import numpy as np
quotient, remainder = np.divmod(10, 3)
print(f"Quotient: {quotient}, Remainder: {remainder}") # Output: Quotient: 3, Remainder: 1

Python tip:
Convert array elements to boolean using np.array.astype(bool).

import numpy as np
arr = np.array([0, 1, -5, 0.0, 0.1])
bool_arr = arr.astype(bool)
print(bool_arr) # Output: [False  True  True False  True]

Python tip:
Generate a sequence of values in a specific range and step for floating-point numbers using np.r_.

import numpy as np
seq = np.r_[0:10:0.5] # Start at 0, stop at 10, step 0.5
print(seq)

Python tip:
Convert a value to a NumPy array for consistent operations using np.asarray().

import numpy as np
my_list = [1, 2, 3]
arr = np.asarray(my_list)
print(arr)

Python tip:
Use np.unique(return_counts=True) to get unique elements and their frequencies.

import numpy as np
arr = np.array([1, 1, 2, 3, 2, 4, 1, 5])
unique_elements, counts = np.unique(arr, return_counts=True)
print(f"Unique elements: {unique_elements}")
print(f"Counts: {counts}")

Python tip:
Access rows and columns of 2D arrays directly by slicing.

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
first_row = matrix[0, :] # All columns of the first row
second_col = matrix[:, 1] # All rows of the second column
print(f"First row: {first_row}")
print(f"Second column: {second_col}")

Python tip:
Use np.diagflat() to create a diagonal array from a flattened input.

import numpy as np
arr = np.array([[1,2],[3,4]])
diag_matrix = np.diagflat(arr)
print(diag_matrix)

Python tip:
Get the trace of a square matrix (sum of main diagonal elements) using np.trace().

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
trace = np.trace(matrix)
print(trace) # Output: 15 (1 + 5 + 9)

Python tip:
Compute the Kronecker product of two arrays using np.kron().

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
kronecker_product = np.kron(a, b)
print(kronecker_product) # Output: [3 4 6 8]

Python tip:
Use np.linalg.matrix_power() to raise a square matrix to the (integer) power n.

import numpy as np
matrix = np.array([[1, 2], [3, 4]])
matrix_squared = np.linalg.matrix_power(matrix, 2)
print(matrix_squared)

Python tip:
Check for finite values (not NaN or Inf) using np.isfinite().

import numpy as np
arr = np.array([1, np.nan, np.inf, -np.inf, 5])
finite_arr = np.isfinite(arr)
print(finite_arr) # Output: [ True False False False  True]

Python tip:
Use np.around() to round to a specified number of decimals.

import numpy as np
val = 123.4567
rounded_val = np.around(val, decimals=2)
print(rounded_val) # Output: 123.46

Python tip:
Generate a Pascal matrix using np.pascal(). (Note: np.pascal() is not a standard NumPy function. If you need it, you'd implement it manually or use SciPy.)

import numpy as np
# This is a conceptual tip as np.pascal doesn't exist.
# You would usually implement it manually:
def pascal_matrix(n):
    matrix = np.zeros((n, n), dtype=int)
    for i in range(n):
        for j in range(n):
            if i == 0 or j == 0:
                matrix[i, j] = 1
            else:
                matrix[i, j] = matrix[i-1, j] + matrix[i, j-1]
    return matrix

print(pascal_matrix(4))

Python tip:
Use np.convolve() to compute the discrete linear convolution of two 1D arrays.

import numpy as np
a = np.array([1, 2, 3])
b = np.array([0, 1, 0.5])
convolution = np.convolve(a, b)
print(convolution) # Output: [0.  1.  2.5 4.  1.5]

Python tip:
Use np.polyval() to evaluate a polynomial at specific values.

import numpy as np
poly_coeffs = np.array([3, 0, 1]) # Represents 3x^2 + 0x + 1
x_values = np.array([0, 1, 2])
y_values = np.polyval(poly_coeffs, x_values)
print(y_values) # Output: [ 1  4 13] (3*0^2+1, 3*1^2+1, 3*2^2+1)

