‘ module provides several useful functions for working with regex, such as:

Function	Description
re.match()	Checks if a regex pattern matches at the beginning of a string.
re.search()	Searches for a regex pattern in a string and returns a match object if found.
re.findall()	Returns all non-overlapping matches of a regex pattern in a string as a list.
re.finditer()	Returns an iterator yielding match objects for all non-overlapping matches of a regex pattern in a string.
re.sub()	Replaces all occurrences of a regex pattern in a string with a specified substitution.

By using these functions provided by the ‘re‘ module, we can harness the full power of regular expressions in our Python programs. So, let’s dive in and start matching!

Working with Regex Groups

When working with regular expressions in Python, it’s common to encounter situations where we need to access multiple matches of a regex group. In this section, I’ll guide you through defining and capturing regex groups, creating a powerful tool to manipulate text data.

Defining Groups

First, let’s talk about how to define groups within a regular expression. To create a group, simply enclose the part of the pattern you want to capture in parentheses. For example, if I want to match and capture a sequence of uppercase letters, I would use the pattern ([A-Z]+). The parentheses tell Python that everything inside should be treated as a single group.

Now, let’s say I want to find multiple groups of uppercase letters, separated by commas. In this case, I can use the pattern ([A-Z]+),?([A-Z]+)?. With this pattern, I’m telling Python to look for one or two groups of uppercase letters, with an optional comma in between.

Capturing Groups

To access the matches of the defined groups, Python provides a few helpful functions in its re module. One such function is findall(), which returns a list of all non-overlapping matches in the string.

For example, using our previous pattern:

import re
pattern = r'([A-Z]+),?([A-Z]+)?'
text = "HELLO,WORLD,HOW,AREYOU"
matches = re.findall(pattern, text)
print(matches)

This code would return the following result:

[('HELLO', 'WORLD'), ('HOW', ''), ('ARE', 'YOU')]

Notice how it returns a list of tuples, with each tuple containing the matches for the specified groups.

Another useful function is finditer(), which returns an iterator yielding Match objects matching the regex pattern. To extract the group values, simply call the group() method on the Match object, specifying the index of the group we’re interested in.

An example:

import re
pattern = r'([A-Z]+),?([A-Z]+)?'
text = "HELLO,WORLD,HOW,AREYOU" for match in re.finditer(pattern, text): print("Group 1:", match.group(1)) print("Group 2:", match.group(2))

This code would output the following:

Group 1: HELLO
Group 2: WORLD
Group 1: HOW
Group 2:
Group 1: ARE
Group 2: YOU

As you can see, using regex groups in Python offers a flexible and efficient way to deal with pattern matching and text manipulation. I hope this helps you on your journey to becoming a regex master!

Accessing Multiple Matches

As a Python user, sometimes I need to find and capture multiple matches of a regex group in a string. This can seem tricky, but there are two convenient functions to make this task a lot easier: finditer and findall.

Using ‘finditer’ Function

I often use the finditer function when I want to access multiple matches within a group. It finds all matches and returns an iterator, yielding match objects that correspond with the regex pattern .

To extract the values from the match objects, I simply need to iterate through each object :

import re pattern = re.compile(r'your_pattern')
matches = pattern.finditer(your_string) for match in matches: print(match.group())

This useful method allows me to get all the matches without any hassle. You can find more about this method in PYnative’s tutorial on Python regex capturing groups.

Using ‘findall’ Function

Another option I consider when searching for multiple matches in a group is the findall function. It returns a list containing all matches’ strings. Unlike finditer, findall doesn’t return match objects, so the result is directly usable as a list:

import re pattern = re.compile(r'your_pattern')
all_matches = pattern.findall(your_string) print(all_matches)

This method provides me with a simple way to access all the matches as strings in a list.

Practical Examples

Let’s dive into some hands-on examples of how to access multiple matches of a regex group in Python. These examples will demonstrate how versatile and powerful regular expressions can be when it comes to text processing.

Extracting Email Addresses

Suppose I want to extract all email addresses from a given text. Here’s how I’d do it using Python regex:

import re text = "Contact me at [email protected] and my friend at [email protected]"
pattern = r'([\w\.-]+)@([\w\.-]+)\.(\w+)'
matches = re.findall(pattern, text) for match in matches: email = f"{match[0]}@{match[1]}.{match[2]}" print(f"Found email: {email}")

This code snippet extracts email addresses by using a regex pattern that has three capturing groups. The re.findall() function returns a list of tuples, where each tuple contains the text matched by each group. I then reconstruct email addresses from the extracted text using string formatting.

Finding Repeated Words

Now, let’s say I want to find all repeated words in a text. Here’s how I can achieve this with Python regex:

import re text = "I saw the cat and the cat was sleeping near the the door"
pattern = r'\b(\w+)\b\s+\1\b'
matches = re.findall(pattern, text, re.IGNORECASE) for match in matches: print(f"Found repeated word: {match}")

Output:

Found repeated word: the

In this example, I use a regex pattern with a single capturing group to match words (using the \b word boundary anchor). The \1 syntax refers to the text matched by the first group, allowing us to find consecutive occurrences of the same word. The re.IGNORECASE flag ensures case-insensitive matching. So, no repeated word can escape my Python regex magic!

Conclusion

In this article, I discussed how to access multiple matches of a regex group in Python. I found that using the finditer() method is a powerful way to achieve this goal. By leveraging this method, I can easily iterate through all match objects and extract the values I need.

Along the way, I learned that finditer() returns an iterator yielding match objects, which allows for greater flexibility when working with regular expressions in Python. I can efficiently process these match objects and extract important information for further manipulation and analysis.

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As artificial intelligence (AI) continues to make strides in tech, the new “hot” jobs look nothing like the old ones. Programming has just been made accessible to billions via a new skill called “prompting”.

The new hot Silicon Valley job role is that of a Prompt Engineer, who plays a critical part in training and “pulling” value out of AI chatbots, particularly those utilizing the ChatGPT framework, to improve their responses to user inputs. Given that millions of new apps emerge that have “ChatGPT at the heart“, i.e., the backend consists of an engineered ChatGPT prompt and nothing else, prompt engineers are highly sought-after in today’s marketplace!

Figure: Prompting is the skill of the exponential age!

But is it the right job for you? Let’s answer one part of this question, i.e., How much dough can you make as a prompt engineer in the US?

Given the complexity and expertise required for prompt engineering, professionals in this field are compensated accordingly to their skills and experience. Annual income for prompt engineers in the US can range from $95,900 to $180,000, with some positions even offering up to $335,000 a year [1][2].

Prompt Engineer Annual Income ($ USD)	Source
$175,000 – $335,000	Bloomberg
$250,000 – $335,000	Anthropic AI
$76,000 – $113,000	FutureWork
$200,000 – $370,000	OpenAI Prompt Engineer
$73,000 – $438,000	ZipRecruiter

With job postings numbering in the thousands [1], the demand for prompt engineers is clear, showcasing the value that companies place on these professionals.

The significance of prompt engineering stems from the concept of garbage in, garbage out, which essentially posits that the quality of an AI’s input determines the quality of its output [3]. As AI continues to develop, companies must invest in skilled prompt engineers to ensure continuous improvement and refinement of chatbot responses.

