# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io

def process_product_image(s3_bucket, s3_key):
    # 1. Download from S3
    s3 = boto3.client('s3')
    response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
    img = Image.open(io.BytesIO(response['Body'].read()))
    
    # 2. Standardize dimensions
    img = img.convert("RGB")
    img = img.resize((1200, 1200), Image.LANCZOS)
    
    # 3. Remove background (simplified)
    # In practice: use rembg or AWS Rekognition
    img = remove_background(img)
    
    # 4. Generate variants
    variants = {
        "web": img.resize((800, 800)),
        "mobile": img.resize((400, 400)),
        "thumbnail": img.resize((100, 100))
    }
    
    # 5. Upload to CDN
    for name, variant in variants.items():
        buffer = io.BytesIO()
        variant.save(buffer, "JPEG", quality=95)
        s3.upload_fileobj(
            buffer, 
            "cdn-bucket", 
            f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
            ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
        )
    
    # 6. Generate WebP version
    webp_buffer = io.BytesIO()
    img.save(webp_buffer, "WEBP", quality=85)
    s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")

process_product_image("user-uploads", "products/summer_dress.jpg")

By: @DataScienceM 👁

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3

🤖🧠 PandasAI: Transforming Data Analysis with Conversational Artificial Intelligence

🗓️ 28 Oct 2025
📚 AI News & Trends

In a world dominated by data, the ability to analyze and interpret information efficiently has become a core competitive advantage. From business intelligence dashboards to large-scale machine learning models, data-driven decision-making fuels innovation across industries. Yet, for most people, data analysis remains a technical challenge requiring coding expertise, statistical knowledge and familiarity with libraries like ...

#PandasAI #ConversationalAI #DataAnalysis #ArtificialIntelligence #DataScience #MachineLearning

🤖🧠 Microsoft Data Formulator: Revolutionizing AI-Powered Data Visualization

🗓️ 28 Oct 2025
📚 AI News & Trends

In today’s data-driven world, visualization is everything. Whether you’re a business analyst, data scientist or researcher, the ability to convert raw data into meaningful visuals can define the success of your decisions. That’s where Microsoft’s Data Formulator steps in a cutting-edge, open-source platform designed to empower analysts to create rich, AI-assisted visualizations effortlessly. Developed by ...

#Microsoft #DataVisualization #AI #DataScience #OpenSource #Analytics

💡 Python: Simple K-Means Clustering Project

K-Means is a popular unsupervised machine learning algorithm used to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean (centroid). This simple project demonstrates K-Means on the classic Iris dataset using scikit-learn to group similar flower species based on their measurements.

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import numpy as np

# 1. Load the Iris dataset
iris = load_iris()
X = iris.data # Features (sepal length, sepal width, petal length, petal width)
y = iris.target # True labels (0, 1, 2 for different species) - not used by KMeans

# 2. (Optional but recommended) Scale the features
# K-Means is sensitive to the scale of features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 3. Define and train the K-Means model
# We know there are 3 species in Iris, so we set n_clusters=3
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10) # n_init is important for robust results
kmeans.fit(X_scaled)

# 4. Get the cluster assignments for each data point
labels = kmeans.labels_

# 5. Get the coordinates of the cluster centroids
centroids = kmeans.cluster_centers_

# 6. Visualize the clusters (using first two features for simplicity)
plt.figure(figsize=(8, 6))

# Plot each cluster
colors = ['red', 'green', 'blue']
for i in range(3):
    plt.scatter(X_scaled[labels == i, 0], X_scaled[labels == i, 1],
                s=50, c=colors[i], label=f'Cluster {i+1}', alpha=0.7)

# Plot the centroids
plt.scatter(centroids[:, 0], centroids[:, 1],
            s=200, marker='X', c='black', label='Centroids', edgecolor='white')

plt.title('K-Means Clustering on Iris Dataset (Scaled Features)')
plt.xlabel('Scaled Sepal Length')
plt.ylabel('Scaled Sepal Width')
plt.legend()
plt.grid(True)
plt.show()

# You can also compare with true labels (for evaluation, not part of clustering process itself)
# print("True labels:", y)
# print("K-Means labels:", labels)

Code explanation: This script loads the Iris dataset, scales its features using StandardScaler, and then applies KMeans to group the data into 3 clusters. It visualizes the resulting clusters and their centroids using a scatter plot with the first two scaled features.

#Python #MachineLearning #KMeans #Clustering #DataScience

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By: @DataScienceM ✨

🤖🧠 MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models

🗓️ 30 Oct 2025
📚 AI News & Trends

Machine Learning models are powerful but building them is only half the story. The true challenge lies in deploying, scaling and maintaining these models in production environments – a process that requires collaboration between data scientists, developers and operations teams. This is where MLOps (Machine Learning Operations) comes in. MLOps combines the principles of DevOps ...

