📌 Evaluating Synthetic Data — The Million Dollar Question

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-07 | ⏱️ Read time: 13 min read

How can you trust your synthetic data? Answering this "million dollar question" is crucial for any AI/ML project. This article details a straightforward method for evaluating synthetic data quality: the Maximum Similarity Test. Learn how this simple test can help you measure how well your generated data mirrors real-world information, building confidence in your models and ensuring the reliability of your results.

#SyntheticData #DataScience #MachineLearning #DataQuality

📌 Power Analysis in Marketing: A Hands-On Introduction

🗂 Category: STATISTICS

🕒 Date: 2025-11-08 | ⏱️ Read time: 18 min read

Dive into the fundamentals of power analysis for marketing. This hands-on introduction demystifies statistical power, explaining what it is and demonstrating how to compute it. Understand why power is crucial for reliable A/B testing and campaign analysis, and learn to strengthen your experimental design. This is the first part of a practical series for data-driven professionals.

#PowerAnalysis #MarketingAnalytics #DataScience #Statistics

🤖🧠 Kimi Linear: The Future of Efficient Attention in Large Language Models

🗓️ 08 Nov 2025
📚 AI News & Trends

The rapid evolution of large language models (LLMs) has unlocked new capabilities in natural language understanding, reasoning, coding and multimodal tasks. However, as models grow more advanced, one major challenge persists: computational efficiency. Traditional full-attention architectures struggle to scale efficiently, especially when handling long context windows and real-time inference workloads. The increasing demand for agent-like ...

#KimiLinear #EfficientAttention #LargeLanguageModels #LLM #ComputationalEfficiency #AIInnovation

🤖🧠 Meilisearch: The Lightning-Fast, AI-Ready Search Engine for Modern Applications

🗓️ 08 Nov 2025
📚 AI News & Trends

Search is no longer a luxury feature. Today’s users expect instant, relevant results across e-commerce platforms, SaaS tools, media libraries and knowledge systems. With AI-powered experiences becoming the new standard, developers need search infrastructure that is fast, flexible, developer-friendly and ready for hybrid semantic search. This is where Meilisearch stands out. Meilisearch is an open-source, ...

#Meilisearch #AIReadySearch #LightningFast #SearchEngine #ModernApplications #OpenSource

🤖🧠 Pixeltable: The Future of Declarative Data Infrastructure for Multimodal AI Workloads

🗓️ 08 Nov 2025
📚 AI News & Trends

In the rapidly evolving AI landscape, building intelligent applications is no longer just about having powerful models. The real challenge lies in handling complex data pipelines, integrating multiple systems and scaling multimodal workloads efficiently. Traditional AI app development stacks involve databases, vector stores, ETL pipelines, model serving layers, orchestration tools, caching systems and lineage tracking ...

#Pixeltable #DeclarativeDataInfrastructure #MultimodalAI #AIDevelopment #DataPipelines #AIWorkloads

🤖🧠 Chandra OCR: The Future of Document Understanding and Layout-Aware Text Extraction

🗓️ 08 Nov 2025
📚 AI News & Trends

Optical Character Recognition (OCR) has evolved far beyond simply converting scanned text into digital characters. With the rise of artificial intelligence and large language models, the industry is shifting toward intelligent document understanding where structure, context and visual elements matter as much as the text itself. In this landscape, Chandra emerges as a breakthrough solution. ...

#ChandraOCR #DocumentUnderstanding #LayoutAwareText #OpticalCharacterRecognition #AIDocumentProcessing #IntelligentOCR

🤖🧠 LMCache: Accelerating LLM Inference With Next-Generation KV Cache Technology

🗓️ 08 Nov 2025
📚 AI News & Trends

As large language models (LLMs) continue to scale in size and complexity, organizations face an increasingly critical challenge: serving models efficiently in real-world applications. While LLM capabilities are rapidly evolving, the bottleneck of inference performance remains a major limitation especially when dealing with long-context workloads or high-traffic enterprise environments. This is where LMCache steps in. ...