Overview of ChatGPT

ChatGPT is a powerful language model that has revolutionized the way we interact with artificial intelligence (AI) systems. As a member of the large language models (LLMs) family, ChatGPT excels in understanding and generating human-like responses based on given prompts . Prompt engineering is an essential skill for working with ChatGPT, as it helps enforce rules, automate processes, and tailor the generated output to specific needs and requirements .

With ChatGPT’s increasing importance in industries like customer support, content creation, and programming, prompt engineers are in high demand .

In the United States, annual salaries for these professionals range from $95,900 to $180,000, with 3,916 job postings available on Indeed.com at the time of writing [source]. The job market for prompt engineers is thriving across various sectors, thanks to the versatility of the ChatGPT technology .

One of the key elements of prompt engineering is a set of prompt patterns that help guide ChatGPT’s responses. These patterns are effectively communicated through prose rather than code, making it possible for non-programmers to contribute immensely to the AI field .

In a nutshell, ChatGPT’s capabilities coupled with the growing need for prompt engineering skills offer promising opportunities for professionals seeking high-income careers in AI-driven sectors .

Role of Prompt Engineers

Prompting is the new programming. Prompt engineers get the most out of large language models (LLMs) such as ChatGPT by asking the right questions and in the right way using informal natural language rather than a formal programming language. By improving these prompts, prompt engineers advance the capabilities of AI language models for various applications, such as chatbots and language translation software.

Prompt engineers play a crucial role in the development and refinement of AI chatbots like ChatGPT. These experts work with prose, rather than code, to test the AI system’s functionality and identify its shortcomings . This helps developers address any issues and maximize the AI’s potential ^source.

Their responsibilities include crafting carefully worded prompts to uncover hidden capabilities and vulnerabilities in the AI. This process enables prompt engineers to work closely with developers, optimizing the chatbot’s performance and ensuring user satisfaction ^source.

Skills of Prompt Engineers

Hard Skills

Crucial hard prompt engineering skills include:

• Understanding the nuances of large language models (LLMs) like ChatGPT^source
• Writing effective and precise prompts to enforce rules, automate processes, and guide the AI’s output quantity and quality ^source
• Strong understanding of natural language processing (NLP) and machine learning (ML): A prompt engineer should have a solid foundation in NLP and ML to create effective prompts that generate accurate and relevant responses.
• Continuous learning and improvement: A prompt engineer should be committed to continuous learning and improvement to stay up-to-date with the latest advancements in NLP and ML and improve their skills and knowledge. After all, one thing we can be sure of is that prompting in 3 years will look nothing like today!

Soft Skills

Optional but helpful “soft” prompt engineering skills include:

• Proficiency in programming languages: Knowledge of programming languages such as Python, Java, and C++ is useful for prompt engineers to develop and implement effective algorithms.
• Experience with deep learning frameworks: Familiarity with deep learning frameworks such as TensorFlow and PyTorch is helpful but optional for prompt engineers to design and train neural networks for language generation.
• Understanding of data structures and algorithms: A prompt engineer should have a solid understanding of data structures and algorithms to develop efficient and scalable solutions for language generation.
• Knowledge of database management: A prompt engineer should be proficient in database management to store and retrieve large amounts of data required for language generation.
• Strong analytical and problem-solving skills: A prompt engineer should have strong analytical and problem-solving skills to analyze large amounts of data, identify patterns, and develop effective solutions for language generation.
• Excellent communication and collaboration skills: A prompt engineer should have excellent communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
• Creative thinking and innovation: A prompt engineer should be able to think creatively and innovatively to develop unique and effective prompts that generate accurate and relevant responses.
• Attention to detail: A prompt engineer should have a keen eye for detail to ensure that the prompts they create are accurate and free of errors.

As industries increasingly rely on AI chatbots, the demand for prompt engineers is set to grow . The role combines creative thinking, language expertise, and a deep understanding of AI to ensure the technology delivers on its promise of effective automation and productive brainstorming ^source.

Here are the specific requirements of a real “prompt engineering” job in the wild (source):

You can learn the basics of prompting quickly, e.g., download our prompting cheat sheet or check out the following prompting tips:

Recommended: 7 Effective Prompting Tricks for ChatGPT

Factors Affecting Annual Income

This section will discuss various factors influencing the annual income of prompt engineers working with ChatGPT. Several elements contribute to the differences in pay, including experience, location, education, and the industry they work in.

Experience

Like any profession, experience plays a significant role in determining the salary of prompt engineers. Their income is likely to increase as their skills develop and they gain a deeper understanding of the technology.

Experts in the field can command salaries ranging from $250,000 to $330,000 a year, reflecting their exceptional proficiency and talent in handling AI systems like ChatGPT (source).

Location

The geographical location of a prompt engineer can also impact their earnings. Specific areas, especially tech hubs like Silicon Valley or Seattle, may offer higher salaries due to the concentration of large tech companies, startups, and innovative projects. However, the cost of living in these regions may also be higher, potentially affecting take-home pay.

Education

While some candidates may be primarily self-taught or come from diverse educational backgrounds, having a formal education in a relevant field, such as computer science, engineering, or linguistics, can positively impact prompt engineers’ salaries.

Employers may view an advanced degree as an indicator of a candidate’s dedication and expertise in their craft, leading to higher compensation packages.

Recommended: 10 Best ChatGPT Books for AI Enthusiasts

Industry

The industry in which a prompt engineer works can also influence their income. Different sectors may require specialized knowledge or expertise, which could translate to premium pay for those with the right skills.

For example, AI applications in finance, health care, or legal services might demand prompt engineers with domain-specific experience, leading to higher salaries in those industries.

Current Salaries and Trends

The demand for prompt engineers working with ChatGPT has surged in recent years, leading to attractive salary packages across the United States. A prime example is San Francisco-based AI start-up Anthropic, which currently offers a salary range of $175,000 to $335,000 for a prompt engineer and librarian role (source).

Such competitive salaries reflect prompt engineers’ expertise in the cutting-edge AI field , with their skills significantly impacting the development and performance of language models that cater to various industries.

Although salary ranges vary depending on factors such as location and experience level, prompt engineers typically enjoy higher incomes than their counterparts in other engineering professions. For context, the median annual wage for engineers across the United States was $91,098 in 2023, with 33.8k salaries reported (source).

Considering these figures, it’s evident that prompt engineering positions in the emerging AI sector are a lucrative choice for professionals seeking attractive career opportunities .

Some noteworthy trends in the prompt engineering domain include:

• Increased demand for AI prompt engineers in tech hubs like Silicon Valley
• A growing focus on cross-disciplinary skillsets, such as NLP and programming languages like Python
• Collaboration among industry professionals to address ethical concerns surrounding AI development

Overall, the field of prompt engineering is dynamic and ever-evolving, with ample prospects for professionals looking to carve a niche in this exciting domain .

Comparisons to Similar Roles

When considering the annual income of prompt engineers, it’s helpful to compare their salaries with those of similar roles in the tech industry. For instance, let’s take a look at data scientists, software engineers, and AI researchers.

Data scientists, who analyze and interpret large datasets to assist businesses in decision-making, often earn around $120,000 annually in the US.

Recommended: Data Scientist – Income and Opportunity

On the other hand, software engineers, responsible for designing, coding, and testing applications, can expect yearly salaries ranging from $110,000 to 180,000, depending on their experience and location within the country.

