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 bring a surprising efficiency level to your work process.

Templatespython

Indexing in Pandas Python

This template demonstrated the concepts behind indexing in Python by giving pandas objects as an example. Some other related topics you might be interested in are Position-Based and Label-Based Indexing in Python, Dealing with Indexing in Python, Using Pandas Methods for Working with Series Objects in Python, Ordering the Rows from a Data Table According to the Values in a Column in Python, and Attribute Chaining in Python. The Indexing in Pandas Python template is among the topics covered in detail in the 365 Program.

This template shows how to use attributes for gathering information about different Objects - in particular - pandas Series. Some other related topics you might be interested in are Using Pandas Methods for Working with Series Objects in Python, Creating DataFrames in Python, Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, and Ordering the Rows from a Data Table According to the Values in a Column in Python. The Using Attributes in Python template is among the topics covered in detail in the 365 Program.

Creating a Series Object by Using a NumPy Array in Python

This template shows how to convert a NumPy array into a Series. First, you import the Pandas and NumPy libraries, after which you create an array containing four integer values. Then you turn the array into a series and finally you check the type of the object. Some other related topics you might be interested in are Using Attributes in Python, Indexing in Pandas Python, Position-Based and Label-Based Indexing in Python, and Dealing with Indexing in Python. The creating a Series Object by Using a NumPy Array in Python template is among the topics covered in detail in the 365 Program.

This template shows how to convert a list object into a Series in the popular Pandas library. Some other related topics you might be interested in are Creating a Series Object by Using a NumPy Array in Python, Using Pandas Methods for Working with Series Objects in Python, Obtaining Descriptive Statistics about the Data in Python, Delivering an Array with the Unique Values from a Dataset in Python, and Ordering the Rows from a Data Table According to the Values in a Column in Python. The Creating a Series Object from a list in Python template is among the topics covered in detail in the 365 Program.

This template demonstrates how to import the pandas library in Python- a popular open-source library that provides high-performance structures and data analysis tools. Some other related topics you might be interested in are Indexing in Pandas Python, Using Attributes in Python, Using Pandas Methods for Working with Series Objects in Python, and Creating DataFrames in Python. The Importing the Pandas Library in Python is among the topics covered in detail in the 365 Program.

This free open-access template shows how to convert date and time values between the different time zones in Python. Some other related topics you might be interested in are Local Time and Universal Time in Python, Importing the Pandas Library in Python, Creating a Series Object by Using a NumPy Array in Python, and Using Attributes in Python. The Converting Timezones in Python template is among the topics covered in detail in the 365 Program.

This is an open-access template demonstrating how to estimate the local time and compare it to universal time in Python. Some other related topics you might be interested in are Converting Timezones in Python, Converting between Timezones in DataFrames, Importing the Pandas Library in Python, and Creating a Series Object from a List in Python. The Local Time and Universal Time in Python template is among the topics covered in detail in the 365 Program.

The following template teaches you one way to convert an image file to an array (tensor). This is extremely useful in ML and Computer Vision, as these fields require images as data, however, the algorithms can only work with numbers and arrays. Some other related topics you might be interested in are Tensorboard - Tracking Metrics in Python, Tensorboard - Confusion Matrix in Python, Tensorboard - Tuning Hyperparameters in Python, and A Simple CNN Network - Convolutional Layer in Python. The Converting Images into Arrays in Python is among the topics covered in great detail in the 365 Data Science Program.

This template demonstrates how one can tune the hyperparameters of their network model using TensorBoard. Hyperparameter tuning is important aspect of Machine Learning and being able to do it automatically can be a time saver. TensorBoard provides other visualization options, as well. Some other related topics you might be interested in are Dropout in Python, L2 Regularization and Weight Decay in Python, Converting Images into Arrays, and A Common CNN Architecture in Python. The TensorBoard – tuning Hyperparameters in Python.

The confusion matrix is an essential tool when trying to solve classification problems. There are many different ways to construct such a matrix. In the following template we show you how one can create and visualize the confusion matrix with the help of TensorBoard and sklearn. Some other related topics you might be interested in are TensorBoard - Tuning Hyperparameters in Python, Converting Images into Arrays, L2 Regularization and Weight Decay in Python, and Dropout in Python. The TensorBoard template is among the topics covered in detail in the 365 Program.

The following is a program used to demonstrate how to log different metrics in Tensorboard for visualization later. An example CNN network is used. The TensorBoard callback is defined to log the loss function and accuracy during training. Then, the extension is loaded in order to visualize these metrics. Some other related topics you might be interested in are TensorBoard - Confusion Metrics in Python, TensorBoard - Tuning Hyperparameters in Python, Converting Images into Arrays. The TensorBoard - Tracking Metrics in Python template is among the topics covered in the 365 Data Science Program.