#MLOps #MachineLearning #DevOps #ModelDeployment #DataScience #ProductionAI

💡 Pandas Cheatsheet

A quick guide to essential Pandas operations for data manipulation, focusing on creating, selecting, filtering, and grouping data in a DataFrame.

1. Creating a DataFrame
The primary data structure in Pandas is the DataFrame. It's often created from a dictionary.

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 32, 28],
        'City': ['New York', 'Paris', 'New York']}
df = pd.DataFrame(data)

print(df)
#       Name  Age       City
# 0    Alice   25   New York
# 1      Bob   32      Paris
# 2  Charlie   28   New York

• A dictionary is defined where keys become column names and values become the data in those columns. pd.DataFrame() converts it into a tabular structure.

2. Selecting Data with .loc and .iloc
Use .loc for label-based selection and .iloc for integer-position based selection.

# Select the first row by its integer position (0)
print(df.iloc[0])

# Select the row with index label 1 and only the 'Name' column
print(df.loc[1, 'Name'])

# Output for df.iloc[0]:
# Name       Alice
# Age           25
# City    New York
# Name: 0, dtype: object
#
# Output for df.loc[1, 'Name']:
# Bob

• .iloc[0] gets all data from the row at index position 0.
• .loc[1, 'Name'] gets the data at the intersection of index label 1 and column label 'Name'.

3. Filtering Data
Select subsets of data based on conditions.

# Select rows where Age is greater than 27
filtered_df = df[df['Age'] > 27]
print(filtered_df)
#       Name  Age       City
# 1      Bob   32      Paris
# 2  Charlie   28   New York

• The expression df['Age'] > 27 creates a boolean Series (True/False).
• Using this Series as an index df[...] returns only the rows where the value was True.

4. Grouping and Aggregating
The "group by" operation involves splitting data into groups, applying a function, and combining the results.

# Group by 'City' and calculate the mean age for each city
city_ages = df.groupby('City')['Age'].mean()
print(city_ages)
# City
# New York    26.5
# Paris       32.0
# Name: Age, dtype: float64

• .groupby('City') splits the DataFrame into groups based on unique city values.
• ['Age'].mean() then calculates the mean of the 'Age' column for each of these groups.

#Python #Pandas #DataAnalysis #DataScience #Programming

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By: @DataScienceM ✨

💡 SciPy: Scientific Computing in Python

SciPy is a fundamental library for scientific and technical computing in Python. Built on NumPy, it provides a wide range of user-friendly and efficient numerical routines for tasks like optimization, integration, linear algebra, and statistics.

import numpy as np
from scipy.optimize import minimize

# Define a function to minimize: f(x) = (x - 3)^2
def f(x):
    return (x - 3)**2

# Find the minimum of the function with an initial guess
res = minimize(f, x0=0)

print(f"Minimum found at x = {res.x[0]:.4f}")
# Output:
# Minimum found at x = 3.0000

• Optimization: scipy.optimize.minimize is used to find the minimum value of a function.
• We provide the function (f) and an initial guess (x0=0).
• The result object (res) contains the solution in the .x attribute.

from scipy.integrate import quad

# Define the function to integrate: f(x) = sin(x)
def integrand(x):
    return np.sin(x)

# Integrate sin(x) from 0 to pi
result, error = quad(integrand, 0, np.pi)

print(f"Integral result: {result:.4f}")
print(f"Estimated error: {error:.2e}")
# Output:
# Integral result: 2.0000
# Estimated error: 2.22e-14

• Numerical Integration: scipy.integrate.quad calculates the definite integral of a function over a given interval.
• It returns a tuple containing the integral result and an estimate of the absolute error.

from scipy.linalg import solve

# Solve the linear system Ax = b
# 3x + 2y = 12
#  x -  y = 1

A = np.array([[3, 2], [1, -1]])
b = np.array([12, 1])

solution = solve(A, b)
print(f"Solution (x, y): {solution}")
# Output:
# Solution (x, y): [2.8 1.8]

• Linear Algebra: scipy.linalg provides more advanced linear algebra routines than NumPy.
• solve(A, b) efficiently finds the solution vector x for a system of linear equations defined by a matrix A and a vector b.

from scipy import stats

# Create two independent samples
sample1 = np.random.normal(loc=5, scale=2, size=100)
sample2 = np.random.normal(loc=5.5, scale=2, size=100)

# Perform an independent t-test
t_stat, p_value = stats.ttest_ind(sample1, sample2)

print(f"T-statistic: {t_stat:.4f}")
print(f"P-value: {p_value:.4f}")
# Output (will vary):
# T-statistic: -1.7432
# P-value: 0.0829

• Statistics: scipy.stats is a powerful module for statistical analysis.
• ttest_ind calculates the T-test for the means of two independent samples.
• The p-value helps determine if the difference between sample means is statistically significant (a low p-value, e.g., < 0.05, suggests it is).

