<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://towardsdatascience.com/upgrade-your-data-visualisations-4-python-libraries-to-enhance-your-matplotlib-charts-74361bc3b92e?source=rss----7f60cf5620c9---4"><img src="https://cdn-images-1.medium.com/max/845/1*CcYlk6SK9bK7c15xbH0C6w.png" width="845"></a></p><p class="medium-feed-snippet">Improve Your Data Visualisations with These Easy-to-Use Matplotlib Style Libraries</p><p class="medium-feed-link"><a href="https://towardsdatascience.com/upgrade-your-data-visualisations-4-python-libraries-to-enhance-your-matplotlib-charts-74361bc3b92e?source=rss----7f60cf5620c9---4">Continue reading on Towards Data Science »</a></p></div>

Upgrade Your Data Visualisations: 4 Python Libraries to Enhance Your Matplotlib Charts

Towards Data Science - Medium

Posted on Mar 7                  In today's data-driven world, being able to extract insights from data is a valuable skill. In this article, we'll explore how to perform basic data analysis using Python and SQL. We'll connect to a SQLite database, execute SQL queries, and visualize the results using Python libraries. 📊The database contains information about sales transactions made by a company. Here's what each field represents:The provided schema simulates a basic sales database, commonly used in retail or e-commerce settings. Each transaction (identified by TransactionID) involves the sale of one or more products. For each product sold, the database records the ProductID, ProductName, Quantity sold, PricePerUnit, and the date of the transaction (SalesDate).Purpose:This database schema and the accompanying data were created for the purpose of practicing database management and performing data analysis tasks,it is not real-world data.But it provides a simplified representation of sales transactions, making it suitable for learning SQL queries, data manipulation, and analysis.Note:It's important to note that in a real-world scenario, databases and data structures may be more complex, and additional considerations such as data integrity, normalization, and indexing would be taken into account. However, for learning and practice purposes, this schema serves as a good starting point.First, let's ensure we have the necessary tools installed. We'll need Python, SQLite, and the matplotlib library for data visualization. 💻We'll start by creating a SQLite database and populating it with some sample data. We'll then connect to this database using Python's sqlite3 library. 🔌We'll define a schema for our database containing information about sales transactions. The schema includes fields such as TransactionID, ProductID, ProductName, Quantity, PricePerUnit, and SalesDate. 💼Now that our database is set up, let's perform some basic data analysis tasks using SQL queries. 📊We'll calculate the total sales revenue for each product by multiplying the Quantity with PricePerUnit and summing it up. 💰We can visualize the results using Python libraries such as matplotlib. 📊We'll find the top 3 best-selling products based on the total quantity sold. 🏆outputWe'll calculate the average price per unit for all products. 📈outputIn this article, we've demonstrated how to connect to a SQLite database using Python, execute SQL queries, and perform basic data analysis tasks. By leveraging Python libraries such as sqlite3 and matplotlib, we can efficiently analyze data and gain valuable insights. 🚀  Templates let you quickly answer FAQs or store snippets for re-use.        Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink.      Hide child comments as well                  Confirm              For further actions, you may consider blocking this person and/or reporting abuse              Namee - Mar 4                          Thiago da Silva Adriano - Mar 3                          Pranjal Sharma - Mar 2                          eaca89 - Mar 3                  DEV Community — A constructive and inclusive social network for software developers. With you every step of your journey.    Built on Forem — the open source software that powers DEV and other inclusive communities.Made with love and Ruby on Rails. DEV Community © 2016 - 2024.          We're a place where coders share, stay up-to-date and grow their careers.

Python Database Connectivity and SQL Basics for EDA 🐍📊. We'll connect to a SQLite database, execute SQL queries, and visualize the results using Python libraries.

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Python Database Connectivity and SQL Basics for EDA 🐍📊

Posted on Mar 6                  In this post, we'll explore some fundamental statistical concepts using Python and explain them in detail. We'll be working with a dataset of student scores in an exam, and we'll use Python's statistics module and matplotlib library for visualization.Let's start by importing the necessary libraries and defining our dataset:students dataMean, Median, and ModeThe mean represents the average value of the dataset, while the median is the middle value when the data is arranged in ascending order. The mode is the most frequent value in the datasetStandard Deviation and VarianceStandard deviation measures the dispersion of data points from the mean, while variance represents the average of the squared differences from the mean.Range and QuartilesThe range is the difference between the maximum and minimum values in the dataset. Quartiles divide the data into four equal parts.Interquartile Range (IQR)The Interquartile Range (IQR) is the range between the first and third quartiles, measuring the spread of data.Correlation CoefficientThe correlation coefficient measures the linear relationship between two variables. We'll calculate the correlation coefficient between hours studied and test scoresScatter Plot VisualizationLastly, we'll visualize the relationship between hours studied and test scores using a scatter plot.Templates let you quickly answer FAQs or store snippets for re-use.        Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink.      Hide child comments as well                  Confirm              For further actions, you may consider blocking this person and/or reporting abuse              Victor Sabare - Feb 9                          Manish - Feb 5                          Scofield Idehen - Feb 27                          Jimmy Guerrero - Feb 8                  DEV Community — A constructive and inclusive social network for software developers. With you every step of your journey.    Built on Forem — the open source software that powers DEV and other inclusive communities.Made with love and Ruby on Rails. DEV Community © 2016 - 2024.          We're a place where coders share, stay up-to-date and grow their careers.

