Save hours of browsing through the Internet and accelerate your workflow with code-ready templates for your programming projects. Personally crafted by 365 Data Science instructors, these templates will bring a surprising level of efficiency to your work process.

Templatespython

Extracting Tables from a Webpage with Pandas in Python

This is a template exploring how one can extract the contents of tables on websites straight to a Pandas data frame. This is done through Pandas itself, so there are no additional libraries required. Some other related topics you might be interested in are, Incorporating Commonly Used HTML Tags in Python, Searching by Attributes with Beautiful Soup in Python is, Extracting HTML attributes using BeautifulSoup in Python and Setting up Beautiful Soup and Choosing a Parser in Python.
You can now download the Python template for free.
The Extracting Tables from a Webpage with Pandas in Python template is among the topics covered in detail in the 365 Data Science program.

Extracting All Links from a Webpage Using Beautiful Soup in Python

This is a template demonstrating how one can extract the attributes from an HTML tag using BeautifulSoup. HTML attributes are a tool to specify additional information regarding the tag and/or change its behavior. Some other related topics you might be interested in are, Incorporating Commonly Used HTML Tags in Python, Searching by Attributes with Beautiful Soup in Python is and Setting up Beautiful Soup and Choosing a Parser in Python.
You can now download the Python template for free.
The Extracting All Links from a Webpage Using Beautiful Soup in Python template is among the topics covered in detail in the 365 Data Science program.

Navigating the HTML Using Beautiful Soup in Python Template

This is a template demonstrating different Beautiful Soup methods for navigating the HTML tree. There are multiple ways to achieve this - finding the contents of a tag, its children, or its parent. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python, Searching by Attributes with Beautiful Soup in Python is and Setting up Beautiful Soup and Choosing a Parser in Python.
You can now download the Python template for free.
The Navigating the HTML using Beautiful Soup in Python template is among the topics covered in detail in the 365 Data Science program.

Searching by Attributes with Beautiful Soup in Python Template

The Searching by Attributes with Beautiful Soup in Python is a template that shows how we can incorporate attributes in our search for tags using the 'find' and 'find_all' methods of Beautiful Soup. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python, and Setting up Beautiful Soup and Choosing a Parser in Python.
You can now download the Python template for free.
The Searching by Attributes with Beautiful Soup in Python template is among the topics covered in detail in the 365 Data Science program.

Searching for Тags with Beautiful Soup - find and find all in Python Template

This is a template that shows how we can use the 'find' and 'find_all' methods of Beautiful Soup to search for tags in the HTML document. It also demonstrates what happens if no tag is found. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python, and Setting up Beautiful Soup and Choosing a Parser in Python.
You can now download the Python template for free.
The Searching for Тags with Beautiful Soup - find and find all in Python template is among the topics covered in detail in the 365 Data Science program.

Setting up Beautiful Soup and Choosing a Parser in Python Template

The Setting up Beautiful Soup and Choosing a Parser in Python Template shows the first steps needed to be taken when starting to scrape with Beautiful Soup - connecting to the website, checking out the html, creating the soup and choosing a Parser, and finally, exporting the html to a file. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python.
You can now download the Python template for free.
The Setting up Beautiful Soup and Choosing a Parser in Python template is among the topics covered in detail in the 365 Data Science program.

Reading from and Writing to Files in Python Template

The Reading from and Writing to Files in Python Template demonstrates how one can read and write files in Python. It also introduces the 'with' statement through which we can automatically close the file after we finish working with it. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python.
You can now download the Python template for free.
The Reading from and Writing to Files in Python template is among the topics covered in detail in the 365 Data Science program.

Request Headers and Emulating a Browser in Python Template

In the Request Headers and Emulating a Browser in Python template we explore how to define different request headers and also manipulate the 'User-Agent' string in order to pretend that the request was sent through a browser. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Reading from and Writing to Files in Python.
You can now download the Python template for free.
The Request Headers and Emulating a Browser in Python template is among the topics covered in detail in the 365 Data Science program.

