This article explains Pair Plots (Scatter Matrices) in Python for exploratory data analysis, showing pairwise relationships between numerical variables using scatter plots and distribution plots.
The article provides the following Python code using `seaborn` and `matplotlib` to create a pair plot:
```python
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Create some random data
data = np.random.rand(100, 4)
df = pd.DataFrame(data, columns= 'A', 'B', 'C', 'D' » )
# Create the pair plot
sns.pairplot(df)
# Show the plot
plt.show()
```
The article provides the following Python code using `seaborn` and `matplotlib` to create a pair plot:
```python
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Create some random data
data = np.random.rand(100, 4)
df = pd.DataFrame(data, columns= 'A', 'B', 'C', 'D' » )
# Create the pair plot
sns.pairplot(df)
# Show the plot
plt.show()
```
An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
"Talk to your data. Instantly analyze, visualize, and transform."
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
This tutorial compares Polars and pandas, covering syntax, performance, LazyFrames, conversions, and plotting to help you choose the right library for your data analysis needs.
The author discusses a shift in approach to clustering mixed data, advocating for starting with the simpler Gower distance metric before resorting to more complex embedding techniques like UMAP. They introduce 'Gower Express', an optimized and accelerated implementation of Gower.
XTRAN is a software meta-tool that marries compiler and expert system technologies to provide rule-driven automation of software tasks involving a wide variety of computer languages. It supports code assessment, generation, transformation, translation, data/text analysis, and more.
This article explores gamma spectroscopy using a Radiacode 103G detector and Python, detailing data collection, analysis, and experiments with various objects to identify radioactive elements.
Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
phyphox turns your smartphone into a mobile lab, allowing you to use its sensors for physics experiments. It offers data export, remote control, and the ability to create custom experiments. The project has received several teaching awards and is supported by various organizations.
A technical blog post about setting up JupyterLab and integrating it with OpenWebUI's code interpreter feature, enabling the LLM to execute and generate code for tasks such as exploratory data analysis.