#LMCache #LLMInference #KVCache #LargeLanguageModels #AIAcceleration #NextGenTechnology

🤖🧠 Dify: A Powerful #1 Production-Ready Platform for Building Advanced LLM Applications

🗓️ 08 Nov 2025
📚 AI News & Trends

The rapid growth of AI has made large language models (LLMs) an essential component for automation, content creation, data intelligence and workflow optimization. But moving AI concepts from prototype to production has traditionally required significant engineering effort, infrastructure planning and model-orchestration expertise. Dify changes that entirely. Dify is an open-source platform designed to help developers, ...

#Dify #LLMApplications #ProductionReady #AIPower #LargeLanguageModels #OpenSourcePlatform

🤖🧠 Open WebUI: The Most Powerful Self-Hosted AI Platform for Local and Private LLMs

🗓️ 09 Nov 2025
📚 AI News & Trends

In the rapidly evolving landscape of artificial intelligence, the ability to run large language models securely and efficiently has become a major priority for developers, enterprises and privacy-focused users. While cloud-based AI services are convenient, they rely heavily on remote servers, internet access and third-party control. This is where Open WebUI stands out as a ...

#OpenWebUI #SelfHostedAI #PrivateLLMs #LocalAI #AISecurity #OpenSourcePlatform

🤖🧠 Generative AI for Beginners: A Complete Guide to Microsoft’s Free Course

🗓️ 09 Nov 2025
📚 AI News & Trends

Generative AI has rapidly shifted from an emerging technology to a foundation of modern digital innovation. From automated writing assistants and AI chatbots to image generators and intelligent search engines, generative AI is transforming industries and shaping the future of work. Whether you are a student, a budding developer or a technology enthusiast, learning generative ...

#GenerativeAI #BeginnersGuide #MicrosoftAI #FreeCourse #AIEducation #DigitalInnovation

🤖🧠 Generative AI for Beginners: A Complete Guide to Microsoft’s Free Course

🗓️ 09 Nov 2025
📚 AI News & Trends

Generative AI has rapidly shifted from an emerging technology to a foundation of modern digital innovation. From automated writing assistants and AI chatbots to image generators and intelligent search engines, generative AI is transforming industries and shaping the future of work. Whether you are a student, a budding developer or a technology enthusiast, learning generative ...

#GenerativeAI #BeginnersGuide #MicrosoftAI #FreeCourse #AIEducation #DigitalInnovation

📌 LLM-Powered Time-Series Analysis

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-09 | ⏱️ Read time: 9 min read

Explore the next frontier of time-series analysis by leveraging the power of Large Language Models. This article, the second in a series, delves into practical prompting strategies for advanced model development. Learn how to effectively guide LLMs to build more sophisticated and accurate forecasting and analysis solutions, moving beyond basic applications to unlock new capabilities in this critical data science domain.

#LLMs #TimeSeriesAnalysis #PromptEngineering #DataScience #AI

📌 How to Build Your Own Agentic AI System Using CrewAI

🗂 Category: AGENTIC AI

🕒 Date: 2025-11-09 | ⏱️ Read time: 12 min read

This article provides a step-by-step guide on developing a custom Agentic AI system using the CrewAI framework. Discover how to define roles, tasks, and tools for multiple AI agents, enabling them to work together autonomously to solve complex problems. This tutorial is ideal for developers looking to build sophisticated, multi-agent AI applications and explore the future of autonomous systems.

#CrewAI #AgenticAI #AIAgents #AIdevelopment

🏆 Python Pillow: Your Image Journey Begins

📢 Unlock Python image processing! Learn to open and display images effortlessly with the powerful Pillow library.

⚡ Tap to unlock the complete answer and gain instant insight.

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

100 essential NumPy tips for beginners

Python tip:
Import NumPy with the standard alias np for convenience.

import numpy as np
print(np.__version__)

Python tip:
Create a NumPy array from a Python list or tuple using np.array().

import numpy as np
my_list = [1, 2, 3, 4, 5]
arr = np.array(my_list)
print(arr)

Python tip:
Initialize an array filled with zeros using np.zeros(), specifying the shape.

import numpy as np
zeros_array = np.zeros((3, 4)) # 3 rows, 4 columns
print(zeros_array)

Python tip:
Initialize an array filled with ones using np.ones(), specifying the shape and optionally dtype.

import numpy as np
ones_array = np.ones((2, 3), dtype=int)
print(ones_array)

