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CoderzColumn Tutorials

Learning is a lifelong process. But you must know what, where, and how to learn? What skills to develop? What skills will help you boost your career? If not, you are at the right place! Our tutorial section at CoderzColumn is dedicated to providing you with all the practical lessons. It will give you the experience to learn Python for different purposes and code on your own. Our tutorials cover:

  • Python Programming - threading, multiprocessing, concurrent.futures, asyncio, queue, imaplib, smtplib, email, mimetypes, cprofile, profile, tracemalloc, logging, ipywidgets, beautifulsoup, filecmp, glob, shutil, tarfile, zipfile, argparse, datetime, traceback, abc, contextlib, warnings, dataclasses, re, difflib, textwrap, collections, heapq, bisect, weakref, configparser, signa, ipaddress, xarray, pandas, subprocess, sched, etc.
  • Artificial Intelligence - PyTorch, Keras, Tensorflow, JAX, MXNet, Torchvision, Torchtext, GluonCV, GluonNLP, etc
  • Machine Learning - Scikit-Learn, Statsmodels, XGBoost, CatBoost, LightGBM, optuna, scikit-optimize, hyperopt, bayes_opt, scikit-plot, lime, shap, eli5, etc.
  • Data Science - missingno, seaborn, pandas, sweetviz, numpy, networkx, xarray, awkward-array, etc.
  • Data Visualization - Matplotlib, Bokeh, Bqplot, Plotnine, Altair, Plotly, Cufflinks, Holoviews, dash, streamlit, panel, voila, bokeh, geopandas, geoviews, folium, ipyleaflet, geoplot, cartopy, etc.
  • Digital Marketing - SEO tactics, marketing strategies, Social Media marketing, and more.

For an in-depth understanding of the above concepts, check out the sections below.

Recent Tutorials

Tags matplotlib, annotations
Add Annotations to Matplotlib Charts
Data Science

Add Annotations to Matplotlib Charts

A simple Python guide to add annotations to matplotlib charts. Tutorial explains annotations of different kinds like text labels, arrows, box annotations, bound annotations, and span annotations. Tutorial covers annotate() function to add arrow annotations in-depth.

Sunny Solanki  Sunny Solanki
Dec 01, 2022 ~25 mins
Tags bqplot, interactive-plots
bqplot - Interactive Charts in Python Jupyter Notebook
Data Science

bqplot - Interactive Charts in Python Jupyter Notebook

A detailed guide on how to create interactive charts in Jupyter Notebook using Python library bqplot. Tutorial explains various chart types like scatter charts, bar charts, line charts, heatmaps, and many more. All charts are created using pyplot API of bqplot which is same as pyplot API of matplotlib. Tutorial is a good starting point for someone new to bqplot.

Sunny Solanki  Sunny Solanki
Nov 22, 2022 ~30 mins
Tags bokeh, data-visualization
Bokeh - Basic Interactive Plotting in Python [Jupyter Notebook]
Data Science

Bokeh - Basic Interactive Plotting in Python [Jupyter Notebook]

A detailed guide on how to use Python data visualization library Bokeh to create interactive charts. Tutorial covers various chart types like bar charts, scatter charts, line charts, pie charts, heatmaps, box whisker plots, and many more. Tutorial is a good starting point for someone who wants to learn bokeh.

Sunny Solanki  Sunny Solanki
Nov 15, 2022 ~30 mins
Tags holoviews, basic-plots
Getting Started with Holoviews - Basic Interactive Plotting [Python]
Data Science

Getting Started with Holoviews - Basic Interactive Plotting [Python]

A detailed guide on how to use Python data visualization library Holoviews to create interactive charts. Holoviews is built on top of bokeh, matplotlib, and plotly. It lets us create charts using one of them as a backend. The tutorial covers various charts like scatter plots, bar charts, histograms, violin plots, and histograms. It even covers how to combine simple charts to create complicated charts.

Sunny Solanki  Sunny Solanki
Nov 12, 2022 ~45 mins
Tags bokeh, animation
Simple Guide to Create Animation using Bokeh
Data Science

Simple Guide to Create Animation using Bokeh

A detailed guide on how to create animation in Python using data visualization library Bokeh. Tutorial creates different types of chart animation like bar chart animation, candlestick chart animation, bubble chart animation, etc.

Sunny Solanki  Sunny Solanki
Nov 04, 2022 ~30 mins
Tags Bokeh, widgets, Apps, GUI
Simple Guide to use Bokeh Widgets (Interactive GUI / Apps)
Data Science

Simple Guide to use Bokeh Widgets (Interactive GUI / Apps)

A simple guide on how to create interactive GUI / apps with widgets using Python Data viz library Bokeh. Tutorial explains how we can use widgets (dropdowns, radio buttons, checkboxes, date pickers, sliders, etc) available from bokeh with simple examples. Bokeh apps explained in tutorial use Python callbacks for updating charts.