Recommended: Python Developer – Income and Opportunity

AI researchers, who study and develop cutting-edge artificial intelligence algorithms, typically receive higher compensation than data scientists and software engineers. Their annual salaries can start at $150,000 and go as high as $500,000 for highly experienced individuals or those working at prestigious research institutions.

Recommended: Machine Learning Engineer – Income and Opportunity

Compared to these roles, prompt engineers enjoy competitive salaries, some reaching up to $335,000 a year, despite not requiring degrees in tech fields. This can be attributed to the unique combination of skills they possess, as well as the growing demand for experts who can effectively test and improve generative AI models like ChatGPT.

Job Growth and Demand

Again, this graphic tells an exciting story:

The rise of AI has generated a new job title: Prompt Engineer. These professionals specialize in working with ChatGPT systems and are in high demand.

As AI adoption increases across various industries, the need for skilled prompt engineers continues to grow. Companies like Anthropic, a Google-backed startup, are offering significant salaries, ranging from $175,000 to $335,000 per year for this role.

Prompt engineering is considered a valuable skill in the age of AI, as it improves the overall productivity of specific occupations, like lawyers and CPAs. As more companies integrate AI technologies into their operations, job opportunities for prompt engineers are expected to increase.

Interestingly, some companies are hiring prompt engineers with little to no technical background. This highlights the value placed on communication and language understanding in the field. As artificial intelligence becomes more entrenched in everyday life, prompt engineering skills may eventually become as essential as learning how to use a search engine (Axios).

Overall, the job growth and demand for prompt engineers is on a promising trajectory as the AI industry continues to expand. One thing is certain: prompt engineers will play a key role in shaping the future of AI-powered communication and productivity.

Recommended: GPT4All Quickstart – Offline Chatbot on Your Computer

Conclusion

In summary, ChatGPT prompt engineers have become crucial professionals in the AI industry. With a growing demand and a salary that can reach up to $300,000 a year, they are carving out an important niche with a bright future.

Their work in developing and refining AI system interactions has made technology more efficient and user-friendly. Companies like OpenAI, Alphabet, and Meta are making significant investments in generative AI technologies, further highlighting the importance of these specialists.

As artificial intelligence continues to grow, we can expect prompt engineers to play a valuable role in shaping how human-AI conversations evolve. Exciting times lie ahead for this field, so let’s watch this space as AI continues its upward trajectory!

Recommended: How I Created a High-Performance Extensible ChatGPT Chatbot with Python (Easy)

No, ChatGPT really doesn’t help with this (yet…) but it did help me write the code snippet explanation (which I attached after each code output).

In my example, we will take a special route in Budapest, using one of the easiest to use library, gmplot.

We can create our maps directly in HTML code using its many methods.
To access some functions, you need a Google Maps API key.

I used Python’s built-in zip function to create the Lat – Lon coordinates, you can learn more about zip here:

Recommended: Python zip() Built-in Function

We can work with the following methods of the GoogleMapPlotter object, which I will describe in detail:

GoogleMapPlotter

• from_geocode(location, zoom=10, apikey='')
• draw(path_file)
• get()
• geocode(location, apikey='')
• text(lat, lon, text, color='red')
• marker(lat, lon, color='#FF0000', title=None, precision=6, label=None, **kwargs)
• enable_marker_dropping(color, **kwargs)
• directions
• scatter
• circle
• plot
• heatmap
• ground_overlay
• polygon
• grid

Let’s go through the functions using “code snippets”:

Find a location by name (from_geocode)

With the geocode method of the GoogleMapPlotter object, we can display a given address and its neighbourhood on the map.

Parameters of the method: location (str), zoom (int), apikey (str)

After creating your map, you have two options, either save the HTML file (.draw()) or store it as a string (with .get()). I use the .draw() method, where the only parameter is the file to create and its path.

import gmplot
apikey = '' # (your API key here)
gmap = gmplot.GoogleMapPlotter.from_geocode('Budapest', apikey=apikey)
gmap.draw("budapest_map.html")

Result:

The code imports the gmplot library, which is a Python wrapper for the Google Maps API. It then creates a new GoogleMapPlotter object named gmap for the location “Budapest” using the from_geocode() method. This method uses the Google Maps API to retrieve the latitude and longitude of the location, which is necessary to display the map.

Finally, the draw() method is called on the gmap object to generate and save the map as an HTML file named budapest_map.html.

Coordinates of a location (geocode)

If you want to know the coordinates of a location, use .geocode(). As an input parameter, pass the name (str) of the place you are looking for and your API key. This returns a tuple of the lat/long coordinates of the given location (float, float).

import gmplot
apikey = '' # (your API key here)
location = gmplot.GoogleMapPlotter.geocode('Budapest, Hungary', apikey=apikey)
print(location)

Result:

(47.497912, 19.040235)

The code calls the geocode() method on the GoogleMapPlotter object to obtain the latitude and longitude of a location specified as a string. In this case, the location is “Budapest, Hungary”. The apikey parameter is also passed to this method to authenticate the Google Maps API.

Text on your map (text)

If you want to place custom text on your map, you can do it with .text(), using the text label’s Latitude and Longitude parameter.

It is possible to color the text with the color=str parameter, which can be the color name ('black'), hexadecimal ('#000000'), or matplotlib-like ('k').

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 17, apikey=apikey)
finxter_text = ['f', 'i', 'n', 'x', 't', 'e', 'r']
colors = ['limegreen', 'cyan', 'gold','orange', 'limegreen', 'cyan', 'orange']
j = 0
lat = 47.529266431577625
lng = 19.00500303401821
for i in finxter_text: gmap.text(lat, lng, i, color = colors[j]) j += 1 lng += 0.001

Result:

Drop marker (marker)

Show markers. The required parameters are, of course, the Latitude and Longitude coordinates (float, float), and additional optional parameters can be used to customize the markers:

• color (str) which can be the name of the color ('black'), hexadecimal ('#000000'), or matplotlib-like ('k')
• title (str) : Hover-over title of the marker.
• precision (int) : Number of digits after the decimal to round to for lat/long values. Defaults to 6.
• label (str) : Label displayed on the marker.
• info_window (str) : HTML content to be displayed in a pop-up info window.
• draggable (bool) : Whether or not the marker is draggable.

import gmplot
apikey = ' ' # (your API key here)
gmap.marker(47.51503432784726, 19.005350430919034, label = 'finxter', info_window = "<a href='https://finxter.com/'>The finxter Academy</a>", draggable = False)

gmap.enable_marker_dropping(color=’black’)

gmap.enable_marker_dropping() allows markers to be dropped onto the map when clicked. Clicking on a dropped marker will delete it.

Note: Calling this function multiple times will just overwrite the existing dropped marker settings.

Parameters:

• color str: Color of the markers to be dropped.
• title str: Hover-over title of the markers to be dropped.
• label str: Label displayed on the markers to be dropped.
• draggable bool: Whether or not the markers to be dropped are draggable.

Result:

The code adds a marker to the Google Maps plot. The marker is placed at the latitude and longitude coordinates (47.51503432784726, 19.005350430919034) and is labeled 'finxter'.

The info_window parameter sets the information displayed when the user clicks on the marker. In this case, it is a link to the Finxter Academy website.

The draggable parameter is set to False, meaning that the user cannot move the marker.

The fourth line enables marker dropping, meaning the user can add new markers to the plot by clicking on the map.

The final line saves the plot to an HTML file named marker.html.

