Data Analysis with Python: Sorting, Filtering, and Restructuring Arrays

𝐃𝐚𝐲 13 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today was about slicing and extracting meaning from structured data using NumPy. ✔️ Created arrays from lists and transposed them in a single step ✔️ Used indexing and slicing to extract specific rows and subarrays ✔️ Retrieved individual data points while preserving correct data types ✔️ Isolated related attributes (age and gender) using slicing ✔️ Extracted a single feature for visualization ✔️ Plotted a histogram from a sliced array with proper labels Key takeaway: slicing is what turns raw arrays into focused insights and clear visualizations. Day 13 complete. Building fluency one operation at a time. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

𝐃𝐚𝐲 16 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s focus was on Pandas Series and basic DataFrame creation, exploring how Series can simplify analysis and preparation of data. ✔️ Created a Pandas Series and counted the occurrences of each item ✔️ Checked for the presence of specific values in the Series ✔️ Extracted all unique values from the Series ✔️ Updated the Series by inserting new items at specific indices ✔️ Converted the Series into a DataFrame and inspected its shape and dimensions Key takeaway: Pandas Series provide a flexible structure for handling labeled data, and converting them to DataFrames allows for more advanced analysis. Day 16 complete. Building fluency with Pandas step by step. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

𝐃𝐚𝐲 14 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s focus was on analyzing and extracting insights directly from arrays using NumPy. ✔️ Combined multiple lists into a single structured NumPy array ✔️ Filtered names and scores using conditional logic ✔️ Queried specific student records by name ✔️ Identified the highest score and the student who achieved it ✔️ Determined the longest name in the dataset and its index Key takeaway: NumPy alone is powerful enough to perform meaningful data analysis when you understand indexing, slicing, and Boolean logic. Day 14 complete. Onward with clarity and discipline. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

The Modern Developer's Power Couple: Data Science & Free Hosting! 🚀 If you are looking to build and deploy data-driven applications in 2026, these two infographics cover the essentials: The Python Data Science Stack: From the foundations of NumPy and Pandas to advanced AI with PyTorch and TensorFlow. The Deployment Layer: 20 incredible platforms like Vercel, Netlify, and Railway that let you host your projects for free. Whether you're performing complex EDA with Seaborn or deploying a fast API on Render, the barrier to entry has never been lower. Which hosting platform is your favorite for side projects? 👇 #Python #DataScience #WebDevelopment #MachineLearning #CloudComputing #OpenSource

The Modern Developer's Power Couple: Data Science & Free Hosting! 🚀 If you are looking to build and deploy data-driven applications in 2026, these two infographics cover the essentials: The Python Data Science Stack: From the foundations of NumPy and Pandas to advanced AI with PyTorch and TensorFlow. The Deployment Layer: 20 incredible platforms like Vercel, Netlify, and Railway that let you host your projects for free. Whether you're performing complex EDA with Seaborn or deploying a fast API on Render, the barrier to entry has never been lower. Which hosting platform is your favorite for side projects? 👇 #Python #DataScience #WebDevelopment #MachineLearning #CloudComputing #OpenSource

The Modern Developer's Power Couple: Data Science & Free Hosting! 🚀 If you are looking to build and deploy data-driven applications in 2026, these two infographics cover the essentials: The Python Data Science Stack: From the foundations of NumPy and Pandas to advanced AI with PyTorch and TensorFlow. The Deployment Layer: 20 incredible platforms like Vercel, Netlify, and Railway that let you host your projects for free. Whether you're performing complex EDA with Seaborn or deploying a fast API on Render, the barrier to entry has never been lower. Which hosting platform is your favorite for side projects? 👇 #Python #DataScience #WebDevelopment #MachineLearning #CloudComputing #OpenSource

𝐃𝐚𝐲 20 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s focus was on exploring data and visualizing insights using Pandas and Matplotlib. ✔️ Created a DataFrame to organize product data ✔️ Identified the most profitable product and visualized it with a bar plot ✔️ Determined the least profitable product and calculated the profit difference ✔️ Plotted costs and profits across all products using a line chart ✔️ Calculated average cost and average profit per product Insight: using Pandas for both analysis and quick visualizations, alongside Matplotlib for more detailed plots, makes it easier to interpret data and communicate insights effectively. Day 20 complete. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

DSA — LeetCode #760: Anagram Mapping I worked on a classic Data Structures & Algorithms problem and added it to my LeetCode journey. Problem Statement Given two integer arrays s and t, where t is an anagram of s, return an array mapping such that: mapping[i] is the index in t where the value s[i] appears. If a value occurs multiple times, any valid index can be returned. Approach Use a hash map to store indices of elements in array t. Traverse t and map each value to its index (or list of indices). Iterate through array s and fetch the corresponding index from the map. Build the result array using these indices. Example Input: s = [16, 5, 12, 10] t = [5, 10, 12, 16] Output: [3, 0, 2, 1] Complexity ✅ Time Complexity: O(n) ✅ Space Complexity: O(n) #DSA #LeetCode #ProblemSolving #Python #CodingInterview #Algorithms #DataStructures #HashMap #Anagram #CodingPractice #TechJourney #SoftwareEngineering #InterviewPrep #LearnToCode

𝐃𝐚𝐲 22 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today was about doing the unseen but critical work that makes analysis reliable: preprocessing. ✔️ Counted missing values across columns to understand data quality ✔️Compared two strategies for handling missing data: dropping vs. imputing with column means ✔️Updated existing data by adding new attributes and reshaping it from wide to long format ✔️Used melt() to make the dataset more analysis-friendly ✔️Applied conditional filtering with where() to isolate valid records ✔️Standardized column headers for consistency and readability Key insight: preprocessing decisions directly shape the quality of insights you can extract. How you handle missing values, structure data, and standardize formats often matters more than the analysis itself. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

#week -2 Topic : Arrays - DSA Array : An array is used to store a collection of elements under a single variable ,instead of storing the data indifferent variables which saves the memory. let us discuss some array operations : 1) #max and min num in an array def find_max_min(arr): if not arr: return none max_val=arr[0] min_val=arr[0] for num in arr: if num>max_val: max_val=num if num<min_val: min_val=num return max_val, min_val arr=[1, 6,7,0,4] print(find_max_min(arr) ) 2) #sum of elements arr = [1, 2, 3, 4, 5] total = 0 for num in arr: total += num print(total) def reverse_array(arr): start = 0 end = len(arr) - 1 while start < end: arr[start], arr[end] = arr[end], arr[start] start += 1 end -= 1 return arr arr = [1, 2, 3, 4, 5] print(reverse_array(arr)) #DSA #python #arrays #learning