Python tip:
Use np.polyfit() to find the coefficients of a polynomial that best fits a set of data points.

import numpy as np
x = np.array([0, 1, 2, 3])
y = np.array([0, 0.8, 0.9, 0.1])
coefficients = np.polyfit(x, y, 2) # Fit a 2nd degree polynomial
print(coefficients)

Python tip:
Use np.clip() to limit values in an array to a specified range, as an instance method.

import numpy as np
arr = np.array([1, 10, 3, 15, 6])
clipped_arr = arr.clip(min=3, max=10)
print(clipped_arr)

Python tip:
Use np.squeeze() to remove single-dimensional entries from the shape of an array.

import numpy as np
arr = np.zeros((1, 3, 1, 4))
squeezed_arr = np.squeeze(arr) # Removes axes of length 1
print(squeezed_arr.shape) # Output: (3, 4)

Python tip:
Create a new array with an inserted axis using np.expand_dims().

import numpy as np
arr = np.array([1, 2, 3]) # Shape (3,)
expanded_arr = np.expand_dims(arr, axis=0) # Add a new axis at position 0
print(expanded_arr.shape) # Output: (1, 3)

Python tip:
Use np.ptp() (peak-to-peak) to find the range (max - min) of an array.

import numpy as np
arr = np.array([1, 5, 2, 8, 3])
peak_to_peak = np.ptp(arr)
print(peak_to_peak) # Output: 7 (8 - 1)

Python tip:
Use np.prod() to calculate the product of array elements.

import numpy as np
arr = np.array([1, 2, 3, 4])
product = np.prod(arr)
print(product) # Output: 24 (1 * 2 * 3 * 4)

Python tip:
Use np.allclose() to compare two arrays for equality within a tolerance.

import numpy as np
a = np.array([1.0, 2.0])
b = np.array([1.00000000001, 2.0])
print(np.allclose(a, b)) # Output: True

Python tip:
Use np.array_split() to split an array into N approximately equal sub-arrays.

import numpy as np
arr = np.arange(7)
split_arr = np.array_split(arr, 3) # Split into 3 parts
print(split_arr)

#NumPyTips #PythonNumericalComputing #ArrayManipulation #DataScience #MachineLearning #PythonTips #NumPyForBeginners #Vectorization #LinearAlgebra #StatisticalAnalysis

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

🤖🧠 DeepAgent: A New Era of General AI Reasoning and Scalable Tool-Use Intelligence

🗓️ 09 Nov 2025
📚 AI News & Trends

Artificial intelligence has rapidly progressed from simple assistants to advanced reasoning systems capable of complex problem-solving. As tasks demand more autonomy, adaptability and real-world interaction, the AI field has entered the era of intelligent agent systems. These agents are expected not just to answer questions, but to think, plan, search, act and interact across digital ...

#GeneralAI #ArtificialIntelligence #AIReasoning #IntelligentAgents #ScalableAI #ToolUseAI

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🗓️ 09 Nov 2025
📚 AI News & Trends

In the modern information era, the ability to research fast, accurately and at scale has become a competitive advantage for businesses, researchers, analysts and developers. As online data expands exponentially, traditional search engines and manual research workflows are no longer sufficient to gather reliable insights efficiently. This need has fueled the rise of AI research ...

#AIResearch #DeepResearch #WebIntelligence #ArtificialIntelligence #ResearchAutomation #DataAnalysis

🤖🧠 Pico-Banana-400K: The Breakthrough Dataset Advancing Text-Guided Image Editing

🗓️ 09 Nov 2025
📚 AI News & Trends

Text-guided image editing has rapidly evolved with powerful multimodal models capable of transforming images using simple natural-language instructions. These models can change object colors, modify lighting, add accessories, adjust backgrounds or even convert real photographs into artistic styles. However, the progress of research has been limited by one crucial bottleneck: the lack of large-scale, high-quality, ...