Route planning (directions)

Using the Directions API, you can display route planning between any points. The origin and destination coordinates are given as parameters (float, float). Optionally, the waypoints as list of tuples and the travel_mode as str can also be specified. The travel modes are:

• DRIVING (Default) indicates standard driving directions using the road network.
• BICYCLING requests bicycling directions via bicycle paths & preferred streets.
• TRANSIT requests directions via public transit routes.
• WALKING requests walking directions via pedestrian paths & sidewalks.

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
gmap.directions( (47.5194613766804, 19.000656008676216), (47.520243896650946, 19.00204002854648), waypoints = [(47.520888742275, 18.99871408933636)])
gmap.directions( (47.5194613766804, 19.000656008676216), (47.520243896650946, 19.00204002854648), waypoints = [(47.520888742275, 18.99871408933636)]) gmap.directions( (47.52226897515179, 19.00018393988221), (47.520243896650946, 19.00204002854648), waypoints = [(47.52088149688948, 19.002871513347902)])
gmap.draw('route.html')

Result:

The fourth line adds a route to the plot using the directions() method.

The starting point of the route is at latitude 47.5194613766804 and longitude 19.000656008676216, and the ending point is at latitude 47.520243896650946 and longitude 19.00204002854648.

The waypoints parameter is set to a list containing one set of latitude and longitude coordinates (47.520888742275, 18.99871408933636).

The fifth line adds another route to the plot, starting at the same point as the previous route and ending at the same point as the previous route, but with different waypoints.

The sixth line adds a third route to the plot, with a starting point at latitude 47.52226897515179 and longitude 19.00018393988221, an ending point at latitude 47.520243896650946 and longitude 19.00204002854648, and a set of waypoints containing one set of latitude and longitude coordinates (47.52088149688948, 19.002871513347902).

Display many points (scatter)

The scatter() allows you to place many points at once. In addition to the necessary lat (float) and lon (float) parameters, the following optional parameters are:

• color
• size
• marker
• symbol
• title
• label
• precision
• face_alpha
• edge_alpha
• edge_width

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
letters = zip(*[ (47.51471253011692, 18.990678878050492), (47.51941514547201, 18.993554206158933), (47.52134244386804, 18.998060317311538), (47.52337110249922, 19.002008528961046), (47.52344355313603, 19.009969325319076), (47.52466070898612, 19.013488383565445), (47.526645771633746, 19.02031192332838)])
gmap.scatter(*letters, color=['limegreen', 'cyan','gold','orange', 'limegreen', 'cyan', 'orange'], s=60, ew=1, title=['f', 'i', 'n', 'x', 't', 'e', 'r'], label=['f', 'i', 'n', 'x', 't', 'e', 'r']
)
gmap.draw('scatter.html')

Result:

The fourth line defines a list of latitude and longitude coordinates as tuples, representing the locations of individual letters of the word 'finxter'.

The fifth line uses the scatter() method of adding the letters to the plot as points. The scatter() method takes the latitude and longitude coordinates as separate arguments using the unpacking operator (*letters).

The color parameter is set to a list of colors that correspond to the letters. The s parameter specifies the size of the points, the ew parameter specifies the width of the edge around the points, and the title and label parameters specify the title and label of each point, respectively.

Draw circle (circle)

Sometimes it is useful to draw a circle. In addition to specifying the center lat, lng, and radius of the circle, you can also to specify the following:

• edge_alpha/ea float: Opacity of the circle’s edge, ranging from 0 to 1. Defaults to 1.0.
• edge_width/ew int: Width of the circle’s edge, in pixels. Defaults to 1.
• face_alpha/alpha float: Opacity of the circle’s face, ranging from 0 to 1. Defaults to 0.5.
• color/c/face_color/fc str: Color of the circle’s face. Can be hex (“#00FFFF”), named (“cyan”), or matplotlib-like (“c”). Defaults to black.
• color/c/edge_color/ec

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
gmap.circle(47.51894874729591, 18.99426698678921, 200, face_alpha = 0.4, ec = 'cyan', fc='cyan')

Result:

The fourth line uses the circle() method to add a circle to the plot.

The circle() method takes the latitude and longitude coordinates of the center of the circle as its first two arguments, followed by the radius of the circle in meters.

The face_alpha parameter specifies the transparency of the circle fill, while the ec and fc parameters specify the color of the circle edge and fill, respectively.

Polyline (plot)

A polyline is a line composed of one or more sections. If we want to display such a line on our map, we use the plot method. In addition to the usual lats [float], lons [float] parameters, you can specify the following optional parameters:

• color/c/edge_color/ec str : Color of the polyline. Can be hex (“#00FFFF”), named (‘cyan’), or matplotlib-like (‘c’). Defaults to black.
• alpha/edge_alpha/ea float: Opacity of the polyline, ranging from 0 to 1. Defaults to 1.0.
• edge_width/ew int: Width of the polyline, in pixels. Defaults to 1.
• precision int: Number of digits after the decimal to round to for lat/lng values. Defaults to 6.

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
f = zip(*[(47.513285942712805, 18.994089961008104), (47.51453956566773, 18.991150259891935), (47.51573518971617, 18.992276787691928), (47.51453956566773, 18.991150259891935), (47.51417000363217, 18.992040753295736), (47.515372882275294, 18.99316728109573)])
gmap.plot(*f, edge_width = 7, color = 'limegreen')
gmap.draw('poly.html')

Result:

f = zip(*[(47.513285942712805, 18.994089961008104), (47.51453956566773, 18.991150259891935), (47.51573518971617, 18.992276787691928), (47.51453956566773, 18.991150259891935), (47.51417000363217, 18.992040753295736), (47.515372882275294, 18.99316728109573)])

This line creates a list of latitude-longitude pairs that define the vertices of a polygon. The zip(*[...]) function is used to transpose the list so that each pair of latitude-longitude values becomes a separate tuple.

gmap.plot(*f, edge_width = 7, color = 'limegreen')

This line plots the polygon on the Google Map. The plot() function takes the *f argument, which is unpacked as separate arguments, representing the latitude and longitude values of the polygon vertices. The edge_width parameter sets the width of the polygon edges in pixels and the color parameter sets the color of the edges.

Create heatmap (heatmap)

Plot a heatmap.

Parameters:

• Latitudes [float],
• Longitudes [float]

Optional Parameters:

• radius [int]: Radius of influence for each data point, in pixels. Defaults to 10.
• gradient [(int, int, int, float)]: Color gradient of the heatmap as a list of RGBA colors. The color order defines the gradient moving towards the center of a point.
• opacity [float]: Opacity of the heatmap, ranging from 0 to 1. Defaults to 0.6.
• max_intensity [int]: Maximum intensity of the heatmap. Defaults to 1.
• dissipating [bool]: True to dissipate the heatmap on zooming, False to disable dissipation.
• precision [int]: Number of digits after the decimal to round to for lat/lng values. Defaults to 6.
• weights [float]: List of weights corresponding to each data point. Each point has a weight of 1 by default. Specifying a weight of N is equivalent to plotting the same point N times.