#SciPy #Python #DataScience #ScientificComputing #Statistics

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By: @DataScienceM ✨

#Pandas #DataAnalysis #Python #DataScience #Tutorial

Top 30 Pandas Functions & Methods

This lesson covers 30 essential Pandas functions for data manipulation and analysis, each with a standalone example and its output.

---

1. pd.DataFrame()
Creates a new DataFrame (a 2D labeled data structure) from various inputs like dictionaries or lists.

import pandas as pd
data = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data)
print(df)

col1  col2
0     1     3
1     2     4

---

2. pd.Series()
Creates a new Series (a 1D labeled array).

import pandas as pd
s = pd.Series([10, 20, 30, 40], name='MyNumbers')
print(s)

0    10
1    20
2    30
3    40
Name: MyNumbers, dtype: int64

---

3. pd.read_csv()
Reads data from a CSV file into a DataFrame. (Assuming a file data.csv exists).

# Create a dummy csv file first
with open('data.csv', 'w') as f:
    f.write('Name,Age\nAlice,25\nBob,30')

df = pd.read_csv('data.csv')
print(df)

Name  Age
0  Alice   25
1    Bob   30

---

4. df.to_csv()
Writes a DataFrame to a CSV file.

import pandas as pd
df = pd.DataFrame({'Name': ['Charlie'], 'Age': [35]})
# index=False prevents writing the DataFrame index to the file
df.to_csv('output.csv', index=False)
# You can check that 'output.csv' has been created.
print("File 'output.csv' created.")

File 'output.csv' created.

#PandasIO #DataFrame #Series

---

5. df.head()
Returns the first n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.head(3))

Name  Value
0    A      1
1    B      2
2    C      3

---

6. df.tail()
Returns the last n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.tail(2))

Name  Value
4    E      5
5    F      6

---

7. df.info()
Provides a concise summary of the DataFrame, including data types and non-null values.

import pandas as pd
import numpy as np
data = {'col1': [1, 2, 3], 'col2': [4.0, 5.0, np.nan], 'col3': ['A', 'B', 'C']}
df = pd.DataFrame(data)
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   col1    3 non-null      int64  
 1   col2    2 non-null      float64
 2   col3    3 non-null      object 
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes

---

8. df.shape
Returns a tuple representing the dimensionality (rows, columns) of the DataFrame.

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
print(df.shape)

(2, 3)

#DataInspection #PandasBasics

---

9. df.describe()
Generates descriptive statistics for numerical columns (count, mean, std, min, max, etc.).

import pandas as pd
df = pd.DataFrame({'Age': [22, 38, 26, 35, 29]})
print(df.describe())

Top 100 Data Analyst Interview Questions & Answers

#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience

Part 1: SQL Questions (Q1-30)

#1. What is the difference between DELETE, TRUNCATE, and DROP?
A:
• DELETE is a DML command that removes rows from a table based on a WHERE clause. It is slower as it logs each row deletion and can be rolled back.
• TRUNCATE is a DDL command that quickly removes all rows from a table. It is faster, cannot be rolled back, and resets table identity.
• DROP is a DDL command that removes the entire table, including its structure, data, and indexes.

#2. Select all unique departments from the employees table.
A: Use the DISTINCT keyword.

SELECT DISTINCT department
FROM employees;

#3. Find the top 5 highest-paid employees.
A: Use ORDER BY and LIMIT.

SELECT name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;

#4. What is the difference between WHERE and HAVING?
A:
• WHERE is used to filter records before any groupings are made (i.e., it operates on individual rows).
• HAVING is used to filter groups after aggregations (GROUP BY) have been performed.

-- Find departments with more than 10 employees
SELECT department, COUNT(employee_id)
FROM employees
GROUP BY department
HAVING COUNT(employee_id) > 10;

#5. What are the different types of SQL joins?
A:
• (INNER) JOIN: Returns records that have matching values in both tables.
• LEFT (OUTER) JOIN: Returns all records from the left table, and the matched records from the right table.
• RIGHT (OUTER) JOIN: Returns all records from the right table, and the matched records from the left table.
• FULL (OUTER) JOIN: Returns all records when there is a match in either the left or right table.
• SELF JOIN: A regular join, but the table is joined with itself.

#6. Write a query to find the second-highest salary.
A: Use OFFSET or a subquery.

-- Method 1: Using OFFSET
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

-- Method 2: Using a Subquery
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

#7. Find duplicate emails in a customers table.
A: Group by the email column and use HAVING to find groups with a count greater than 1.