#TextGuidedEditing #MultimodalAI #ImageEditing #AIResearch #ComputerVision #DeepLearning

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🗓️ 09 Nov 2025
📚 AI News & Trends

The world of artificial intelligence is rapidly evolving and self-supervised learning has become a driving force behind breakthroughs in computer vision and 3D scene understanding. Traditional supervised learning relies heavily on labeled datasets which are expensive and time-consuming to produce. Self-supervised learning, on the other hand, extracts meaningful patterns without manual labels allowing models to ...

#SelfSupervisedLearning #ComputerVision #3DSceneUnderstanding #SpatialIntelligence #AIResearch #DeepLearning

📌 Data Culture Is the Symptom, Not the Solution

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-10 | ⏱️ Read time: 22 min read

Your data investments are failing, and the common advice to "build a better data culture" might be wrong. This article posits that a flawed data culture is not the root cause but a symptom of a more fundamental, often overlooked issue. To achieve real ROI from data initiatives, organizations must look beyond cultural fixes and address the hidden problems that truly undermine success.

#DataCulture #DataStrategy #DataAnalytics #DataROI

📌 Make Python Up to 150× Faster with C

🗂 Category: PROGRAMMING

🕒 Date: 2025-11-10 | ⏱️ Read time: 14 min read

Dramatically accelerate your Python applications—up to 150x faster—by strategically offloading performance-critical code to C. This practical guide shows how to seamlessly integrate C with your existing Python projects, supercharging your code's bottlenecks without abandoning the Python ecosystem. Achieve significant performance gains where they matter most.

#Python #CProgramming #PerformanceOptimization #Coding

📌 Why Storytelling With Data Matters for Business and Data Analysts

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-10 | ⏱️ Read time: 7 min read

Data is the engine of modern business, but raw information alone doesn't drive action. The key is data storytelling: the art of weaving complex data into a compelling narrative. For data analysts and business leaders, this skill is no longer optional; it's essential for translating insights into clear, actionable strategies. Mastering data storytelling is crucial for harnessing the true power of information and shaping the future of any organization.

#DataStorytelling #DataAnalysis #BusinessIntelligence #DataViz

📌 Does More Data Always Yield Better Performance?

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-10 | ⏱️ Read time: 9 min read

Exploring and challenging the conventional wisdom of “more data → better performance” by experimenting with…

#DataScience #AI #Python

📌 Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-11 | ⏱️ Read time: 15 min read

Go beyond the hype with this practitioner's guide to GraphRAG. This article offers a critical perspective on the advanced RAG technique, exploring essential design best practices, common challenges, and key learnings from real-world implementation. It provides a framework to help you decide if GraphRAG is the right solution for your specific needs, moving past the buzz to focus on practical application.

#GraphRAG #RAG #AI #KnowledgeGraphs #LLM

🤖🧠 The Transformer Architecture: How Attention Revolutionized Deep Learning

🗓️ 11 Nov 2025
📚 AI News & Trends

The field of artificial intelligence has witnessed a remarkable evolution and at the heart of this transformation lies the Transformer architecture. Introduced by Vaswani et al. in 2017, the paper “Attention Is All You Need” redefined the foundations of natural language processing (NLP) and sequence modeling. Unlike its predecessors – recurrent and convolutional neural networks, ...

#TransformerArchitecture #AttentionMechanism #DeepLearning #NaturalLanguageProcessing #NLP #AIResearch

🤖🧠 The Transformer Architecture: How Attention Revolutionized Deep Learning

🗓️ 11 Nov 2025
📚 AI News & Trends

The field of artificial intelligence has witnessed a remarkable evolution and at the heart of this transformation lies the Transformer architecture. Introduced by Vaswani et al. in 2017, the paper “Attention Is All You Need” redefined the foundations of natural language processing (NLP) and sequence modeling. Unlike its predecessors – recurrent and convolutional neural networks, ...

#TransformerArchitecture #AttentionMechanism #DeepLearning #NaturalLanguageProcessing #NLP #AIResearch

🤖🧠 BERT: Revolutionizing Natural Language Processing with Bidirectional Transformers

🗓️ 11 Nov 2025
📚 AI News & Trends

In the ever-evolving landscape of artificial intelligence and natural language processing (NLP), BERT (Bidirectional Encoder Representations from Transformers) stands as a monumental breakthrough. Developed by researchers at Google AI in 2018, BERT introduced a new way of understanding the context of language by using deep bidirectional training of the Transformer architecture. Unlike previous models that ...