R-squared and Adjusted R-squared with sklearn in Python Template

The R-squared and Adjusted R-squared with sklearn in Python demonstrates how to return the R-squared and R-squared values of a model when performing linear regression. Some other related topics you might be interested in are Regression Summary Table with sklearn in Python, Feature Selection through p-values with sklearn in Python, Feature Selection through Standardization with sklearn in Python.
You can now download the Python template for free.
The R-squared and Adjusted R-squared with sklearn in Python template is among the topics covered in detail in the 365 Data Science program.

Visualizing Linear Regressions with Matplotlib in Python Template

The Visualizing Linear Regressions with Matplotlib in Python template demonstrates how to plot the regression line of a linear regression model onto the data. We go through the steps of loading the data from a .csv file, then mapping dummy variables onto numerical values, performing a linear regression using statsmodels and, finally, visualize what we have created. Some other related topics you might be interested in are Regression Summary Table with statsmodels in Python, Predictions with statsmodels in Python, Linear Regression Model in Python - predictions versus targets. eeee
You can now download the Python template for free.
The Visualizing Linear Regressions with Matplotlib in Python template is among the topics covered in detail in the 365 Data Science program.

Heatmaps and Dendrograms with seaborn in Python Template

The Heatmaps and Dendrograms with seaborn in Python template demonstrates how to create heatmaps and dendrograms using the seaborn package in Python.Some other related topics you might be interested in are K-Means Clustering of Numerical Data with sklearn in Python, The Elbow Method for K-Means Clustering in Python, K-Means Clustering of Categorical Data with sklearn in Python.
You can now download the Python template for free.
The Heatmaps and Dendrograms with seaborn in Python template is among the topics covered in detail in the 365 Data Science program.

The Elbow Method for K-Means Clustering in Python Template

The Elbow Method for K-Means Clustering in Python template demonstrates a way to determine the most optimal value of K in a K-Means clustering problem. Recall that K represents the numbers of clusters. The way this is done is through the so-called elbow method which requires calculating the within-cluster sum of squares for each number of clusters.. Some other related topics you might be interested in are K-Means Clustering of Numerical Data with sklearn in Python, Heatmaps and Dendrograms with seaborn in Python, K-Means Clustering of Categorical Data with sklearn in Python.
You can now download the Python template for free.
The Elbow Method for K-Means Clustering in Python template is among the topics covered in detail in the 365 Data Science program.

K-Means Clustering of Numerical Data with sklearn in Python Template

The K-Means Clustering of Numerical Data with sklearn in Python template shows how to solve a simple clustering problem using the K-Means algorithm provided by the sklearn machine learning package. After performing the clustering, we will visualize the results and identify the clusters. Some other related topics you might be interested in are The Elbow Method for K-Means Clustering in Python, Heatmaps and Dendrograms with seaborn in Python, K-Means Clustering of Categorical Data with sklearn in Python.
You can now download the Python template for free.
The K-Means Clustering of Numerical Data with sklearn in Python is among the topics covered in detail in the 365 Data Science program.

Logistic Regression with statsmodels in Python Template

The Logistic Regression with statsmodels in Python template shows how to solve a simple classification problem using the logistic regression model provided by the statsmodels library. The database used for the example is read using the pandas library.. Some other related topics you might be interested in are Confusion Matrix with statsmodels in Python, Logistic Regression Curve in Python, Model Accuracy in Python.
You can now download the Python template for free.
The Logistic Regression with statsmodels in Python template is among the topics covered in detail in the 365 Data Science program.

Confusion Matrix with statsmodels in Python Template

In this Confusion Matrix with statsmodels in Python template, we will show you how to solve a simple classification problem using the logistic regression algorithm. Then, we will create a python confusion matrix of the model using the statsmodels library and make the table more beautiful and readable with the help of the pandas library. Some other related topics you might be interested in are Logistic regression with statsmodels in Python, Logistic Regression Curve in Python, Model Accuracy in Python.
You can now download the Python template for free.
The Confusion Matrix with statsmodels in Python template is among the topics covered in detail in the 365 Data Science program.

