Numerical data is common in data analysis. Often you have numerical data that is continuous, or very large scales, or is highly skewed. Sometimes, it can be easier to bin values into discrete intervals. This is helpful to perform descriptive statistics when values are divided into meaningful categories. For example, we can divide the age into Toddler, Child, Adult, and Elder.

Pandas’ built-in cut() function is a great way to transform numerical data into categorical data. In this article, you’ll learn how to use it to deal with the following common tasks.

1. Discretizing into equal-sized bins
2. Adding custom bins
3. Adding…

DataFrame and Series are two core data structures in Pandas. DataFrame is a 2-dimensional labeled data with rows and columns. It is like a spreadsheet or SQL table. Series is a 1-dimensional labeled array. It is sort of like a more powerful version of the Python list. Understanding Series is very important, not only because it is one of the core data structures, but also because it is the building blocks of a DataFrame.

In this article, you’ll learn the most commonly used data operations with Pandas Series and should help you get started with Pandas. …

Getting Started

The activation functions are at the very core of Deep Learning. They determine the output of a model, its accuracy, and computational efficiency. In some cases, activation functions have a major effect on the model’s ability to converge and the convergence speed.

In this article, you’ll learn why ReLU is used in Deep Learning and the best practice to use it with Keras and TensorFlow 2.

1. Problems with Sigmoid and Tanh activation functions
2. What is Rectified Linear Unit (ReLU)
3. Training a deep neural network using ReLU
4. Best practice to use ReLU with He initialization
5. Comparing to models with Sigmoid and…

TensorFlow 2 tutorials

In artificial neural networks (ANNs), the activation function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer [1].

The activation functions are at the very core of Deep Learning. They determine the output of a model, its accuracy, and computational efficiency. In some cases, activation functions have a major effect on the model’s ability to converge and the convergence speed.

In this article, you’ll learn the following most popular activation functions in Deep Learning and how to use them with Keras and TensorFlow 2.

1. Sigmoid (Logistic)
2. Hyperbolic Tangent (Tanh)
3. …

Callbacks are an important type of object in Keras and TensorFlow. They are designed to be able to monitor the model performance in metrics at certain points in the training run and perform some actions that might depend on those performances in metric values.

Keras has provided a number of built-in callbacks, for example, EarlyStopping, CSVLogger, ModelCheckpoint, LearningRateScheduler etc. Apart from these popular built-in callbacks, there is a base class called Callback which allows us to create our own callbacks and perform some custom actions. …

Reading data is the first step in any data science project. Often, you’ll work with data in JSON format and run into problems at the very beginning. In this article, you’ll learn how to use the Pandas built-in functions read_json() and json_normalize() to deal with the following common problems:

1. Reading simple JSON from a local file
2. Reading simple JSON from a URL
3. Flattening nested list from JSON object
4. Flattening nested list and dict from JSON object
5. Extracting a value from deeply nested JSON

Please check out Notebook for the source code.

1. Reading simple JSON from a local file

Let’s begin with a simple example.

[ { "id"…

Web scraping is the process of collecting and parsing data from the web. The Python community has come up with some pretty powerful web scrapping tools. However, many modern websites are dynamic, in which the content is loaded and populated using client JavaScript. Therefore, some extra setups are required in order to scrape data from JavaScript webpages.

In this article, you’ll learn how to scrape tables from a JavaScript webpage using Selenium, BeautifulSoup, and Pandas.

1. Challenges of a JavaScript webpage
2. Install libraries and Selenium web driver
3. Scrap tables using Selenium, BeautifulSoup, and Pandas

Please check out the source code from…

Web scraping is the process of collecting and parsing data from the web. The Python community has come up with some pretty powerful web scrapping tools. Among them, Pandas read_html() is a quick and convenient way for scraping data from HTML tables.

In this article, you’ll learn Pandas read_html() to deal with the following common problems and should help you get started with web scraping.

1. Reading tables from a string
2. Reading tables from a URL
3. Reading tables from a file
4. Parsing date columns with parse_dates
5. Explicitly typecast with converters
6. MultiIndex, header, and index column
7. Matching a table with match
8. Filtering…