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
letters = zip(*[ (47.51471253011692, 18.990678878050492), (47.51941514547201, 18.993554206158933), (47.52134244386804, 18.998060317311538), (47.52337110249922, 19.002008528961046), (47.52344355313603, 19.009969325319076), (47.52466070898612, 19.013488383565445), (47.526645771633746, 19.02031192332838)]) gmap.heatmap( *letters, radius=55, weights=[0.1, 0.2, 0.5, 0.6, 1.8, 2.10, 1.12], gradient=[(89, 185, 90, 0), (54, 154, 211, 0.5), (254, 179, 19, 0.79), (227, 212, 45, 1)], opacity = 0.7
)
gmap.draw('heatmap.html')

Result:

First, a list of tuples called letters is created. Each tuple contains two values representing latitude and longitude coordinates of a point on the map.

Then, an instance of the GoogleMapPlotter class is created with a specified center point, zoom level, and an API key.

Next, the heatmap method of the GoogleMapPlotter object is called, passing in the letters list as positional arguments, along with other parameters.

The radius parameter determines the radius of each data point’s influence on the heatmap, while the weights parameter determines the intensity of each data point’s contribution to the heatmap.

The gradient parameter is a list of tuples representing the color gradient of the heatmap, with each tuple containing four values representing red, green, blue, and alpha values.

Finally, the opacity parameter determines the transparency of the heatmap.

Picture above the map (ground_overlay)

Overlay an image from a given URL onto the map.

Parameters:

• url [str]: URL of image to overlay.
• bounds [dict]: Image bounds, as a dict of the form {'north':, 'south':, 'east':, 'west':}.

Optional Parameters:

• opacity [float]: Opacity of the overlay, ranging from 0 to 1. Defaults to 1.0.

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
url = 'https://finxter.com/wp-content/uploads/2022/01/image-2.png'
bounds = {'north': 47.53012124664374, 'south': 47.50860174660818, 'east': 19.0247910821219, 'west': 18.985823949220986}
gmap.ground_overlay(url, bounds, opacity=0.3)
gmap.draw('overlay.html')

Result:

The variable url contains the URL of an image that will be used as the ground overlay. The bounds dictionary defines the north, south, east, and west coordinates of the image on the map.

Finally, the ground_overlay method is called on the GoogleMapPlotter object, passing the URL and bounds variables as arguments. The opacity parameter is set to 0.3 to make the overlay partially transparent. The resulting map is saved to a file called overlay.html using the draw method.

Plot a Polygon

Parameters:

• lats [float]: Latitudes.
• lngs [float]: Longitudes.

Optional Parameters:

• color/c/edge_color/ec str: Color of the polygon’s edge. Can be hex (“#00FFFF”), named (“cyan”), or matplotlib-like (“c”). Defaults to black.
• alpha/edge_alpha/ea float: Opacity of the polygon’s edge, ranging from 0 to 1. Defaults to 1.0.
• edge_width/ew int: Width of the polygon’s edge, in pixels. Defaults to 1.
• alpha/face_alpha/fa float: Opacity of the polygon’s face, ranging from 0 to 1. Defaults to 0.3.
• color/c/face_color/fc str: Color of the polygon’s face. Can be hex (“#00FFFF”), named (“cyan”), or matplotlib-like (“c”). Defaults to black.
• precision int: Number of digits after the decimal to round to for lat/lng values. Defaults to 6.

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
finxter_in_Budapest = zip(*[ (47.53012124664374, 18.985823949220986), (47.53012124664374, 19.0247910821219), (47.50860174660818, 19.0247910821219), (47.50860174660818, 18.985823949220986), (47.53012124664374, 18.985823949220986)]) gmap.polygon(*finxter_in_Budapest, face_color='grey', face_alpha = 0.15, edge_color='cornflowerblue', edge_width=3)
gmap.draw('poligon.html')

Result:

Defines a set of coordinates for a polygon named finxter_in_Budapest.

Calls the gmap.polygon() method with the *finxter_in_Budapest argument to draw the polygon on the map. The face_color, face_alpha, edge_color, and edge_width parameters define the appearance of the polygon.

Saves the map as an HTML file named 'poligon.html' using the gmap.draw() method.

Display grid (grid)

The parameters are used to specify the grid start and end points and the width and length of the grid.

(lat_start, lat_end, lat_increment, lng_start, lng_end, lng_increment)

import gmplot
apikey = ' ' # (your API key here)
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 14, apikey=apikey)
gmap.grid(47.50, 47.53, 0.0025, 19.0, 19.05, 0.0025)
gmap.draw('grid.html')

Result:

This code generates a Google Map centered at latitude 47.519350864380385 and longitude 19.010462124312387, with a zoom level of 14. It then adds a grid to the map with vertical lines spaced 0.0025 degrees apart between longitude 19.0 and 19.05, and horizontal lines spaced 0.0025 degrees apart between latitude 47.50 and 47.53. Finally, it saves the resulting map with the grid to an HTML file named "grid.html".

Let’s put it together and see where I found “finxter”!

Import module, create gmap instance

import gmplot
#apikey = # (your API key here)
bounds = {'north': 47.623, 'south': 47.323, 'east': 19.208, 'west': 18.808}
gmap = gmplot.GoogleMapPlotter(47.519350864380385, 19.010462124312387, zoom = 13, fit_bounds=bounds, apikey=apikey)

Define letter direction routes

gmap.directions( (47.525977181062025, 19.02052238472371), (47.524798091352515, 19.021546988570606))
gmap.directions( (47.5194613766804, 19.000656008676216), (47.520243896650946, 19.00204002854648), waypoints = [(47.520888742275, 18.99871408933636)]) gmap.directions( (47.52226897515179, 19.00018393988221), (47.520243896650946, 19.00204002854648), waypoints = [(47.52088149688948, 19.002871513347902)])

Define letters routes

r = zip(*[ (47.52356554300279, 19.02012541778466), (47.5259726531124, 19.020546524602626), (47.52484065497139, 19.020254163818944), (47.52481167549813, 19.021541624161788), (47.52479718575549, 19.021541624161788), (47.52398575378016, 19.021906404592265)]) e = zip(*[(47.52529997270366, 19.014375612740643), (47.52403211687861, 19.013828442094937), (47.52369884683277, 19.01479403735207), (47.52514058679826, 19.01530902148921), (47.52369884683277, 19.01479403735207), (47.52335832959914, 19.015786454699683), (47.524981200408554, 19.016301438836823)]) t = zip(*[(47.52326414357012, 19.01047031640738), (47.52316633484429, 19.012573168300694), (47.52319169267961, 19.011403725155944), (47.521873068989784, 19.01161830187975)]) x = zip(*[(47.52149735340168, 19.006154537181025), (47.52312028178215, 19.002850055634386), (47.522424747195465, 19.004411101300082), (47.52301522767022, 19.00516211983341), (47.522424747195465, 19.004411101300082), (47.52161690117839, 19.003729820201993)]) i = zip(*[(47.51762820549394, 18.996203541721567), (47.51873681072719, 18.994594216293006)]) f = zip(*[(47.513285942712805, 18.994089961008104), (47.51453956566773, 18.991150259891935), (47.51573518971617, 18.992276787691928), (47.51453956566773, 18.991150259891935)])

Plot the letters

gmap.plot(*r, edge_width = 7, color = 'orange')
​
gmap.plot(*e, edge_width = 7, color = 'c')
​
gmap.plot(*t, edge_width = 7, color = 'limegreen')
​
gmap.plot(*x, edge_width = 7, color = 'gold')
​
gmap.plot(*i, edge_width = 7, color = 'cyan')
​
gmap.plot(*f, edge_width = 7, color = 'limegreen')
gmap.circle(47.51894874729591, 18.99426698678921, 10, face_alpha = 1, ec = 'cyan', fc='cyan')

