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Do you ever use print() or log() to debug your code? Of course you do. IceCream, or ic for short, makes print debugging a little sweeter. ic() is like print(), but better. It prints both expressions/variable names and their values, it’s 40% faster to type. The data structures that it provides are printed in the prettiest way possible, the output is syntax highlighted and it optionally includes program context: filename, line number, and parent function. IceCream is well tested, permissively licensed, and supports Python 2, Python 3, PyPy2, and PyPy3. …


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Generating fake data for your tests has never been more fun or easy. If you are ever lacking data or just need some mock data to run a through a model or what have you, then look no further because drawdata does just that in a very easy to use way. This library is very simple yet can be useful in so many different cases, it is a small python app allows you to draw a dataset in any jupyter notebook. In this short blog post I will cover how to get started using drawdata as well as how to…


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Visualizations can be the most important part of a data science project. Matplotlib as well as Seaborn are both very common and most of the time are the go to libraries to use when you need to visualize something. Well it’s time to introduce Plotly Express, this extension is a built-in part of the Plotly library. Plotly Express provides more than 30 functions for creating different types of figures. In this post I will discuss how to use this library as well cover it’s creatively unique functionalities.

To get started we will need to import the package, we will also…


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Messy datasets? Missing values? MissingNo provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. In this short blog post I will discuss how to get started using this library as well as demonstrate some of it’s functionalities.

To get started you will need to install the library, this can be done using the code found in the cell below.

pip install missingno

Once you have installed MissingNo you will then need actual data to work with and visualize…


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When programming running into error after error can be the most frustrating and time consuming part of your process. If you struggle with the tedious task of sifting through lines of error messages just to get parts of your code running or even just want to make troubleshooting easier on yourself, look no further because PrettyErrors does an excellent job at assisting with any issue. In this blog post I will discuss how to use and cover the different function that this library includes. To get started, you will want to install using the code found in the cell below.


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Colorcet is a collection of perceptually acccurate 256-color colormaps for use with Python plotting programs like Bokeh, Matplotlib, HoloViews, and Datashader. There are two types of colormaps currently included: continuous and categorical. The continuous colormaps are perceptually uniform, with each new color equally perceptually distinct from the previous and following colors. The continuous maps were constructed by Peter Kovesi at the Center for Exploration Targeting using the methods described in Kovesi (2015).

The categorical colormaps are perceptually distinct, but not uniform; each color is meant for a separate category and not as a position on a numerical scale. Here, categorical…


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Netron is an open-source multi-platform visualizer and editor for artificial intelligence models. Netron has experimental support for PyTorch, TensorFlow, TorchScript, OpenVINO, Torch, Vitis AI, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET and scikit-learn.

It supports many extensions for deep learning, machine learning and neural network models. These include ONNX, TensorFlow Lite, Keras, Caffe, Darknet, ncnn, MNN, PaddlePaddle, Core ML, MXNet, RKNN, MindSpore Lite, TNN, Barracuda, Tengine, TensorFlow.js, Caffe2 and UFF. This library can be incredibly useful for gaining a better understanding of the flow and structure of your model or neural network. …


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The PPS or ppscore library is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two columns. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). It can be used as an alternative to the correlation (matrix). In this blog I will discuss how to use the library as well as give my thoughts on it’s functions. To get started you will want to enter the following code found in the cell below.

pip install -U ppscore

Next let’s start by generating some sample data to work with so we can get some…


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For this project I wanted to take a good look into many different factors of a company that may effect it’s customer attrition rate, or the amount of paying customers who fail to become repeat customers to a service or product. In this context, churn is a quantifiable rate of change that occurs over a specified amount of time, so let’s take a look into the details. In this example, I will be working with a dataset that includes the following variables.

  • ‘Gender’, ‘SeniorCitizen’, ‘Partner’, ‘Dependents’,
  • ‘Tenure’, ‘PhoneService’, ‘MultipleLines’, ‘InternetService’,
  • ‘OnlineSecurity’, ‘OnlineBackup’, ‘DeviceProtection’, ‘Attrition’
  • ‘TechSupport’, ‘StreamingTV’, ‘StreamingMovies’, ‘Contract’,
  • ‘PaperlessBilling’, ‘PaymentMethod’…


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Rebound is a command-line tool that instantly fetches Stack Overflow results when an exception is thrown. Just use the rebound command to execute your file. Rebound works on MacOS, Linux, and Windows (if you use Cygwin). You can install it using the code in the cell below.

pip install rebound-cli

Running a file with rebound is just as easy as running it normally:

rebound [file_path]

This will execute the file, pull the error message, and let you browse related Stack Overflow questions and answers without leaving the terminal.

Supported file types: Python, Node.js, Ruby, Golang, and Java.

Rebound is written…

@leerowe

Data Scientist | ML Engineer | Create Value

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