Python tip:
Create an empty array with np.empty(). Its initial content is random and depends on memory.

import numpy as np
empty_array = np.empty((2, 2))
print(empty_array)

Python tip:
Generate a sequence of numbers with np.arange(), similar to Python's range().

import numpy as np
sequence = np.arange(0, 10, 2) # start, stop (exclusive), step
print(sequence)

Python tip:
Generate evenly spaced numbers over a specified interval using np.linspace().

import numpy as np
evenly_spaced = np.linspace(0, 10, 5) # start, stop (inclusive), number of samples
print(evenly_spaced)

Python tip:
Create an array filled with a specific constant value using np.full().

import numpy as np
full_array = np.full((2, 2), 7)
print(full_array)

Python tip:
Create an identity matrix (square matrix with ones on the main diagonal) using np.eye() or np.identity().

import numpy as np
identity_matrix = np.eye(3) # 3x3 identity matrix
print(identity_matrix)

Python tip:
Create a new array of zeros with the same shape and data type as an existing array using np.zeros_like().

import numpy as np
original_array = np.array([[1, 2], [3, 4]])
like_zeros = np.zeros_like(original_array)
print(like_zeros)

Python tip:
Check the shape (dimensions) of an array using the .shape attribute.

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape) # Output: (2, 3)

Python tip:
Check the number of dimensions of an array using the .ndim attribute.

import numpy as np
arr_1d = np.array([1, 2, 3])
arr_2d = np.array([[1, 2], [3, 4]])
print(arr_1d.ndim) # Output: 1
print(arr_2d.ndim) # Output: 2

Python tip:
Get the total number of elements in an array using the .size attribute.

import numpy as np
arr = np.zeros((3, 4))
print(arr.size) # Output: 12 (3 * 4)

Python tip:
Determine the data type of elements in an array using the .dtype attribute.

import numpy as np
arr_int = np.array([1, 2, 3])
arr_float = np.array([1.0, 2.0])
print(arr_int.dtype)   # Output: int64 (or int32 depending on system)
print(arr_float.dtype) # Output: float64

Python tip:
Specify the data type when creating an array for memory efficiency or specific operations.

import numpy as np
arr = np.array([1, 2, 3], dtype=np.int8)
print(arr.dtype)

Python tip:
Access individual elements using square brackets [] with zero-based indexing.

import numpy as np
arr = np.array([10, 20, 30, 40])
print(arr[0])  # Output: 10
print(arr[2])  # Output: 30

Python tip:
For 2D arrays, use [row_index, col_index] to access elements.

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix[0, 0]) # Output: 1
print(matrix[1, 2]) # Output: 6

Python tip:
Slice arrays to extract sub-arrays using [start:stop:step]. stop is exclusive.

import numpy as np
arr = np.arange(10) # [0, 1, ..., 9]
slice_arr = arr[2:7:2] # elements from index 2 to 6, with step 2
print(slice_arr) # Output: [2, 4, 6]

Python tip:
Slice rows and columns in 2D arrays: [row_slice, col_slice].

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
sub_matrix = matrix[0:2, 1:3] # rows 0 and 1, columns 1 and 2
print(sub_matrix)

Python tip:
Use -1 for negative indexing to access elements from the end of the array.

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[-1]) # Output: 50 (last element)
print(arr[-3:]) # Output: [30, 40, 50] (last three elements)

Python tip:
Reshape an array to a new shape using the .reshape() method. The new shape must have the same total number of elements.

import numpy as np
arr = np.arange(12) # 12 elements
reshaped_arr = arr.reshape(3, 4) # 3 rows, 4 columns
print(reshaped_arr)

Python tip:
Use -1 in reshape() to let NumPy automatically calculate one dimension.

import numpy as np
arr = np.arange(12)
reshaped_arr = arr.reshape(3, -1) # 3 rows, NumPy calculates 4 columns
print(reshaped_arr)

Python tip:
Flatten an array into a 1D array using .ravel() or .flatten(). .ravel() returns a view where possible, .flatten() always returns a copy.

