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This repository is a curated list of essential Data Science libraries, showcasing the core tools like Pandas and NumPy for data manipulation, as well as innovative libraries such as Seaborn and PyCaret for advanced data visualization and automated machine learning workflows.
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Welcome to the Data Science Library Hub, a curated collection of the most pivotal and innovative tools in the Pyhton Data Science ecosystem. My aim is to serve as a comprehensive resource for data scientists, analysts, and enthusiasts.
This repository is a roadmap to the vast landscape of Python libraries that drive analysis, insights, and machine learning. From data manipulation with Pandas and NumPy to creating sophisticated models with Scikit-learn, and visualizations with MatplotLib and Seaborn - these tools form the core of day-to-day Data Science work.
Here, you will also discover libraries that are pushing the boundaries, whether through elegant solutions for complex problems or by introducing new paradigms altogether. Libraries like Altair for declarative visualizations, PyCaret for automating machine learning workflows like MLflow), and Pingouin for advanced statistical analysis show the exciting direction of our field.
Each library is listed with its description, GitHub link, and tags for easy navigation and reference.
In the realm of Data Science libraries, each tool often possesses a unique set of capabilities, potentially spanning multiple functional areas. To simplify categorization, we have grouped these libraries into five distinct tags, acknowledging that some libraries could fit into more than one category.
Data Manipulation: This category encompasses libraries that are integral to processing and transforming data. They are the backbone of data analysis, offering functionalities for sorting, filtering, and summarizing data.
Data Visualization: Libraries under this tag focus on the graphical representation of data. They enable users to create a wide array of charts, graphs, and other visual tools to make data more understandable and engaging.
Machine Learning: This category includes libraries specifically designed for developing machine learning models. They provide tools for training, testing, and deploying algorithms that can learn from and make predictions on data.
Selected from shared topics, language and repository description—not editorial ratings.
chaklam-silpasuwanchai /
This is the repository for the course Python for Data Science covering Python Basics, Data Science Libraries (e.g., Pandas, Matplotlib, Sklearn).
65/100 healthtanvirakibul /
Predicting heart disease using machine learning¶ This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem definition Data Evaluation Features Modelling Experimentation 1. Problem Definition In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease There is also a version of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci 3. Evaluation If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project. 4. Features Create data dictionary age - age in years sex - (1 = male; 0 = female) cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber thalach - maximum heart rate achieved exang - exercise induced angina (1 = yes; 0 = no) oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more stress more slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots) thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising target - have disease or not (1=yes, 0=no) (= the predicted attribute)
Advanced Tools: Here, you will find libraries that offer specialized functionalities, often for specific, more complex tasks in data science, such as high-performance computing, advanced statistical models, or large-scale data processing.
Development and Debugging: Libraries in this group are geared towards facilitating the development process itself, including code writing, testing, and debugging. They enhance the efficiency and quality of the development workflow.
In the realm of Data Science, certain tools form the backbone of data analysis and modeling. This section, Key Data Science Tools, focuses on the essential libraries that are foundational for any Data Science practitioner. Ranging from data manipulation to basic visualization and statistical analysis, these tools are the building blocks for developing robust Data Science solutions. They include well-known libraries like Pandas for data processing, Scikit-learn for machine learning, and MatplotLib for plotting and visualization, among others. Whether you are just starting out or are a seasoned data scientist, these are the tools you will turn to time and again.
| Library Name | Description | GitHub Link | Tags |
|---|---|---|---|
| matplotlib | 2D Plotting library for Python | MatplotLib | Data Visualization |
| seaborn | Statistical data visualization | Seaborn | Data Visualization |
| altair | Declarative statistical visualization library for Python | Altair | Data Visualization |
| pandas | Data manipulation and analysis | Pandas | Data Manipulation |
| numpy | Numerical computing with Python | NumPy | Data Manipulation |
| scipy | Scientific computing and technical computing | SciPy | Data Manipulation |
| math | Mathematical functions defined by the C standard | - | Data Manipulation |
| itertools | Functions creating iterators for efficient looping | - | Data Manipulation |
| Scikit-learn | Machine learning in Python | Scikit-learn | Machine Learning |
| yaml | YAML parser and emitter for Python | YAML | Advanced Tools |
| joblib | Lightweight pipelining: using Python functions as pipeline jobs | joblib | Advanced Tools |
| pickle | Serialize Python object structures | - | Advanced Tools |
| bs4 (BeautifulSoup) | Pulling data out of HTML and XML files | BS4 | Advanced Tools |
| requests | HTTP library for Python | request | Advanced Tools |
| urllib | URL handling modules for Python | - | Advanced Tools |
| xlsxwriter | Python module for creating Excel XLSX files | xlsxwriter | Advanced Tools |
| time | Time access and conversions | - | Development and Debugging |
| sqlite3 | Database engine included with Python | - | Development and Debugging |
| IPython | Powerful interactive shell for Python | IPython | Development and Debugging |
| datetime | Basic date and time types | - | Development and Debugging |
| warnings | Non-fatal alerts used to issue cautionary advice | - | Development and Debugging |
Beyond the basics, the Data Science landscape is enriched by a variety of innovative tools that push the boundaries of data analysis and model development. In Innovative Data Science Tools, we explore libraries that bring unique functionalities, offer enhanced performance, or simplify complex processes in groundbreaking ways. These tools might not be as widely known as the foundational ones, but they are invaluable for specialized tasks, advanced analysis, and for keeping your methodologies at the cutting edge. From libraries that handle large datasets with ease, to those that provide novel approaches to machine learning and data visualization, this section is dedicated to the tools that are reshaping the future of Data Science.