Linear Regression Model in Python- Predictions versus Targets Template

In this Linear Regression Model in Python- predictions versus targets template, we will show you how to plot the predictions the model has made versus the true targets. Some other related topics you might be interested in are Predictions with statsmodels in Python, Feature Selection through Standardization with sklearn in Python, Predictions with standardized Coefficients with sklearn in Python, Visualizing Linear regressions with matplotlib in Python.
You can now download the Python template for free.
The Dummy Variables with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

When preparing data for a machine learning algorithm, very often we see variables that do not bear numerical values The Dummy Variables with pandas in Python template demonstrates how to map categorical data onto numerical values using the pandas library. Some other related topics you might be interested in are Mapping Categorical to Numerical Data with pandas in Python, Removing Missing Values with pandas in Python, Removing Outliers with pandas in Python.
You can now download the Python template for free.
The Dummy Variables with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

The OLS Assumptions in Python – No Multicollinearity

The OLS Assumptions in Python – No Multicollinearity shows how to detect possible collinearity between several data set features and deal with them. In this example, we investigate the possible collinearity between several car features and remove the unnecessary ones. Some other topics you might be interested in exploring are OLS Assumptions in Python - No Multicollinearity, Linear Regression Model in Python – Residuals.
You can now download the Python template for free.
The OLS Assumptions in Python - No Multicollinearity template is among the topics covered in detail in the 365 Data Science program.

The OLS Assumptions in Python - Linearity shows how to transform non-linear dependencies into linear. In this example, we check the dependencies between the price of a car with respect to the year of manufacturing, its price and its mileage. Some other related topics you might be interested are OLS assumptions in Python – Linearity and Linear regression model in Python - residuals.
You can now download the Python template for free.
The OLS Assumptions in Python - Linearity template is among the topics covered in detail in the 365 Data Science program.

The Removing Outliers with pandas in Python shows how to detect and remove samples that skew a dataset and might lead to building an inaccurate model. Some other related topics you might be interested are Removing Outliers with pandas in Python, Dummy Variables with pandas in Python, Feature Selection through p-values with sklearn in Python, Feature Selection through standardization with sklearn in Python, Linear Regression Model in Python – residuals.
You can now download the Python template for free.
The Removing Outliers with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.

Templatespython

Linear Regression with statsmodels in Python Template

The following Linear Regression with Statsmodels in Python free .ipynb template shows how to solve a simple linear regression problem using the Ordinary Least Squares statsmodels library. We are going to examine the causal relationship between the independent variable in the dataset - SAT score of a student, and the dependent variable -the GPA score. This database is read with the help of the pandas library. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook.

A line chart is often used when we want to chronologically track the changes in value of a variable over a period of time and identify existing patterns and trends. Therefore, the line chart is often applied in financial statements, weather forecasts, stock market analysis and experiment statistics reports. This free .xlsx template displays the S&P 500 and Footsie indices for the second half of the economically devastating 2008 on a line chart

Feature Selection Through Standardization with sklearn in Python Template

The following Feature Selection Through Standardization with sklearn in Python template shows how to solve a multiple linear regression problem with two continuous features. These features are standardized using a StandardScaler() object. After fitting the model to the scaled data, we construct a summary table in the form of a dataframe. It stores the features as well as their biases and weights (the machine learning jargon for intercepts and coefficients). The irrelevant features are automatically penalized by a small magnitude of the weight. Such a procedure is known as feature scaling through standardization. Open the .ipynb file using Jupyter notebook. Another related topics is Feature selection through p-values with sklearn in Python. You can now download the Python template for free.
Feature Selection Through Standardization with sklearn in Python is among the topics covered in detail in the 365 Data Science program.

Developed in the 1970s by a group of IBM researchers, SQL continues to be the most popular programming language for relational database management and is used by companies like Facebook to store mounts of user data. None of this would be possible without the foundation of the SQL language- the database. That is the place where information is organized into tables and can be accessed, manipulated, and retrieved in any desired way. Consider this as the very first step in your SQL journey as this free sql template will show you how to make a SQL database in MYSQL.

Joins are the SQL tools that allow us to work with data from multiple tables simultaneously relying on the logical relationship between their objects. The INNER JOIN clause, in particular, creates a new instance of a table that combines rows with matching values from two tables. Thus, null values, or values appearing only in one of the tables, will not be extracted. In this free sql template you will be applying the SQL Inner Join clause on a set of business department tables.

