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Andy McDonald
United Kingdom
Приєднався 31 тра 2021
#petrophysics #python #matplotlib #geoscience
Structuring and Organising Streamlit Apps
Ensuring your Streamlit app is well organised can go a long way to helping you stay sane when developing your app or provide a nice starting point that saves you time by not having to create a new folder structure from scratch. Using cookiecutter templates, like the Streamlit Cookiecutter template can help automate the process and get you off to a better start when creating your app.
Get The Cookiecutter Template: github.com/andymcdgeo/cookiecutter-streamlit
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towardsdatascience.com/how-to-structure-and-organise-a-streamlit-app-e66b65ece369
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🔽 CONNECT WITH ME
Thanks for watching, if you want to connect you can find me at the links below:
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#datascience #petrophysics #python #streamlit #eda
Get The Cookiecutter Template: github.com/andymcdgeo/cookiecutter-streamlit
⭐️ If you haven't already, make sure you subscribe to the channel: ua-cam.com/channels/n1O_4_ApzbYwrsUdRoMmOg.html
🎒READ THE ARTICLE
Check out the article version of this video on Medium:
towardsdatascience.com/how-to-structure-and-organise-a-streamlit-app-e66b65ece369
🙌 SUPPORT THE CHANNEL
- Hit the "Thanks" button on any video
- Buy Me a Coffee: www.buymeacoffee.com/andymcdonaldgeo
🔽 CONNECT WITH ME
Thanks for watching, if you want to connect you can find me at the links below:
- Medium: andymcdonaldgeo.medium.com/
- LinkedIn: www.linkedin.com/in/andymcdonaldgeo/
- Official Website: www.andymcdonald.scot/
#datascience #petrophysics #python #streamlit #eda
Переглядів: 788
Відео
Creating Waffle Chart Subplots With Matplotlib - Easy Data Visualisation for Geoscience
Переглядів 5319 місяців тому
This video follows my previous video where we created a basic waffle chart using PyWaffle. In this video, I share how you can quickly and easily display waffle charts as subplots in matplotlib, which can make it easier to understand the variances between different categories. ⭐️ If you haven't already, make sure you subscribe to the channel: ua-cam.com/channels/n1O_4_ApzbYwrsUdRoMmOg.html ▼ SUP...
Creating Waffle Charts With Matplotlib and PyWaffle
Переглядів 88110 місяців тому
Waffle charts are a great way to visualise categorical data, are aesthetically pleasing and easy for readers to understand - which is one of the key goals of effective data visualisations. They also provide a nicer looking alternative to pie charts. Waffle charts are square or rectangular displays made up of smaller squares in a grid pattern. Most commonly, it is a 10 x 10 grid, but they can be...
Styling Your Matplotlib Figures With a Cyberpunk Theme
Переглядів 1,2 тис.10 місяців тому
When we create infographics or posters containing data, we want to catch the reader’s attention and make it aesthetically pleasing to look at whilst telling a convincing story. Within Python, we have numerous plotting libraries that allow us to create charts - one such library is the well-known matplotlib library. However, out of the box, the plots generated by matplotlib are often seen as bori...
Displaying Maps With Plotly Express Mapbox and Streamlit
Переглядів 3,2 тис.10 місяців тому
Streamlit provides a quick and easy way to build interactive applications and dashboards for data analysis and machine learning. If we are looking to build a data analysis app within Streamlit that uses data containing location information, one of the first visualisations we may want to consider adding is a map. Having an interactive map within our app allows us to visualise where the data poin...
Creating Geospatial Heatmaps With Plotly Express MapBox and Folium in Python - Data Visualisation
Переглядів 3,9 тис.10 місяців тому
Heatmaps, also known as Density Maps, are data visualisations that display the spatial distribution of a variable across a geographic area. They can be great tools for visualising and identifying trends, supporting decision-making, detecting outliers, and creating compelling visualisations for presentations. There are several mapping Python libraries available; however, two very popular and eas...
Pandas Dataframes - Data Aggregation Using Geological Lithology Data
Переглядів 48511 місяців тому
Using data aggregation techniques can help us transform an overwhelming and almost incomprehensible numeric dataset into something that is easily digestible and much more reader-friendly. The process of data aggregation involves summarising multiple data points into single metrics that can be used to provide a high-level overview of the data. One way we can apply this process within petrophysic...
How To Make Your Matplotlib Bar Charts Stand Out
Переглядів 1,9 тис.11 місяців тому
Bar charts are a commonly used data visualisation tool where categorical features are represented by bars of varying lengths/heights. The height or length of the bar corresponds to the value being represented for that category. Bar charts can easily be created in matplotlib. However, the matplotlib library is often regarded as a library that produces unexciting charts and can be challenging to ...
PyGWalker for Exploratory Data Analysis In Jupyter Notebooks
Переглядів 13 тис.Рік тому
PyGWalker (Python binding of Graphic Walker) is a python library that can help speed up the data analysis and visualisation workflow directly within a Jupyter notebook. It leverages the power of interactivity by providing an interface similar to the popular data analytics software called Tableau. This video will explore some of the features of PyGWalker using one of my favourite well log data s...
Isolation Forest for Outlier Detection within Python
Переглядів 28 тис.Рік тому
Isolation Forest is a popular unsupervised machine learning algorithm for detecting anomalies (outliers) within datasets. Anomaly detection is a crucial part of any machine learning and data science workflow. Erroneous values that are not identified early on can result in inaccurate predictions from machine learning models, and therefore impact the interpretation of those results. The code and ...