Convolutional Neural Networks are a powerful choice for problems and datsets involving images. However, they can grow to become so big, that training it on a normal system takes too long. So, the following template shows a particular network architecture that can be very effective for most problems, but is also small enough to be trained quickly. Some other related topics you might be interested in are Pooling Layers in Python, Tensorboard - Tracking Metrics in Python, Tensorboard - Confusion Metrics in Python, and Tensorboard - Tuning Hyperparameters in Python. The Common CNN Architecture in Python template is among the topics covered in detail in the 365 Program.

Pooling Layers are an important part of a Convolutional Neural Network (CNN). That's why, the following template demonstrates how one can add a MaxPooling layer to the network architecture, as well as discuss the important parameters that need to be considered and included. Some other related topics you might be interested in are Simple CNN Network - Convolutional Layer in Python, A Common CNN Architecture in Python, TensorBoard - Tracking Metrics in Python, and Tensorboard - Tuning Hyperparameters in Python. The Pooling Layers in Python template is among the topics covered in detail in the 365 Program.

A Simple CNN Network - Convolutional Layer in Python

This template demonstrates how one can add convolutional layers to our network in order to create a Convolutional Neural Network (CNN).. Some other related topics you might be interested in are Pooling Layers in Python, A Common CNN Architecture in Python, Tensorboard - Tracking Metrics in Python, and Tensorboard - Tuning Hyperparameters in Python. The template is among the topics covered in detail in the 365 Program

In this template you will find a list of the most commonly used regular expressions as well as a link to the Python documentation website, where the full list is stored. We will start with a simple example demonstrating the function of the compile(), match(), and search() methods. After that, more complicated regular expressions are constructed. Some other related topics you might be interested in are String formatting in Python, Bubble Sort in Python, Linear search in Python, Binary Search in Python. The Regular Expressions in Python template is among the topics covered in detail in the 365 Data Science Program.

The following template demonstrates how to implement an insertion sort function in Python. Some other related topics you might be interested in are Bubble sort in Python, Linear search in Python, Binary search in Python, and Sets and operations with sets in Python. The Insertion Sort in Python is among the topics covered in detail in the 365 Data Science Program

The following notebook demonstrates how to implement a binary search function(also knows as half interval) in Python. Some other related topics you might be interested in are Bubble sort in Python, Linear search in Python, and Sets and operations with sets in Python. The Binary Search in Python is among the topics covered in detail in the 365 Data Science Program

The following Jtemplate demonstrates how to implement a linear search function in Python. Some other related topic you might be interested in Bubble sort in Python, Insertion sort in Python, Binary search in Python, Sets and operations with sets in Python are Bubble Sort in Python, Insertion Sort in Python, Binary Search in Python, Sets and Operations with Sets in Python. The Linear Search in Python template is among the topics covered in detail in the 365 Data Science Program.

The following template demonstrates the difference between lists and sets in Python, and includes examples. Some other related topics you might be interested in are Linear search in Python, Binary search in Python, Insertion sort in Python, Bubble sort in Python. The Set and Operations with Sets in Python Template is among the topics covered in detail in the 365 Data Science Program.

The following notebook demonstrates how to iterate through a list more effectively using the enumerate() built-in function in Python. Some other related topics you might be interested in are The zip function in Python, Defining functions in Python - the Fibonacci sequence, Recursion in Python - the Fibonacci sequence and Defining Classes in Python. The Enumerate Function 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 students and our expert team of instructors.

Templatespython

Obtaining Descriptive Statistics about the Data in Python

The following template demonstrates how to obtain an overview about the dataset. It shows the application of the .describe() method on a pandas Series object. Some other related topics you might be interested in are Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, Ordering the Rows from a Data Table According to the Values in a Column in Python, Data Selection in Python, and Common Attributes for Working with DataFrames in Python. The Obtaining Descriptive Statistics about the Data in Python template is among the topics covered in detail in the 365 Program.

Common Attributes for Working with DataFrames in Python

The following template demonstrates the application of important pandas attributes when cleaning, preprocessing, and analyzing a dataset. Some other related topics you might be interested in are Data Selection in Python, Indexing with.iloc[] and .loc[] in Python, Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, and Using Pandas Methods for Working with Series Objects in Python. The Common Attributes for Working with DataFrames in Python template is among the topics covered in detail in the 365 Program.

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.

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.

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

Templatespython

Obtaining Descriptive Statistics about the Data in Python

The following template demonstrates how to obtain an overview about the dataset. It shows the application of the .describe() method on a pandas Series object. Some other related topics you might be interested in are Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, Ordering the Rows from a Data Table According to the Values in a Column in Python, Data Selection in Python, and Common Attributes for Working with DataFrames in Python. The Obtaining Descriptive Statistics about the Data in Python template is among the topics covered in detail in the 365 Program.

Common Attributes for Working with DataFrames in Python

The following template demonstrates the application of important pandas attributes when cleaning, preprocessing, and analyzing a dataset. Some other related topics you might be interested in are Data Selection in Python, Indexing with.iloc[] and .loc[] in Python, Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, and Using Pandas Methods for Working with Series Objects in Python. The Common Attributes for Working with DataFrames in Python template is among the topics covered in detail in the 365 Program.

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.

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.