Create text on map:

finxter_text = ['f', 'i', 'n', 'x', 't', 'e', 'r']
colors = ['limegreen', 'cyan', 'gold','orange', 'limegreen', 'cyan', 'orange']
j = 0
lat = 47.529266431577625
lng = 19.00200303401821
for i in finxter_text: gmap.text(lat, lng, i, color = colors[j], size = 10) j += 1 lng += 0.001

Drop a marker with finxter link, enable marker dropping:

gmap.marker(47.515703432784726, 19.005350430919034, label = 'finxter', info_window = "<a href='https://finxter.com/'>The finxter academy</a>")
​
gmap.enable_marker_dropping(color = 'black')

Define and plot scatter points for letters:

letters = zip(*[ (47.51471253011692, 18.990678878050492), (47.51941514547201, 18.993554206158933), (47.52134244386804, 18.998060317311538), (47.52337110249922, 19.002008528961046), (47.52344355313603, 19.009969325319076), (47.52466070898612, 19.013488383565445), (47.526645771633746, 19.02031192332838)])
​
gmap.scatter(*letters, color=['limegreen', 'cyan','gold','orange', 'limegreen', 'cyan', 'orange'], s=60, ew=1, title=['f', 'i', 'n', 'x', 't', 'e', 'r'], label=['f', 'i', 'n', 'x', 't', 'e', 'r']
)

Create heatmap:

letters = zip(*[ (47.51471253011692, 18.990678878050492), (47.51941514547201, 18.993554206158933), (47.52134244386804, 18.998060317311538), (47.52337110249922, 19.002008528961046), (47.52344355313603, 19.009969325319076), (47.52466070898612, 19.013488383565445), (47.526645771633746, 19.02031192332838)])
​
gmap.heatmap( *letters, radius=55, weights=[0.1, 0.2, 0.5, 0.6, 1.8, 2.10, 1.12], gradient=[(89, 185, 90, 0), (54, 154, 211, 0.5), (254, 179, 19, 0.79), (227, 212, 45, 1)], opacity = 0.7
)

Overlay image from URL:

url = 'https://finxter.com/wp-content/uploads/2022/01/image-2.png'
bounds = {'north': 47.53012124664374, 'south': 47.50860174660818, 'east': 19.0247910821219, 'west': 18.985823949220986}
gmap.ground_overlay(url, bounds, opacity=0.3)

Draw polygon:

finxter_in_Budapest = zip(*[ (47.53012124664374, 18.985823949220986), (47.53012124664374, 19.0247910821219), (47.50860174660818, 19.0247910821219), (47.50860174660818, 18.985823949220986), (47.53012124664374, 18.985823949220986)])
​
gmap.polygon(*finxter_in_Budapest, face_color='grey', face_alpha = 0.15, edge_color='cornflowerblue', edge_width=3)

Draw map to file:

gmap.draw('finxter_in_budapest.html')

Output:

Conclusion

Congratulations, now you’ve learned how to draw almost anything on Google Maps with a few lines of code. While gmplot is a powerful library, it has some limitations (e.g., I can’t figure out how to change the color of the path), so maybe other modules like geopandas are a good place to learn more.

A Few Final Words on gmplot

gmplot is a Python library that allows the user to plot data on Google Maps. It provides a simple interface to create various types of maps, including scatterplots, heatmaps, ground overlays, and polygons.

With gmplot, the user can add markers, lines, and shapes to the map, customize colors, labels, and other properties, and export the map to a static HTML file.

The library uses the Google Maps API and requires an API key to be able to use it. gmplot is a useful tool for visualizing geospatial data and creating interactive maps for data exploration and analysis.

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ChatGPT technology has seen use in various industries, such as customer support, content creation, virtual assistance, and many others. For those looking to stay abreast of cutting-edge AI technology or seeking to implement ChatGPT into their products or services, learning from the best books is paramount to their success.

In our search for the best ChatGPT books, we analyzed numerous texts, focusing on their coverage of concepts, functionality, and real-world applications. We identified top contenders that help you master this technology quickly and thoroughly, ensuring your success in leveraging ChatGPT for your personal endeavors or professional projects.

Best ChatGPT Books

Discover our top picks for the best ChatGPT books available on Amazon.

The ChatGPT Revolution

If you’re aiming to stay ahead in the AI-driven digital media landscape, “The ChatGPT Revolution” is a must-read.

Pros

• Insightful AI industry knowledge
• Practical tips for career transition
• Well-organized information

Cons

• Relatively short in length
• Only in English
• Independently published

As someone who’s always on the lookout for the next big thing in AI, I couldn’t wait to get my hands on “The ChatGPT Revolution.” I must say, this book didn’t disappoint. The detailed information about how AI is transforming the digital media landscape makes it engaging and easy to follow, even for a non-expert like me.

One of the critical aspects that stood out to me is the book’s focus on career transition for digital media professionals. It not only highlights the in-demand AI skills but also provides a list of learning resources to guide you in the right direction. This book is a treasure trove for anyone looking to break into the AI job market!

Although the book’s length seems a bit short, the content is undoubtedly rich in valuable insights and practical tips. My only regret is not having access to a translated version for my non-English-speaking friends. But overall, “The ChatGPT Revolution” is an indispensable resource for anyone interested in AI or digital media. Trust me, you won’t want to miss this one.

ChatGPT & Social Media Marketing: The Ultimate Guide

If you’re eager to conquer the world of social media marketing, this book will equip you with the power of ChatGPT and practical strategies.

Pros

• Demystifies the use of AI in social media marketing
• Offers valuable insights for beginners and experts alike
• Provides real-world examples and case studies

Cons

• Not suitable for those uninterested in AI integration
• Requires time investment to fully grasp concepts
• Not a shortcut to instant success

As someone who has read “ChatGPT & Social Media Marketing: The Ultimate Guide,” I can confidently say that it provides a deep understanding of how artificial intelligence, particularly ChatGPT, can revolutionize one’s social media marketing approach. The book is packed with helpful tips and tools to create engaging and effective campaigns, regardless of the platform you’re using.

Going through this guide felt like a journey of discovering new ways to enhance my social media presence. It highlights the importance of crafting captivating content, and how ChatGPT can significantly aid in that process. The book also addresses common challenges faced by marketers and offers practical solutions to overcome them.

In conclusion, if you’re looking to up your social media marketing game using AI, this book is a must-read. It may not be a magical key to instant success, but it will certainly provide you with the knowledge and tools needed to stay ahead of the competition. Give it a try, and let ChatGPT help you become the world’s best social media manager!

The ChatGPT Millionaire: Making Money Online has never been this EASY

The ChatGPT Millionaire is a must-read for those who want to learn how to utilize ChatGPT to create financial success online.

Pros

• Helpful for beginners and professionals alike
• Practical and applicable tips
• Engaging and easy-to-understand writing

Cons

• A bit short and compact
• Some filler ChatGPT responses
• May not suit everyone’s needs

The ChatGPT Millionaire is a fantastic guide that introduces readers to the world of ChatGPT, making it accessible for both newbies and seasoned professionals. With its real-life examples and applicable tips, this book brings value to anyone eager to leverage the power of ChatGPT for financial gains.

