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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 asyncio, async-await, concurrent_programming
asyncio - Concurrent Programming using Async / Await Syntax in Python
Python

asyncio - Concurrent Programming using Async / Await Syntax in Python

A detailed guide on how to use Python Module "asyncio" and keywords "async" & "await" for concurrent programming in Python. Tutorial covers how to create coroutines using "async/await" syntax and run them in parallel using "asyncio" module. Tutorial covers topics like creating coroutines, collecting results once coroutines are complete, waiting for coroutines, canceling coroutines, retrieving pending coroutines, etc in detail.

Sunny Solanki  Sunny Solanki
Sep 11, 2022 ~30 mins
Tags hyperopt, hyperparameters-optimization
Hyperopt - Complete Guide to Hyperparameters Tuning / Optimization
Machine Learning

Hyperopt - Complete Guide to Hyperparameters Tuning / Optimization

A comprehensive guide on how to use Python library 'hyperopt' for hyperparameters tuning with simple examples. Tutorial explains how to fine-tune scikit-learn models solving regression and classification tasks. Tutorial is a complete guide to hyperparameters optimization of ML models in Python using 'hyperopt'.

Sunny Solanki  Sunny Solanki
Sep 09, 2022 ~45 mins
Tags sklearn, naive-bayes
Scikit-Learn - Naive Bayes Classifiers
Machine Learning

Scikit-Learn - Naive Bayes Classifiers

A simple guide to use naive Bayes classifiers available from scikit-learn to solve classification tasks. All 5 naive Bayes classifiers available from scikit-learn are covered in detail. Tutorial first trains classifiers with default models on digits dataset and then performs hyperparameters tuning to improve performance. Various ML metrics are also evaluated to check performance of models.

Sunny Solanki  Sunny Solanki
Sep 08, 2022 ~30 mins
Tags time-series, dates, times, timezones
Dates, Timestamps, Timedeltas, Periods & Time Zone Handling in Python using Pandas
Data Science

Dates, Timestamps, Timedeltas, Periods & Time Zone Handling in Python using Pandas

A simple guide to work with dates, timestamps, periods, time deltas, and time zones using Python library Pandas. Tutorial covers aspects like creating date time ranges / time stamps / time deltas / periods / period ranges, adding / subtracting time deltas from dates / periods, adding time zone to dates, converting time zone of dates, etc.

Sunny Solanki  Sunny Solanki
Sep 07, 2022 ~20 mins
Tags time-series, resampling, moving-window-functions
Time Series - Resampling & Moving Window Functions in Python using Pandas
Data Science

Time Series - Resampling & Moving Window Functions in Python using Pandas

A detailed guide to resampling time series data using Python Pandas library. Tutorial covers pandas functions ('asfreq()' & 'resample()') to upsample and downsample time series data. Apart from resampling, tutorial covers a guide to apply moving window functions ('rolling', 'expanding' & 'ewm()') to time series data as well. The rolling window, expanding window and exponential moving average is covered in tutorial.

Sunny Solanki  Sunny Solanki
Sep 06, 2022 ~25 mins
Tags email
email - How to Represent an Email Message in Python?
Python

email - How to Represent an Email Message in Python?

A complete guide on how to use Python library "email" to represent emails with simple examples. Tutorial covers topics like creating a simple email message, emails with CC / BCC, emails with diff types of attachments, setting HTTP headers in emails, combining different content types in emails, etc.

Sunny Solanki  Sunny Solanki
Aug 30, 2022 ~20 mins
Tags sunburst-chart, plotly
How to Create Sunburst Chart / Diagram in Python [Plotly]?
Data Science

How to Create Sunburst Chart / Diagram in Python [Plotly]?

A simple guide to creating sunburst charts in Python using interactive data visualization library Plotly. Tutorial explains how we can use plotly express and plotly graph objects API of a library to create sunburst charts. The sunburst chart is also referred to by other names like multi-level pie chart, ring chart, donut chart, doughnut chart, or radial treemap.

Sunny Solanki  Sunny Solanki
Aug 29, 2022 ~20 mins
Tags smtp, send-mails
smtplib - Simple Guide to Sending Mails using Python
Python

smtplib - Simple Guide to Sending Mails using Python

A detailed guide on how to use Python library "smtplib" to send emails (Gmail, Yahoo, etc) with simple examples. Tutorial covers various operations with mailbox servers like login / logout, verifying email ids, sending emails with CC / BCC, sending mails with attachments, etc. It uses SMTP protocol behind the scene to send emails.

Sunny Solanki  Sunny Solanki
Aug 29, 2022 ~20 mins
Tags system-signals
signal - Simple Guide to Send, Receive and Handle System Signals in Python
Python

signal - Simple Guide to Send, Receive and Handle System Signals in Python

A comprehensive guide on how to use Python module "signal" to send, receive and handle system (Unix/Windows) signals to signal some event. The library lets us catch signal and run a handler (callback) based on event represented by signal. The signal can be sent to different threads and processes to inform them about event and execute handler by them accordingly.

Sunny Solanki  Sunny Solanki
Aug 28, 2022 ~20 mins
Tags difflib, sequence-comparison
difflib - Simple Way to Find Out Differences Between Sequences / File Contents using Python
Python

difflib - Simple Way to Find Out Differences Between Sequences / File Contents using Python

A simple guide on how to use Python module “difflib” to compare sequences and find out differences between them. Tutorial explains whole API of a module to explain different ways of comparing sequences and format results in different ways. It can be very useful to compare file contents to see differences.

Sunny Solanki  Sunny Solanki
Aug 27, 2022 ~25 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