Sunny Solanki  Sunny Solanki
Nov 02, 2022 ~25 mins
Tags plotnine-charts, annotations
Add Annotations to Plotnine Charts
Data Science

Add Annotations to Plotnine Charts

A simple guide on how to add annotations to plotnine charts with simple examples. Plotnine is a Python data viz library that let us create static charts. Tutorial explains annotations like text labels, arrows, boxes, polygons, spans, slopes, etc.

Sunny Solanki  Sunny Solanki
Oct 14, 2022 ~25 mins
Tags bayesian-optimization, hyperparameters-tuning
bayes_opt: Bayesian Optimization for Hyperparameters Tuning
Machine Learning

bayes_opt: Bayesian Optimization for Hyperparameters Tuning

A comprehensive guide on how to use Python library "bayes_opt (bayesian-optimization)" to perform hyperparameters tuning of ML models. Tutorial explains the usage of library by performing hyperparameters tuning of scikit-learn regression and classification models. Tutorial also covers other functionalities of library like changing parameter range during tuning process, manually looping for tuning, guided tuning, saving and resuming tuning process, etc.

Sunny Solanki  Sunny Solanki
Oct 12, 2022 ~45 mins
Tags scikit-optimize, hyperparameters-optimization
Scikit-Optimize: Simple Guide to Hyperparameters Tuning / Optimization
Machine Learning

Scikit-Optimize: Simple Guide to Hyperparameters Tuning / Optimization

A complete guide on how to use Python library "scikit-optimize" to perform hyperparameters tuning of ML Models. Tutorial explains library usage by performing hyperparameters tuning of scikit-learn regression and classification models. Tutorial even covers plotting functionality provided by scikit-optimize to analyze hyperparameters tuning process.

Sunny Solanki  Sunny Solanki
Oct 11, 2022 ~45 mins
Tags Pandas-Bokeh, interactive-charts
Pandas-Bokeh: Create Bokeh Charts from Pandas DataFrame with One Line of Code
Data Science

Pandas-Bokeh: Create Bokeh Charts from Pandas DataFrame with One Line of Code

A detailed guide to Python data visualization library Pandas_bokeh that let us create interactive bokeh charts from pandas dataframe with just one simple function call. Tutorial covers various charts (scatter, bar, step, line, pie, histogram, scatter maps, bubble maps, etc) available from library with simple examples.

Sunny Solanki  Sunny Solanki
Oct 10, 2022 ~30 mins

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Most Popular Tutorials

1. Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks)
2. ipywidgets - An In-depth Guide to Interactive Widgets in Jupyter Notebook
3. Simple Guide to Style Display of Pandas DataFrames
4. How to Use LIME to Understand sklearn Models Predictions [Python]?
5. imaplib - Simple Guide to Manage Mailboxes using Python

Tutorial Categories

Artificial Intelligence (83)
Data Science (84)
Digital Marketing (8)
Machine Learning (38)
Python (130)

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Parallel Computing using Python

Parallel Computing using Python

Parallel Computing is a type of computation where tasks are assigned to individual processes for completion. These processes can be running on a single computer or cluster of computers. Parallel Computing makes multi-tasking super fast.

Python provides different libraries (joblib, dask, ipyparallel, etc) for performing parallel computing.

joblib

joblib
ipyparallel

ipyparallel
dask.distributed

dask.distributed
dask.delayed

dask.delayed
dask.bag

dask.bag
Concurrent Programming in Python

Concurrent Programming in Python

Concurrent computing is a type of computing where multiple tasks are executed concurrently. Concurrent programming is a type of programming where we divide a big task into small tasks and execute these tasks in parallel. These tasks can be executed in parallel using threads or processes.

Python provides various libraries (threading, multiprocessing, concurrent.futures, asyncio, etc) to create concurrent code.

threading

threading
multiprocessing

multiprocessing
concurrent.futures

concurrent.futures
asyncio

asyncio
asyncio - Synchronization Primitives

asyncio - Synchronization Primitives
queue

queue
Visualize Machine Learning Metrics

Visualize Machine Learning Metrics

Once our Machine Learning model is trained, we need some way to evaluate its performance. We need to know whether our model has generalized or not.

For this, various metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc) are designed over time. These metrics help us understand the performance of our models trained on various tasks like classification, regression, clustering, etc.

Python has various libraries (scikit-learn, scikit-plot, yellowbrick, interpret-ml, interpret-text, etc) to calculate and visualize these metrics.

Scikit-Learn: ML Model Evaluation Metrics

Scikit-Learn: ML Model Evaluation Metrics
scikit-plot

scikit-plot
yellowbrick

yellowbrick
interpret-ml

interpret-ml
interpret-text

interpret-text
Interpret Predictions Of ML Models

Interpret Predictions Of ML Models

After training ML Model, we generally evaluate the performance of model by calculating and visualizing various ML Metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc).

These metrics are normally a good starting point. But in many situations, they don’t give a 100% picture of model performance. E.g., A simple cat vs dog image classifier can be using background pixels to classify images instead of actual object (cat or dog) pixels.