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6]])
flat_arr = matrix.ravel()
print(flat_arr)

Python tip:
Convert an array to a different data type using .astype().

import numpy as np
arr_float = np.array([1.5, 2.7, 3.1])
arr_int = arr_float.astype(int)
print(arr_int) # Output: [1 2 3]

Python tip:
Perform element-wise arithmetic operations directly on arrays.

import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b) # Addition
print(a * b) # Multiplication

Python tip:
Multiply arrays element-wise, not matrix multiplication, using *.

import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
print(a * b) # Element-wise product

Python tip:
Perform matrix multiplication using the @ operator (Python 3.5+) or np.dot().

import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
matrix_product = a @ b # or np.dot(a, b)
print(matrix_product)

Python tip:
Broadcasting allows operations on arrays of different shapes if compatible.

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
scalar = 10
print(arr + scalar) # Scalar is broadcast to all elements

Python tip:
Broadcasting also works between arrays with compatible dimensions.

import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]]) # shape (2, 3)
b = np.array([10, 20, 30])           # shape (3,)
print(a + b) # b is broadcast across rows of a

Python tip:
Use np.newaxis (or None) to add a new dimension for broadcasting.

import numpy as np
vec = np.array([1, 2, 3]) # shape (3,)
col_vec = vec[:, np.newaxis] # shape (3, 1)
print(col_vec)

Python tip:
Concatenate arrays along an existing axis using np.concatenate().

import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
c = np.concatenate((a, b), axis=0) # Concatenate vertically
print(c)

Python tip:
Stack arrays vertically (row-wise) using np.vstack() or np.row_stack().

import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
stacked_vert = np.vstack((a, b))
print(stacked_vert)

Python tip:
Stack arrays horizontally (column-wise) using np.hstack() or np.column_stack().

import numpy as np
a = np.array([[1], [2]])
b = np.array([[3], [4]])
stacked_horz = np.hstack((a, b))
print(stacked_horz)

Python tip:
Split an array into multiple sub-arrays using np.split().

import numpy as np
arr = np.arange(12).reshape(2, 6)
split_arr = np.split(arr, 3, axis=1) # Split into 3 arrays along column axis
print(split_arr)

Python tip:
Use np.where() for conditional element selection, similar to a ternary operator.

import numpy as np
arr = np.array([1, 5, 2, 8, 3])
result = np.where(arr > 4, arr * 10, arr) # if > 4, multiply by 10, else keep original
print(result) # Output: [ 1 50  2 80  3]

Python tip:
Calculate the sum of all elements in an array using np.sum().

import numpy as np
arr = np.array([[1, 2], [3, 4]])
total_sum = np.sum(arr)
print(total_sum) # Output: 10

Python tip:
Calculate the sum along a specific axis (axis=0 for columns, axis=1 for rows).

import numpy as np
arr = np.array([[1, 2], [3, 4]])
sum_rows = np.sum(arr, axis=1) # Sum of each row
sum_cols = np.sum(arr, axis=0) # Sum of each column
print(f"Row sums: {sum_rows}")
print(f"Column sums: {sum_cols}")

Python tip:
Find the minimum and maximum values in an array using np.min() and np.max().

import numpy as np
arr = np.array([1, 5, 2, 8, 3])
print(np.min(arr)) # Output: 1
print(np.max(arr)) # Output: 8

Python tip:
Find the index of the minimum/maximum value using np.argmin() and np.argmax().

import numpy as np
arr = np.array([1, 5, 2, 8, 3])
print(np.argmin(arr)) # Output: 0 (index of 1)
print(np.argmax(arr)) # Output: 3 (index of 8)

Python tip:
Calculate the mean (average) of array elements using np.mean().

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.mean(arr)) # Output: 3.0

Python tip:
Calculate the median of array elements using np.median().

import numpy as np
arr = np.array([1, 5, 2, 8, 3])
print(np.median(arr)) # Output: 3.0

Python tip:
Calculate the standard deviation using np.std().

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.std(arr))

Python tip:
Calculate the variance using np.var().