Incorporating URL parameters into a GET request in Python Template

A GET request is used when we want to obtain a certain document from a server- like a web page or API output. However, we can also add different parameters to the request to obtain a more specific result – either by modifying or adding additional information. In this free .ipynb template, we will show you how to incorporate such parameters into the URL by using the "requests" Python package.

Pie charts are one of the most popular data visualization tools since they express the part-to whole relationship of a dataset in a very intuitive manner. As such they are best used when we want to communicate for example the revenue of each product and its relationship to the whole. The following free. xlsx template shows an Excel pie chart, displaying the number of cars using a particular type of fuel.

The seaborn library has been one of the most popular Python libraires in recent years. Compared to matplotlib, seaborn has simpler and more intuitive syntax, and wider visual-enhancing features. This free .ipynb Scatter Plot with Seaborn in Python template shows the relationship between the price and area of houses, based on real estate data. It's easy and intuitive to build and customize a scatter plot with the help of seaborn.

Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.

Templatespython

Linear Regression with statsmodels in Python Template

The following Linear Regression with Statsmodels in Python free .ipynb template shows how to solve a simple linear regression problem using the Ordinary Least Squares statsmodels library. We are going to examine the causal relationship between the independent variable in the dataset - SAT score of a student, and the dependent variable -the GPA score. This database is read with the help of the pandas library. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook.

A line chart is often used when we want to chronologically track the changes in value of a variable over a period of time and identify existing patterns and trends. Therefore, the line chart is often applied in financial statements, weather forecasts, stock market analysis and experiment statistics reports. This free .xlsx template displays the S&P 500 and Footsie indices for the second half of the economically devastating 2008 on a line chart

Feature Selection Through Standardization with sklearn in Python Template

The following Feature Selection Through Standardization with sklearn in Python template shows how to solve a multiple linear regression problem with two continuous features. These features are standardized using a StandardScaler() object. After fitting the model to the scaled data, we construct a summary table in the form of a dataframe. It stores the features as well as their biases and weights (the machine learning jargon for intercepts and coefficients). The irrelevant features are automatically penalized by a small magnitude of the weight. Such a procedure is known as feature scaling through standardization. Open the .ipynb file using Jupyter notebook. Another related topics is Feature selection through p-values with sklearn in Python. You can now download the Python template for free.
Feature Selection Through Standardization with sklearn in Python is among the topics covered in detail in the 365 Data Science program.

Developed in the 1970s by a group of IBM researchers, SQL continues to be the most popular programming language for relational database management and is used by companies like Facebook to store mounts of user data. None of this would be possible without the foundation of the SQL language- the database. That is the place where information is organized into tables and can be accessed, manipulated, and retrieved in any desired way. Consider this as the very first step in your SQL journey as this free sql template will show you how to make a SQL database in MYSQL.

Joins are the SQL tools that allow us to work with data from multiple tables simultaneously relying on the logical relationship between their objects. The INNER JOIN clause, in particular, creates a new instance of a table that combines rows with matching values from two tables. Thus, null values, or values appearing only in one of the tables, will not be extracted. In this free sql template you will be applying the SQL Inner Join clause on a set of business department tables.

Incorporating URL parameters into a GET request in Python Template

A GET request is used when we want to obtain a certain document from a server- like a web page or API output. However, we can also add different parameters to the request to obtain a more specific result – either by modifying or adding additional information. In this free .ipynb template, we will show you how to incorporate such parameters into the URL by using the "requests" Python package.

Pie charts are one of the most popular data visualization tools since they express the part-to whole relationship of a dataset in a very intuitive manner. As such they are best used when we want to communicate for example the revenue of each product and its relationship to the whole. The following free. xlsx template shows an Excel pie chart, displaying the number of cars using a particular type of fuel.

The seaborn library has been one of the most popular Python libraires in recent years. Compared to matplotlib, seaborn has simpler and more intuitive syntax, and wider visual-enhancing features. This free .ipynb Scatter Plot with Seaborn in Python template shows the relationship between the price and area of houses, based on real estate data. It's easy and intuitive to build and customize a scatter plot with the help of seaborn.