In these situations, our ML metrics will give good results. But we should always be a little skeptical of model performance.

We can dive further deep and try to understand how our model is performing on an individual example by interpreting results. Various algorithms have been developed over time to interpret predictions of ML models and many Python libraries (lime, eli5, treeinterpreter, shap, etc) provide their implementation.

LIME

LIME
Shap Values

Shap Values
eli5

eli5
treeinterpreter

treeinterpreter
dice-ml

dice-ml
Python Data Visualization Libraries

Python Data Visualization Libraries

Data Visualization is a field of graphical representation of information / data. It is one of the most efficient ways of communicating information with users as humans are quite good at capturing patterns in data.

Python has a bunch of libraries that can help us create data visualizations. Some of these libraries (matplotlib, seaborn, plotnine, etc) generate static charts whereas others (bokeh, plotly, bqplot, altair, holoviews, cufflinks, hvplot, etc) generate interactive charts. Majority of basic visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are supported by all libraries. Many libraries also support advanced visualization, widgets, and dashboards.

matplotlib (Static Charts)

matplotlib (Static Charts)
bokeh (Interactive Charts)

bokeh (Interactive Charts)
bqplot (Interactive Charts)

bqplot (Interactive Charts)
plotnine (Static Charts)

plotnine (Static Charts)
altair (Interactive Charts)

altair (Interactive Charts)
seaborn (Static Charts)

seaborn (Static Charts)
holoviews (Static & Interactive Charts)

holoviews (Static & Interactive Charts)
cufflinks (Interactive Charts)

cufflinks (Interactive Charts)
hvplot (Interactive Charts)

hvplot (Interactive Charts)
Pandas-Bokeh (Interactive Charts)

Pandas-Bokeh (Interactive Charts)
Advanced Data Visualizations using Python

Advanced Data Visualizations using Python

Basic Data Visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are quite good at representing information and exploring relationships between data variables.

But sometimes these visualizations are not enough and we need to analyze data from different perspectives. For this purpose, many advanced visualizations are developed over time like Sankey diagrams, candlestick charts, network charts, chord diagrams, sunburst charts, radar charts, parallel coordinates charts, etc. Python has many data visualization libraries that let us create such advanced data visualizations.

Candlestick Charts

Candlestick Charts
Sankey Diagrams

Sankey Diagrams
Sunburst Charts

Sunburst Charts
Chord Diagrams

Chord Diagrams
Network Charts

Network Charts
Parallel Coordinates Charts

Parallel Coordinates Charts
Radar Charts

Radar Charts
Treemap Charts

Treemap Charts
Connection Maps

Connection Maps
Choropleth Maps

Choropleth Maps
Scatter & Bubble Maps

Scatter & Bubble Maps
Python Deep Learning Libraries

Python Deep Learning Libraries

Deep learning is a field in Machine Learning that uses deep neural networks to solve tasks. The neural networks with generally more than one hidden layer are referred to as deep neural networks.

Many real-world tasks like object detection, image classification, image segmentation, etc can not be solved with simple machine learning models (decision trees, random forest, logistic regression, etc). Research has shown that neural networks with many layers are quite good at solving these kinds of tasks involving unstructured data (Image, text, audio, video, etc). Deep neural networks nowadays can have different kinds of layers like convolution, recurrent, etc apart from dense layers.

Python has many famous deep learning libraries (PyTorch, Keras, JAX, Flax, MXNet, Tensorflow, Sonnet, Haiku, PyTorch Lightning, Scikeras, Skorch, etc) that let us create deep neural networks to solve complicated tasks.

PyTorch

PyTorch
Keras

Keras
JAX

JAX
Flax

Flax
MXNet

MXNet
PyTorch Lightning

PyTorch Lightning
Haiku

Haiku
Sonnet

Sonnet
Scikeras

Scikeras
Skorch

Skorch
Image Classification

Image Classification

Image classification is a sub-field under computer vision and image processing that identifies an object present in an image and assigns a label to an image based on it. Image classification generally works on an image with a single object present in it.

Over the years, many deep neural networks (VGG, ResNet, AlexNet, MobileNet, etc) were developed that solved image classification task with quite a high accuracy. Due to the high accuracy of these algorithms, many Python deep learning libraries started providing these neural networks. We can simply load these networks with weights and make predictions using them.

Python libraries PyTorch and MXNet have helper modules named 'torchvision' and 'gluoncv’ respectively that provide an implementation of image classification networks.

PyTorch (Torchvision): Image Classification using Pre-trained Networks

PyTorch (Torchvision): Image Classification using Pre-trained Networks
MXNet (GluonCV): Image Classification using Pre-trained Networks

MXNet (GluonCV): Image Classification using Pre-trained Networks
Image Classification using PyTorch CNNs

Image Classification using PyTorch CNNs
Image Classification using Flax (JAX) CNNs

Image Classification using Flax (JAX) CNNs
Image Classification using MXNet CNNs

Image Classification using MXNet CNNs