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.var(arr))

Python tip:
Sort an array using np.sort(). It returns a sorted copy, leaving the original array unchanged.

import numpy as np
arr = np.array([3, 1, 4, 1, 5, 9, 2])
sorted_arr = np.sort(arr)
print(sorted_arr)
print(arr) # Original array is unchanged

Python tip:
Sort an array in-place using the .sort() method.

import numpy as np
arr = np.array([3, 1, 4, 1, 5, 9, 2])
arr.sort() # Sorts the array itself
print(arr)

Python tip:
Get the unique elements of an array using np.unique().

import numpy as np
arr = np.array([1, 1, 2, 3, 2, 4, 1, 5])
unique_elements = np.unique(arr)
print(unique_elements) # Output: [1 2 3 4 5]

Python tip:
Generate random floating-point numbers in [0.0, 1.0) using np.random.rand().

import numpy as np
random_floats = np.random.rand(3, 2) # 3 rows, 2 columns
print(random_floats)

Python tip:
Generate random integers within a specified range using np.random.randint().

import numpy as np
random_ints = np.random.randint(0, 10, size=(2, 3)) # integers between 0 (inclusive) and 10 (exclusive)
print(random_ints)

Python tip:
Set a random seed using np.random.seed() for reproducible results.

import numpy as np
np.random.seed(42)
print(np.random.rand(2))
np.random.seed(42) # Resetting seed
print(np.random.rand(2)) # Same output as above

Python tip:
Create a deep copy of an array using .copy() to avoid unintended modifications.

import numpy as np
original = np.array([1, 2, 3])
copy = original.copy()
copy[0] = 100
print(original) # Output: [1 2 3] (original is unchanged)
print(copy)    # Output: [100 2 3]

Python tip:
Using arr[index] for assignment modifies the original array (views, not copies, for slices).

import numpy as np
original = np.arange(5)
view = original[1:4]
view[0] = 99 # Modifies original[1]
print(original) # Output: [ 0 99  2  3  4]

Python tip:
Compare arrays element-wise using comparison operators, resulting in a boolean array.

import numpy as np
a = np.array([1, 2, 3])
b = np.array([1, 0, 4])
print(a == b) # Output: [ True False False]
print(a > b)  # Output: [False  True False]

Python tip:
Use boolean indexing to select elements based on a condition.

import numpy as np
arr = np.array([10, 25, 5, 30, 15])
filtered_arr = arr[arr > 20] # Select elements greater than 20
print(filtered_arr) # Output: [25 30]

Python tip:
Combine multiple boolean conditions using & (AND), | (OR), and ~ (NOT) for element-wise logical operations.

import numpy as np
arr = np.array([1, 6, 2, 7, 3, 8])
filtered = arr[(arr > 3) & (arr < 8)] # Elements greater than 3 AND less than 8
print(filtered) # Output: [6 7]

Python tip:
Use np.clip() to limit the values in an array to a specified range.

import numpy as np
arr = np.array([1, 10, 3, 15, 6])
clipped_arr = np.clip(arr, 3, 10) # Values below 3 become 3, above 10 become 10
print(clipped_arr) # Output: [ 3 10  3 10  6]

Python tip:
Round array elements to the nearest integer using np.round().

import numpy as np
arr = np.array([1.2, 2.7, 3.5, 4.1])
rounded_arr = np.round(arr)
print(rounded_arr) # Output: [1. 3. 4. 4.]

Python tip:
Use np.floor() to round down to the nearest integer.

import numpy as np
arr = np.array([1.2, 2.7, 3.5, 4.9])
floored_arr = np.floor(arr)
print(floored_arr) # Output: [1. 2. 3. 4.]

Python tip:
Use np.ceil() to round up to the nearest integer.

import numpy as np
arr = np.array([1.2, 2.7, 3.5, 4.1])
ceiled_arr = np.ceil(arr)
print(ceiled_arr) # Output: [2. 3. 4. 5.]

Python tip:
Calculate the absolute value of each element using np.abs().

import numpy as np
arr = np.array([-1, 5, -3, 0])
abs_arr = np.abs(arr)
print(abs_arr) # Output: [1 5 3 0]

Python tip:
Transpose a 2D array using the .T attribute or np.transpose().