Statistical Essentials for Data Analysts: A Beginner's Guide. The mean represents the average value of the dataset, while the median is the middle value when the data is arranged in ascending order.

Statistical Essentials for Data Analysts: A Beginner's Guide

LLMs Meet SQL: Revolutionizing Data Querying with Natural Language Processing. This object encapsulates the prompt template, making it easy to generate prompts dynamically by filling in the placeholders with specific query strings, SQL queries, and query results

Level Up Coding - Medium

LLMs Meet SQL: Revolutionizing Data Querying with Natural Language Processing

Sign upSign inSign upSign inRitanshi AgarwalFollowCode Like A Girl--ListenShareHello! If you're new to convolutional neural networks (CNNs) and want to create your own image-based classifier using Python, this post is for you.Machine Learning (ML) involves training computer systems that can work without explicit directions to complete a given task. During supervised training, we feed data and its corresponding label to a network (system) and get the rule as the trained model. In layman’s terms, ‘2 + 2 = 4’, we feed [2, 2] and [4] into the system, and get the ‘+’ operation as a trained model. So, next time we give [3, 6] to the trained model, it automatically generates [9] as output. These outputs from a trained model are called predictions.For training, we need a large amount of data so that a model can learn the desired operation as accurately as possible. ML models are not just for linear numbers, they can be trained for any operations, even non-linear ones if you have the data. Images are just another form of numerical data. When our training inputs are images, instead of a list of numbers, then we call the model, a Convolutional Neural Network (CNN). The CNNs extract the feature details from an image by performing a mathematical operation called convolution and then pass those features as a list of numerical values to the rest of the model, say a neural network.I have an awesome book in mind for those who want a deep understanding of machine learning and neural networks. “MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence” by Phil Kim.In this article, we are going to build an image-classifier that can recognize the hand-written digits solely. We can see it as how a child learns to read and write letters, digits, and symbols. I mean by rewriting, relearning, and redoing these two steps again and again. In the same way, a machine learning model trains itself repeatedly, calculates a loss function, and tries to optimize it on each turn. Let’s see this made step-by-step in Python.Let’s begin with importing some libraries for the model. We need TensorFlow, of course. Because we are dealing with images, we need Numpy to treat them as matrices. And finally, to plot the training and output results, Matplotlib is here too.MNIST dataset is openly available for enthusiasts to get started with machine learning. For this project, we are using the one available on Kaggle. It has two CSV files: train.csv and test.csv.Load mnist digits data using np.loadtxt(). We can also use csv_reader(), but loading the data takes much longer. I have divided the data from train.csv into 29400 training data and 12600 as validation data.We will use the Conv2D layer from tensorflow.keras, so we need to add one more dimension to our data. The input format to the Conv2D layer is (Number_of_data, height_image, width_image, Number_channel). So, here, Number_channel = 1 (pixel values: 0 to 255 takes 1 byte for representation).The following code implements the CNN with one convolutional layer with maximum pooling function, a hidden layer, and an output dense layer.Overfitting is a condition where the model cannot classify images other than the ones it’s trained on. For example, we all know that the number ‘4’ is written differently in different countries. So, if we train our model only with images of one kind of number’s look, it might not be able to identify the real test image. To avoid this, we included a Dropout layer in our CNN model. This layer drops 20% of the hidden layer nodes, making the model more realistically trainable.So, we are plotting training and validation loss and accuracy to check whether data overfit. The target is to get plots (red and blue lines, here) coinciding towards one point in the end with as little difference as possible.Well, we got both the graphs intersecting each other and going in the same direction with a slight change. So I have considered it a ‘Just good’ situation with no underfit and no overfit. But there is always a scope for improvement!!Our target is to get a matrix with maximum numbers in the diagonal, which shows that both true and predicted labels are the same. This matrix is called Confusion Matrix and can be plotted using a function from another Python library, called Scikit-learn.Now, we are predicting the classes for the testing data in the test.csv file. We can change the value of ‘idx’ below (from 0 to 27999) to check some more of the results.The following code will help you save the test results in a CSV file for further analysis.Great!!! We did it. We just created our first machine-learning model in Python. That’s pretty cool. Everyone who has started with the CNNs, has used the MNIST Digit Dataset as one of their getting-started models for sure.Now, you can change the image dataset with any other use case, for instance, facial expressions, object detections, etc., and run a CNN model on it, starting with one layer, playing around with filter size, adding more layers, etc.Well, We are done here with this simple code. I hope you guys had fun. (Maybe a little!). I’ll be back with another article soon.Have fun researching!!Regards,Ritanshi----Code Like A GirlLearner. Researcher. Engineering Scholar.......HelpStatusAboutCareersBlogPrivacyTermsText to speechTeams