#BERT #NaturalLanguageProcessing #TransformerArchitecture #BidirectionalLearning #DeepLearning #AIStrategy

🤖🧠 vLLM Semantic Router: The Next Frontier in Intelligent Model Routing for LLMs

🗓️ 11 Nov 2025
📚 AI News & Trends

As large language models (LLMs) continue to evolve, organizations face new challenges in optimizing performance, accuracy and cost across various AI workloads. Running multiple models efficiently – each specialized for specific tasks has become essential for scalable AI deployment. Enter vLLM Semantic Router, an open-source innovation that introduces a new layer of intelligence to the ...

#vLLMSemanticRouter #LargeLanguageModels #AIScaling #ModelRouting #OpenSourceAI #LLMOptimization

🤖🧠 Plandex AI: The Future of Autonomous Coding Agents for Large-Scale Development

🗓️ 11 Nov 2025
📚 AI News & Trends

As software development becomes increasingly complex, developers are turning to AI tools that can manage, understand and automate large portions of the coding workflow. Among the most promising innovations in this space is Plandex AI, an open-source terminal-based coding agent designed for real-world, large-scale projects. Unlike simple AI coding assistants that handle small snippets, Plandex ...

#PlandexAI #AutonomousCoding #LargeScaleDevelopment #AICoding #OpenSourceAI #CodeAutomation

📌 The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example)

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-11 | ⏱️ Read time: 10 min read

This article charts the evolution of the data scientist's role through three distinct eras: traditional machine learning, deep learning, and the current age of large language models (LLMs). Using a single, practical use case, it illustrates how the approach to problem-solving has shifted with each technological generation. The piece serves as a guide for practitioners, clarifying when to leverage classic algorithms, complex neural networks, or the latest foundation models, helping them select the most appropriate tool for the task at hand.

#DataScience #MachineLearning #DeepLearning #LLM

📌 How to Build Agents with GPT-5

🗂 Category: AGENTIC AI

🕒 Date: 2025-11-11 | ⏱️ Read time: 8 min read

Learn how to use GPT-5 as a powerful AI Agent on your data.

#DataScience #AI #Python

📌 AI Hype: Don’t Overestimate the Impact of AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-11-11 | ⏱️ Read time: 7 min read

The current wave of AI hype is leading to an overestimation of its impact. The tech industry is often guilty of targeting futuristic "moonshots" while ignoring the immediate, practical, and ethical "trolley problems" that require solutions now. This perspective calls for a crucial shift in focus from speculative, long-term goals to solving the tangible challenges of today. Prioritizing real-world applications and ethical frameworks over grand ambitions is essential for building a responsible and genuinely valuable AI foundation.

#AIHype #ResponsibleAI #AIStrategy #AIEthics

🤖🧠 Nanobrowser: The Open-Source AI Web Automation Tool Changing How We Browse

🗓️ 12 Nov 2025
📚 AI News & Trends

The rise of artificial intelligence has redefined how we interact with the web, transforming routine browsing into a space for automation and productivity. Among the most exciting innovations in this field is Nanobrowser, an open-source AI-powered web automation tool designed to run directly inside your browser. Developed as a free alternative to OpenAI Operator, Nanobrowser ...

#Nanobrowser #AIWebAutomation #OpenSourceTools #BrowserAI #ProductivityTech #AIAutomation

🤖🧠 Nanobrowser: The Open-Source AI Web Automation Tool Changing How We Browse

🗓️ 12 Nov 2025
📚 AI News & Trends

The rise of artificial intelligence has redefined how we interact with the web, transforming routine browsing into a space for automation and productivity. Among the most exciting innovations in this field is Nanobrowser, an open-source AI-powered web automation tool designed to run directly inside your browser. Developed as a free alternative to OpenAI Operator, Nanobrowser ...

#Nanobrowser #AIWebAutomation #OpenSourceTools #BrowserAI #ProductivityTech #AIAutomation