Although the book is concise with its 114 pages, it manages to offer valuable information in an easy-to-digest manner. However, some readers might find it too short to cover every aspect of ChatGPT. A few filler ChatGPT responses in the book might also come across as unnecessary, though they do provide context for the technology.

Overall, The ChatGPT Millionaire is a valuable resource on its subject matter, and despite some minor shortcomings, it’s worth adding to your reading list if you are keen to explore the potential of ChatGPT in creating wealth and success online.

The AI Question and Answer Book

An enlightening read for those eager to dive deep into the ChatGPT world, uncovering the artificial intelligence behind it and how it works.

Pros

• Insightful explanations
• Easy-to-understand language
• Practical examples

Cons

• Limited audience appeal
• May need prior AI knowledge
• Few visual aids

Having just finished reading “The AI Question and Answer Book,” I am amazed by how this book unravels the complexity of ChatGPT in a digestible manner. The author breaks down the intricate workings of artificial intelligence and how it pertains to ChatGPT throughout the book’s 205 pages.

What I truly appreciate in this volume is the clear language used to explain technical concepts, making it an excellent resource for both beginners and advanced AI enthusiasts alike. It covers various aspects of ChatGPT, offering a comprehensive understanding of the technology that powers it.

However, the book does have a few drawbacks. It caters mostly to individuals with an interest in AI, making its appeal somewhat limited. Additionally, readers may need to have a basic understanding of AI to fully appreciate the content. The lack of visual aids may also hinder comprehension for some readers. Nonetheless, this fascinating read will undoubtedly offer valuable insights into the world of ChatGPT and artificial intelligence.

A Teacher’s Prompt Guide to ChatGPT

If you’re an educator looking to effectively utilize ChatGPT, this guide is a fantastic resource to help you achieve maximum results.

Pros

• Comprehensive coverage of ChatGPT applications
• Aligns well with ‘What Works Best’ framework
• Accessible language for educators

Cons

• No text-to-speech option
• Lacks enhanced typesetting
• Page Flip feature not available

Just finished going through “A Teacher’s Prompt Guide to ChatGPT” and found it to be an excellent resource for educators who are eager to incorporate ChatGPT into their teaching repertoire. The author masterfully covers the core applications of ChatGPT and aligns them with the ‘What Works Best’ framework, ensuring that the content is relevant and beneficial for educators.

In my experience with the book, the language used is easily digestible for teachers from various backgrounds. The guide maintains a practical tone throughout, avoiding complicated jargon that might hinder a seamless learning experience. However, there’s room for improvement in terms of accessibility features.

Unfortunately, the guide doesn’t include text-to-speech, enhanced typesetting, or Page Flip features, which could be a deal-breaker for some users who rely on these options for a more accessible reading experience. Despite these drawbacks, I still believe that “A Teacher’s Prompt Guide to ChatGPT” is a valuable tool for educators to better understand and apply ChatGPT in their classrooms.

ChatGPT: Best Uses According to ChatGPT

If you’re looking to explore the fascinating world of advanced AI language generation, this book serves as a fantastic guide.

Pros

• In-depth user perspective
• Up-to-date AI language model
• Easy to follow & informative

Cons

• Only 70 pages
• Independently published
• Limited physical dimensions

ChatGPT: Best Uses According to ChatGPT dives deep into the potential applications of AI-powered language models. The author has done a phenomenal job explaining the different capabilities of this advanced language model, discussing its uses, and shedding light on how it can transform industries.

While the book is fairly short, it packs a punch in terms of content. Readers get a unique perspective on what makes ChatGPT such a game-changer, thanks to the author’s engaging writing. The book offers a truly eye-opening look at how ChatGPT’s language generation capabilities can be utilized effectively for various purposes.

Overall, ChatGPT: Best Uses According to ChatGPT is a compelling read for anyone looking to understand and enhance their AI language model experience. It provides practical and enlightening tips on how to maximize the benefits of using ChatGPT, without resorting to exaggerated claims or false statements.

ChatGPT and Artificial Intelligence: The Complete Guide

A comprehensive must-read for those interested in leveraging ChatGPT and AI technologies to enhance their skills and career prospects.

Pros

• Informative and beginner-friendly
• Up-to-date and relevant content
• Text-to-speech and screen reader support

Cons

• No X-Ray feature
• Some reviews report formatting issues
• Can be difficult for some readers

As someone who recently dived into the world of ChatGPT and AI, I found this book to be an invaluable resource. It does an exceptional job of breaking down complex concepts into digestible information for readers with little to no experience.

One aspect of the book that stood out to me was the depth of content and its relevance to today’s ever-evolving AI landscape. The author goes above and beyond to ensure that the reader is not only able to understand the basics of ChatGPT but also appreciates the greater role of AI technologies in our lives and professions.

While the book’s formatting could use some improvement, I found the support for text-to-speech and screen readers to be a huge plus! It made the learning experience much more accessible, especially for readers with visual impairments or learning differences. However, the lack of an X-Ray feature is a minor drawback for those who depend on it for quick reference.

ChatGPT The Recruiter’s New Best Friend

A must-read for recruiters looking to enhance their process with AI, Curtis Germany’s e-book delivers practical insights and tips.

Pros

• Brief, informative read
• Relevant examples of AI in recruitment
• Accessible for non-IT professionals

Cons

• Short length may leave readers wanting more
• Narrow focus on recruitment industry
• Lacks exploration of broader AI applications

As someone who has just read “ChatGPT The Recruiter’s New Best Friend,” I can confidently say this e-book is a great resource for recruiters looking to integrate AI into their workflow. The author explains in layman’s terms how ChatGPT can streamline various recruitment tasks, from sourcing to job offers.

Curtis Germany’s engaging writing style makes understanding the concepts a breeze, even for those unfamiliar with AI. He shares valuable insights that can be immediately applied to enhance daily tasks, boosting productivity in recruitment efforts. Moreover, the book’s short length makes it easy to digest, maximizing the time spent reading and applying its principles.

Despite its practical focus, it has a few drawbacks. The book’s brevity may leave some readers wanting more detailed and expansive information on broader AI applications. However, if you are a recruiter or an HR professional eager to dip your toes into the AI pool, this e-book offers the perfect starting point!

OpenAI’s ChatGPT Prompts Book to Increase Productivity

This book is a must-have for anyone looking to supercharge their AI communication skills and improve productivity with OpenAI’s ChatGPT platform.

Pros

• Offers over 200 useful prompts
• Elevates productivity by 10x
• Applicable for ChatGPT and GPT-3

Cons

• Only 62 pages of content
• No X-Ray support
• English language only

As someone who recently delved into “OpenAI’s ChatGPT prompts book to increase productivity by 10x,” I found it to be an invaluable resource for mastering the art of AI communication. The book features an extensive collection of prompts, specifically designed to work seamlessly with ChatGPT and GPT-3 platforms.

With over 200 stimulating prompts, you’re bound to find plenty of inspiration for crafting engaging conversations with AI. The most significant selling point of the book is its potential to increase your productivity by ten-fold. While reading, I noticed a marked improvement in my ability to generate compelling responses from my AI conversation partners, saving time and effort on tasks that once felt tedious.

However, the 62-page length of the book might leave some readers wanting more. Additionally, it would have been nice to see support for Amazon’s X-Ray feature, which enhances the reading experience through helpful information and insights. Overall, this book is an excellent investment for those seeking to up their AI communication game and unlock the true potential of ChatGPT and GPT-3.