Recognizing Handwritten Numbers. Well, we got both the graphs intersecting each other and going in the same direction with a slight change.

Code Like A Girl - Medium

Recognizing Handwritten Numbers

Sign upSign inSign upSign inSabrine BendimeradFollowCode Like A Girl--ListenShareAre you curious about how to make your data science skills stand out? You’re in the right place! Each project within this guide has been meticulously chosen to transition you from foundational skills to tackling advanced problems, all while providing you with the datasets and methodologies needed for success. I’ve also had the privilege of mentoring over a hundred data scientists through these projects, witnessing firsthand their journey from novices to experts.So, take the plunge ! Regardless of your current level, these projects are designed to illuminate your path in the data science field, catering to everyone from beginners to seasoned practitioners with a spectrum of projects covering Python basics to complex machine learning challenges.Goal: Master Python basics by creating a tic-tac-toe game against the computer.Data Source: N/A — Algorithmic logic based on game rules.Methodology: Develop a console-based game to practice Python syntax, conditions, loops, data types, and basic algorithms.Detailed Steps: On my githubLibraries: N/A (standard Python).Goal: Analyze data from speed dating experiments to identify factors influencing the decision to go on a second date.Data Source: EDA Speed Dating ExperimentMethodology: Use statistical analysis and visualization tools to explore and interpret dating preferences.Detailed Steps:Libraries: pandas, numpy, matplotlib, seaborn, scipy.Goal: Predict energy consumption of buildings to aid in climate action goals.Data Source: Seattle’s 2016 Building Energy BenchmarkingMethodology: Use machine learning to analyze and predict building energy consumption.Detailed Steps:Libraries: pandas, sklearn, xgboost, matplotlib ,shap .Goal: Segment customers using clustering techniques to identify distinct groups within Brazilian e-commerce data.Data Source: Brazilian E-Commerce Public Dataset by OlistMethodology: Apply unsupervised learning to discover customer segments based on purchasing behaviour.Detailed Steps:Libraries: pandas, sklearn, matplotlib, seaborn ,yellowbrick.Goal: Implement a deep learning model to classify images from the STL-10 dataset.Data Source: STL-10 Image Recognition DatasetMethodology: Explore and apply convolutional neural networks (CNNs) and transfer learning for image classification.Detailed Steps:Libraries: tensorflow, keras, pytorch, matplotlib ,skimage, matplotlib.Goal: Predict tags for Stack Overflow questions using NLP techniques.Data Source: Stack Overflow API or dataset.Methodology: Utilize natural language processing to classify text data into multiple tags.Detailed Steps:Libraries: pandas,nltk, transformers , spacy, gensim, tensorflow .Goal: Deploy a model as an API and build a dashboard for real-time interaction.Data Source: Use a model from Project 6.Methodology: Serialize a machine learning model and deploy it via an API, then build a dashboard for user interaction.Detailed Steps:Libraries: flask, fastapi, streamlit, dash, pickle, joblib.Goal: Implement MLOps practices including model monitoring, experiment tracking, and automated deployment.Data Source: Use Project 7.Methodology: Integrate MLflow for tracking experiments, set up automated deployment with GitHub Actions, and monitor model performance.Detailed Steps:Libraries: MLflow, GitHub Actions, AWS S3 (for artifact storage).In crafting your data science portfolio, the selection of projects is crucial. Opt for those that resonate with your skills, experience, and passions. This ensures not only your commitment to completion but also a deeper engagement with your work.Here are key tips for a standout portfolio:By following these tips, you’ll create a portfolio that showcases your skills and helps you stand out to potential employers.Thank you for reading!If you found this article informative and helpful, please don’t hesitate to 👏 and follow me on Medium | LinkedIn.----Code Like A GirlA senior AI engineer working on AI applications across major industries for 8 years. Currently working on AI based solutions for the water sector.HelpStatusAboutCareersBlogPrivacyTermsText to speechTeams

8 Projects to Skyrocket Your Data Science Portfolio. Data Source: Seattle’s 2016 Building Energy BenchmarkingMethodology: Use machine learning to analyze and predict building energy consumption