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6]])
transposed_matrix = matrix.T
print(transposed_matrix)

Python tip:
Use np.isin() to check if elements of one array are present in another.

import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array([2, 4, 6])
presence = np.isin(arr1, arr2)
print(presence) # Output: [False  True False  True False]

Python tip:
Use np.diff() to calculate the difference between consecutive elements.

import numpy as np
arr = np.array([1, 3, 7, 12, 10])
differences = np.diff(arr)
print(differences) # Output: [ 2  4  5 -2]

Python tip:
Load data from a text file into a NumPy array using np.loadtxt().

import numpy as np
# Create a dummy file for the example
with open("data.txt", "w") as f:
    f.write("1 2 3\n")
    f.write("4 5 6\n")

data = np.loadtxt("data.txt")
print(data)

Python tip:
Save a NumPy array to a text file using np.savetxt().

import numpy as np
arr = np.array([[10, 20], [30, 40]])
np.savetxt("output.txt", arr, fmt="%d", delimiter=",")
# Check output.txt, it will contain:
# 10,20
# 30,40

Python tip:
Save and load arrays in NumPy's native binary format (.npy) using np.save() and np.load().

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.save("my_array.npy", arr)
loaded_arr = np.load("my_array.npy")
print(loaded_arr)

Python tip:
Handle missing data using np.nan (Not a Number).

import numpy as np
arr_with_nan = np.array([1, 2, np.nan, 4])
print(arr_with_nan)

Python tip:
Check for NaN values using np.isnan().

import numpy as np
arr = np.array([1, 2, np.nan, 4])
is_nan_arr = np.isnan(arr)
print(is_nan_arr) # Output: [False False  True False]

Python tip:
Use np.inf for infinity.

import numpy as np
result = 10 / np.inf
print(result) # Output: 0.0

Python tip:
Check for infinite values using np.isinf().

import numpy as np
arr = np.array([1, np.inf, -np.inf, 4])
is_inf_arr = np.isinf(arr)
print(is_inf_arr) # Output: [False  True  True False]

Python tip:
Replace NaN values using boolean indexing or np.nan_to_num().

import numpy as np
arr = np.array([1, 2, np.nan, 4, np.nan])
arr[np.isnan(arr)] = 0 # Replace NaN with 0
print(arr)

Python tip:
Check if all elements in a boolean array are true using np.all().

import numpy as np
bool_arr = np.array([True, True, True])
print(np.all(bool_arr)) # Output: True

Python tip:
Check if any element in a boolean array is true using np.any().

import numpy as np
bool_arr = np.array([False, False, True])
print(np.any(bool_arr)) # Output: True

Python tip:
Get the dimensions of an array by unpacking .shape.

import numpy as np
matrix = np.zeros((2, 3, 4))
d1, d2, d3 = matrix.shape
print(f"Dim 1: {d1}, Dim 2: {d2}, Dim 3: {d3}")

Python tip:
Use np.transpose() with a tuple of axes to reorder dimensions for multi-dimensional arrays.

import numpy as np
arr_3d = np.arange(24).reshape(2, 3, 4)
# Swap axis 0 and 2
transposed_arr = np.transpose(arr_3d, (2, 1, 0)) # (4, 3, 2)
print(transposed_arr.shape)

Python tip:
Generate a diagonal array from a 1D array or extract the diagonal from a 2D array using np.diag().

import numpy as np
arr_1d = np.array([1, 2, 3])
diag_matrix = np.diag(arr_1d)
print(diag_matrix)

Python tip:
Repeat elements of an array using np.repeat().

import numpy as np
arr = np.array([1, 2])
repeated_arr = np.repeat(arr, 3) # Each element repeated 3 times
print(repeated_arr) # Output: [1 1 1 2 2 2]

Python tip:
Repeat an entire array or tile it using np.tile().

import numpy as np
arr = np.array([[1, 2], [3, 4]])
tiled_arr = np.tile(arr, (2, 1)) # Repeat 2 times vertically, 1 time horizontally
print(tiled_arr)

Python tip:
Dot product of two 1D arrays or matrix multiplication of 2D arrays with np.dot().

import numpy as np
v1 = np.array([1, 2])
v2 = np.array([3, 4])
dot_product = np.dot(v1, v2)
print(dot_product) # Output: 11 (1*3 + 2*4)

Python tip:
Compute the outer product of two vectors using np.outer().

import numpy as np
v1 = np.array([1, 2])
v2 = np.array([3, 4, 5])
outer_product = np.outer(v1, v2)
print(outer_product)

Python tip:
Calculate the inverse of a matrix using np.linalg.inv().

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

Python tip:
Calculate the determinant of a matrix using np.linalg.det().