Working With Well Survey Data in Python Using wellpathpy
Переглядів 2,6 тис.2 роки тому
Depth is an essential measurement when working with subsurface data. It is used to tie multiple sets of data to a single reference. There are numerous depth references used to identify a position within the subsurface. These include Measured Depth (MD), True Vertical Depth (TVD), and True Vertical Depth Subsea (TVDSS). When wells are drilled, survey measurements are often taken to ensure that t...
Combining Well Log Data With Formation Tops in Python for Petrophysics
Переглядів 2,9 тис.2 роки тому
When working with well log data we often have to deal with different data sources and sampling rates. One area where we commonly experience this is with well log data and formation tops. Formation top data contains a formation name along with a single depth reference, whereas well log data is regularly depth sampled. In this video I will go over the process on how to create a master dataframe w...
Random Forest Regression Machine Learning - Well Log Prediction for Petrophysics
Переглядів 3,9 тис.2 роки тому
Random forest is a very popular machine learning algorithm that can be used for both classification and regression. Within this tutorial, we will see how we can use the Random Forest algorithm to predict a continuous output using well logs as an example. ⭐️ If you haven't already, make sure you subscribe to the channel: ua-cam.com/channels/n1O_4_ApzbYwrsUdRoMmOg.html ▼ SUPPORT THE CHANNEL ▼ ☕️ ...
Porosity Permeability (Poro-Perm) Log-Linear Regression in Python - Petrophysics
Переглядів 2,9 тис.2 роки тому
Permeability is one of the key reservoir properties we as petrophysicists attempt to derive as part of our workflow. As well logging tools do not provide a direct measurement for permeability, we have to infer it through relationships with core data from the same field or well, from empirically derived equations or NMR data. One common method of deriving permeability is to plot core porosity (o...
Seaborn Heatmap - How to Visualise Correlations and Data With Heatmaps in Python
Переглядів 35 тис.2 роки тому
Heatmaps are a great way to visualise tabular data. They allow us to identify trends, spot outliers and understand the range of our data. In this week's video, we are going to see how to visualise data using a Seaborn Heatmap. ⭐️ If you haven't already, make sure you subscribe to the channel: ua-cam.com/channels/n1O_4_ApzbYwrsUdRoMmOg.html ▼ SUPPORT THE CHANNEL ▼ ☕️ BUY ME A COFFEE: www.buymeac...
Seaborn Pairplot - How to Create a Pairplot for Data Visualization in Python Using Seaborn
Переглядів 6 тис.2 роки тому
Seaborn Pairplot - How to Create a Pairplot for Data Visualization in Python Using Seaborn
Documenting Your Code with Python - Overview of Comments, Docstrings and Type Hints
Переглядів 2,4 тис.2 роки тому
Documenting Your Code with Python - Overview of Comments, Docstrings and Type Hints
Random Forest Machine Learning Tutorial in Python for Lithology Prediction - Includes Overview
Переглядів 6 тис.2 роки тому
Random Forest Machine Learning Tutorial in Python for Lithology Prediction - Includes Overview
New Streamlit Multi-Page Web Apps - Converting Existing Apps
Переглядів 12 тис.2 роки тому
New Streamlit Multi-Page Web Apps - Converting Existing Apps
Creating Multiple Subplots the Easy Way - Seaborn FacetGrid Introduction
Переглядів 5 тис.2 роки тому
Creating Multiple Subplots the Easy Way - Seaborn FacetGrid Introduction
Seaborn Relplot - Create Scatter Plots and Line Plots in Python
Переглядів 1,8 тис.2 роки тому
Seaborn Relplot - Create Scatter Plots and Line Plots in Python
Create Semi Log Scatter Plots in Python - Display Data on a Logarithmic Axis in Seaborn
Переглядів 2,3 тис.2 роки тому
Create Semi Log Scatter Plots in Python - Display Data on a Logarithmic Axis in Seaborn
Adding Interactive Plotly Charts to a Streamlit App
Переглядів 23 тис.2 роки тому
Adding Interactive Plotly Charts to a Streamlit App
Creating Multi-Page Streamlit Apps | Python Streamlit Series Part 2
Переглядів 13 тис.2 роки тому
Creating Multi-Page Streamlit Apps | Python Streamlit Series Part 2
Getting Started With Streamlit in Python
Переглядів 28 тис.2 роки тому
Getting Started With Streamlit in Python
Fast and Effective Exploratory Data Analysis (EDA) With Python and Pandas Profiling for Data Science
Переглядів 7 тис.2 роки тому
Fast and Effective Exploratory Data Analysis (EDA) With Python and Pandas Profiling for Data Science
Data Quality Considerations for Petrophysical Machine Learning Models
Переглядів 1,9 тис.2 роки тому
Data Quality Considerations for Petrophysical Machine Learning Models
CSV to LAS with Python and LASIO for Well Log Data
Переглядів 3,4 тис.2 роки тому
CSV to LAS with Python and LASIO for Well Log Data
Free Well Logging & Petrophysics Datasets for Data Science and Machine Learning
Переглядів 7 тис.2 роки тому
Free Well Logging & Petrophysics Datasets for Data Science and Machine Learning
6 Essential Python Libraries for Well Log Data
Переглядів 3,2 тис.2 роки тому
6 Essential Python Libraries for Well Log Data