SELECT email, COUNT(email)
FROM customers
GROUP BY email
HAVING COUNT(email) > 1;

#8. What is a primary key vs. a foreign key?
A:
• A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
• A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.

#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.

-- Rank employees by salary within each department
SELECT
    name,
    department,
    salary,
    RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees;

#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It helps improve readability and break down complex queries.

fig, ax = plt.subplots() # Single subplot
fig, axes = plt.subplots(2, 2) # 2x2 grid of subplots

• Plot on a specific subplot (Axes object).

axes[0, 0].plot(x, np.sin(x))

• Set the title for a specific subplot.

axes[0, 0].set_title('Subplot 1')

• Set labels for a specific subplot.

axes[0, 0].set_xlabel('X-axis')
axes[0, 0].set_ylabel('Y-axis')

• Add a legend to a specific subplot.

axes[0, 0].legend(['Sine'])

• Add a main title for the entire figure.

fig.suptitle('Main Figure Title')

• Automatically adjust subplot parameters for a tight layout.

plt.tight_layout()

• Share x or y axes between subplots.

fig, axes = plt.subplots(2, 1, sharex=True)

• Get the current Axes instance.

ax = plt.gca()

• Create a second y-axis that shares the x-axis.

ax2 = ax.twinx()

VI. Specialized Plots

• Create a contour plot.

X, Y = np.meshgrid(x, x)
Z = np.sin(X) * np.cos(Y)
plt.contour(X, Y, Z, levels=10)

• Create a filled contour plot.

plt.contourf(X, Y, Z)

• Create a stream plot for vector fields.

U, V = np.cos(X), np.sin(Y)
plt.streamplot(X, Y, U, V)

• Create a 3D surface plot.

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z)

#Python #Matplotlib #DataVisualization #DataScience #Plotting

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

• Group data by a column.

df.groupby('col1')

• Group by a column and get the sum.

df.groupby('col1').sum()

• Apply multiple aggregation functions at once.

df.groupby('col1').agg(['mean', 'count'])

• Get the size of each group.

df.groupby('col1').size()

• Get the frequency counts of unique values in a Series.

df['col1'].value_counts()

• Create a pivot table.

pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])

VI. Merging, Joining & Concatenating

• Merge two DataFrames (like a SQL join).

pd.merge(left_df, right_df, on='key_column')

• Concatenate (stack) DataFrames along an axis.

pd.concat([df1, df2]) # Stacks rows

• Join DataFrames on their indexes.

left_df.join(right_df, how='outer')

VII. Input & Output

• Write a DataFrame to a CSV file.

df.to_csv('output.csv', index=False)

• Write a DataFrame to an Excel file.

df.to_excel('output.xlsx', sheet_name='Sheet1')

• Read data from an Excel file.

pd.read_excel('input.xlsx', sheet_name='Sheet1')

• Read from a SQL database.

pd.read_sql_query('SELECT * FROM my_table', connection_object)

VIII. Time Series & Special Operations

• Use the string accessor (.str) for Series operations.

s.str.lower()
s.str.contains('pattern')

• Use the datetime accessor (.dt) for Series operations.

s.dt.year
s.dt.day_name()

• Create a rolling window calculation.

df['col1'].rolling(window=3).mean()

• Create a basic plot from a Series or DataFrame.

df['col1'].plot(kind='hist')

#Python #Pandas #DataAnalysis #DataScience #Programming

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By: @DataScienceM ✨

📌 NumPy for Absolute Beginners: A Project-Based Approach to Data Analysis

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-04 | ⏱️ Read time: 14 min read

Master NumPy for data analysis with this project-based guide for absolute beginners. Learn to build a high-performance sensor data pipeline from scratch and unlock the true speed of Python for data-intensive applications.

#NumPy #Python #DataAnalysis #DataScience

📌 Why Nonparametric Models Deserve a Second Look

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-11-05 | ⏱️ Read time: 7 min read

Nonparametric models offer a powerful, unified framework for regression, classification, and synthetic data generation. By leveraging nonparametric conditional distributions, these methods provide significant flexibility because they don't require pre-defining a specific functional form for the data. This adaptability makes them highly effective for capturing complex patterns and relationships that might be missed by traditional models. It's time for data professionals to reconsider the unique advantages of these assumption-free techniques for modern machine learning challenges.

#NonparametricModels #MachineLearning #DataScience #Statistics

📌 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

📌 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

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)

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By: @DataScienceM ✨

📌 Does More Data Always Yield Better Performance?

🗂 Category: DATA SCIENCE

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🗂 Category: DATA SCIENCE

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🗂 Category: AGENTIC AI

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