| Library Name | Description | GitHub Link | Tags |
|---|---|---|---|
| YellowBrick | A suite of visualization and diagnostic tools for faster model selection. | YellowBrick | Data Visualization |
| Missingno | Visualize missing values in your dataset with ease. | Missingno | Data Visualization |
| stickyland | Break the linear presentation of Jupyter Notebooks with sticky cells! | stickyland | Data Visualization |
| PyGWalker | PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis | PyGWalker | Data Visualization |
| lux | Automatically visualize your pandas dataframe via a single print! | lux | Data Visualization |
| SweetViz | In-depth EDA report in two lines of code. | SweetViz | Data Visualization |
| Pivot Table JS | Drag-n-drop tools to group, pivot, plot dataframe. | Pivot Table JS | Data Visualization |
| DABEST | Data Analysis using Bootstrapped ESTimation | DABEST | Data Visualization |
| tableone | Create "Table 1" summaries for research papers | TableOne | Data Visualization |
| statannot | Add statistical annotations on an existing boxplot/barplot | StatAnnot | Data Visualization |
| imbalanced-learn | A variety of methods to handle class imbalance. | Imbalanced Learn | Data Manipulation |
| Modin | Boost Pandas' performance up to 70x by modifying the import. | Modin | Data Manipulation |
| Parallel-Pandas | Parallelize Pandas across all CPU cores for faster computation. | Parallel Pandas | Data Manipulation |
| Vaex | High performance package for lazy Out-of-Core DataFrames. | Vaex | Data Manipulation |
| statsmodels | Statistical testing and data exploration at fingertips. | StatsModels | Data Manipulation |
| Pandas-Profiling | Generate a high-level EDA report of your data in no time. | Pandas Profiling | Data Manipulation |
| Category-encoders | Over 15 categorical data encoders. | Category Encoders | Data Manipulation |
| DuckDB | Run SQL queries on DataFrame. | DuckDB | Data Manipulation |
| Numexpr | Parallelize NumPy to all CPU cores for 20x speedup. | NumExpr | Data Manipulation |
| CSV-Kit | Explore, query, and describe CSV files from the terminal. | CSV-Kit | Data Manipulation |
| pingouin | Statistics in Python | Pingouin | Data Manipulation |
| Sidetable | Supercharge Pandas' value_counts() method. | Side Table | Data Manipulation |
| PyCaret | Automate ML workflows with this low-code library. | PyCaret | Machine Learning |
| mlflow | Open source platform for the machine learning lifecycle | MLflow | Machine Learning |
| SHAP | Explain the output of any ML model in a few lines of code. | [SHAP |
ankur3907 /
Predicting Heart disease using Machine Learning This notebook looks into using various Python-based ML and Data Science libraries in an attempt to build a Machine Learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem Definition Data Evaluation Features Modelling About Heart Disease Cardiovascular disease or heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease. From WHO statistics every year 17.9 million dying from heart disease. The medical study says that human life style is the main reason behind this heart problem. Apart from this there are many key factors which warns that the person may/maynot getting chance of heart disease. From the dataset if we create suitable machine learning technique which classify the heart disease more accurately, it is very helpful to the health organisation as well as patients. 1. Problem Defintion In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? 2. Data The original data came from the Cleavland data from the UCI Machine Learning Repository There is also a version of it available on Kaggle. Heart Disease Date 3. Evaluation Target to reach more than 85% If the model scored better than 85% we will select the model 4. Features age: displays the age of the individual. sex: displays the gender of the individual using the following format : 1 = male 0 = female cp (Chest-Pain Type): displays the type of chest-pain experienced by the individual using the following format : 0 = typical angina 1 = atypical angina 2= non — anginal pain 3 = asymptotic trestbps(Resting Blood Pressure): displays the resting blood pressure value of an individual in mmHg (unit) chol(Serum Cholestrol): displays the serum cholesterol in mg/dl (unit) fbs (Fasting Blood Sugar): compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false) restecg (Resting ECG): displays resting electrocardiographic results 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy thalach(Max Heart Rate Achieved): displays the max heart rate achieved by an individual. exang (Exercise induced angina): 1 = yes 0 = no oldpeak (ST depression induced by exercise relative to rest): displays the value which is an integer or float. slope (Peak exercise ST segment) : 0 = upsloping 1 = flat 2 = downsloping ca (Number of major vessels (0–3) colored by flourosopy): displays the value as integer or float. thal : displays the thalassemia (is an inherited blood disorder that causes your body to have less hemoglobin than normal) : 0 = normal 1 = fixed defect 2 = reversible defect target (Diagnosis of heart disease): Displays whether the individual is suffering from heart disease or not : 0 = absence 1 = present. Preparing the tools We are going to use :- Pandas & Numpy for Data Analysis and Manipulation Matplotlib and Seaborn for Data Visualisation Scikit-Learn for the Modeling building and Reports
44/100 healthkunal-ppatil /
This repository contains a collection of data analytics projects, analyses, and visualizations using Python and popular data science libraries. It is designed to showcase practical applications of data manipulation, exploration, statistical modeling, and predictive analytics.
53/100 healthsnehalap7480 /
This repository contains a collection of data analytics projects, analyses, and visualizations using Python and popular data science libraries. It is designed to showcase practical applications of data manipulation, exploration, statistical modeling, and predictive analytics.
52/100 healthTikhon-Radkevich /
This repository contains a Python implementation of a polynomial regression model, designed to predict Y-coordinates based on input X-values. The model is built without using popular data science libraries like NumPy, scikit-learn, or TensorFlow, making it a self-contained and educational project.
31/100 health