ChatGPT-4: Transforming the Future

If you’re looking to delve into the world of ChatGPT technology, this comprehensive guide should be your go-to resource.

Pros

• Expertly written and easy to understand
• Highly relevant in today’s AI-driven world
• Adopts a hands-on approach with practical examples

Cons

• Only available in paperback format
• 112 pages may feel a bit condensed for the topic
• Independently published, which may affect credibility for some

The book “ChatGPT-4: Transforming the Future: A Comprehensive How-to Guide on Harnessing the Power and Potential of AI” is a must-read for anyone curious about this fascinating AI technology. After going through its 112 pages, I found the content to be insightful, well-organized, and highly informative.

One of the things that stood out to me was the book’s clarity in explaining complex concepts. The author has done a commendable job at breaking down ChatGPT-4 technology and its potential applications in a way that even a beginner can grasp with ease. Additionally, this guide is packed with real-world examples and hands-on techniques that will surely improve your understanding of AI capabilities in practical scenarios.

The only downside is that the book is currently available only in paperback format, which might not be ideal for those who prefer digital reading material. Additionally, given its independent publication, skeptics may question its credibility compared to a book released by a more established publisher. Regardless, “ChatGPT-4: Transforming the Future” proves to be a valuable resource, and I highly recommend it to anyone looking to explore AI’s potential through the lens of ChatGPT technology.

Recommended: Free ChatGPT Prompting Cheat Sheet (PDF)

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Working with dates is a common task in programming, and Python provides robust tools for handling them. One such tool is regular expressions, which can be used to match and validate date strings in a specific format. In this article, we will explore how to use Python regex patterns to match dates in the "YYYY-MM-DD" format.

Matching a date string in the "YYYY-MM-DD" format requires a regex pattern that accounts for four-digit years, two-digit months between 01 and 12, and two-digit days between 01 and 31. This can be achieved by creating an expression that considers the range of valid values for each date component and ensures the proper placement of hyphens.

The use of regular expressions in Python helps validate date strings and offers flexibility in extracting or manipulating date components. Understanding the basics of regex patterns and applying them to date matching can enhance your ability to work effectively with date-related data in your Python projects.

Matching YYYY-MM-DD Format

In this section, we will learn how to match dates in the 'YYYY-MM-DD' format using Python regular expressions. We will explore building the regex pattern and validating dates using the re module in Python. Let’s get started!

Building the Regex Pattern

The first step is to build a regex pattern that matches the desired date format. For the 'YYYY-MM-DD' format, a pattern may look like this:

^\d{4}-\d{2}-\d{2}$

The pattern breakdown:

• ^ – Denotes the start of the string.
• \d{4} – Matches 4 digits for the year.
• - – Matches the '-' separator.
• \d{2} – Matches 2 digits for the month.
• - – Matches the '-' separator again.
• \d{2} – Matches 2 digits for the day.
• $ – Denotes the end of the string.

Recommended: Python Regex – How to Match the Start of Line (^) and End of Line ($)

Validating Dates with Python

To validate a date string and check if it matches the 'YYYY-MM-DD' format, we will use Python’s re module. Let’s import the module and create a function to validate dates:

import re def validate_date(date_string): pattern = "^\d{4}-\d{2}-\d{2}$" if re.match(pattern, date_string): return True else: return False

Now we can use the validate_date() function to check if a date string is valid:

date = "2023-03-30"
is_valid = validate_date(date)
print(is_valid) # Returns True if the date is valid; otherwise, False.

That’s it! Now you know how to build a regex pattern for the ‘YYYY-MM-DD’ date format and validate dates using Python’s re module. Happy coding!

Alternative Approach to Match a Date ‘YYYY-MM-DD’

In this section, we will explore an alternative approach to matching dates in the ‘YYYY-MM-DD’ format using Python regular expressions (regex). This method provides a more concise and efficient way to match dates.

Instead of matching each component of the date separately, you can use a single regex pattern that takes care of all the components at once. Using this approach, we would match the year, month, and day components, along with the separators, in a single pattern. Here’s an example:

date_pattern = r'(\d{4})-(\d{2})-(\d{2})'

This pattern uses the following elements:

• \d{4} to match exactly four digits for the year
• - as a separator between the year, month, and day components
• \d{2} to match exactly two digits for the month and day components

Once you have defined the regex pattern, you can use the re module to search for and extract dates in the specified format. For example:

import re text = "The meeting is scheduled for 2023-04-15 and the deadline is 2023-03-30."
matched_dates = re.findall(date_pattern, text)
print(matched_dates) # [('2023', '04', '15'), ('2023', '03', '30')]

As you can see, this alternative approach makes it easier to spot and extract dates in the ‘YYYY-MM-DD’ format using Python regex.

Real-World Applications

Python Regex is a powerful tool for working with text, and one of its many applications is matching dates in the 'YYYY-MM-DD' format. In this section, we will explore two real-world use cases for this pattern: date validation in user input, and extracting dates from text.

Date Validation in User Input

When developing a software application, it’s common to require users to input dates in a specific format, such as 'YYYY-MM-DD'. Python Regex can be used to ensure that the entered date matches the desired format before it’s saved or processed. This helps maintain data consistency and prevents potential issues related to invalid date inputs.

For example, you can use the following regex pattern to match a date in the 'YYYY-MM-DD' format:

date_regex = re.compile(r'(\d{4}-\d{2}-\d{2})')

Extracting Dates from Text

Another common application for Python Regex is extracting dates from large blocks of text, such as documents or web pages. This can be particularly useful when working with historical records, reports, or any text where dates are mentioned in the 'YYYY-MM-DD' format.

Using the regex pattern mentioned earlier, you can search through a given text and extract all instances of dates in the desired format:

date_matches = re.findall(date_regex, text)

The results can then be further processed, analyzed, or visualized depending on what you want to achieve with the extracted data.

Python Regex Library

Regex Basics

Regular expressions (regex) are powerful tools used in programming to find and manipulate text based on specific patterns. In Python, the re module provides all the necessary functions to work with regular expressions. Let’s explore some essential regex concepts and how they can help us match dates in ‘YYYY-MM-DD’ format.

Creating Regex Patterns

Regex patterns consist of special characters and literals that define a specific search pattern. For instance, to match the ‘YYYY-MM-DD’ date format, we can use the following pattern:

r'\d{4}-\d{2}-\d{2}'

This pattern includes:

• \d – a digit character
• {4} – exactly 4 repetitions of the previous element
• - – a literal hyphen character

Thus, the pattern looks for 4 digits followed by a hyphen, 2 digits, another hyphen, and finally 2 more digits.

re Module Functions

The re module in Python provides various functions to work with regex patterns, such as:

• search() – searches for the first occurrence of a pattern in a string
• findall() – returns all non-overlapping matches in a string as a list
• sub() – replaces all occurrences of a pattern with a specified replacement
• compile() – compiles a pattern for repeated use

re Module Usage

To match a date in the 'YYYY-MM-DD' format, you can use the re module, as shown in this example:

import re pattern = r'\d{4}-\d{2}-\d{2}'
date_string = 'The event takes place on 2023-04-15.' match = re.search(pattern, date_string)
if match: print('Date found:', match.group(0))

This code snippet defines a regex pattern, searches for it in the provided string, and prints the matched date if found. Remember, regex patterns are incredibly versatile, so you can customize them to fit your specific needs!

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