Python Dictionaries: Key-Value Pairs and Data Storage

Most costly data mistakes don’t look like errors. I just published a post on Python Data Analysis Errors That Cost Companies Money A must-read for analysts working with real business data. Read it here : https://lnkd.in/dErh6gXH #DataAnalytics #Python #DataQuality

Python script helps users track their daily internet usage against a predefined data bundle. It calculates whether the user has consumed more or less than the expected daily allowance and provides feedback accordingly. https://lnkd.in/d3uv65Rr

🚀 Project Showcase: Web Scraping with Python! 🐍 I recently built a web scraper using Python, BeautifulSoup, and Pandas to extract book data from Books to Scrape 📌 Key Highlights: Scraped book titles, prices, availability, and ratings across multiple pages Used requests and BeautifulSoup for data extraction Stored the data in a CSV file for easy analysis 💡 This project helped me practice data extraction, web automation, and data cleaning—skills essential for Data Analysis and Python development. Check out the CSV output and explore how you can turn web data into actionable insights! Project Link : https://lnkd.in/gqRcbYdb #Python #WebScraping #DataAnalysis #BeautifulSoup #Pandas #DataScience #LearningByDoing

List in Python: Lists are collection of ordered and mutable data. Here are some of the list programs. 1) Program to swap first and fourth element of the List a= ["Ram","Mohan","Krish","Rita"] print(a) a[0],a[3]=a[3],a[0] print(a) 2) Program to add a value a second position in the list a=["Ram","Mohan","Krish","Rita"] print(a) a.insert(1,"Ganesh") == (Here the first value of the list is indexed as 0 hence to add the value a the second place 1 is used) ====================================== a= [13,7,12,10] 1) Program to get the largest and smallest value from the list a.sort() print(a) print("The Largest value in the list is:", a[-1]) print("The Smallest value in the list is:", a[0]) Note: Here once the list is sorted the first value which is at 0 index will always be the smallest and last value at -1 index will always be the highest value.

Python doesn’t replace Excel — it fixes its limits. I use Python in Excel only where it adds value: data cleaning, validation, and repeatable logic. Excel stays the front end; Python does the heavy lifting behind the scenes. Less manual work. Fewer errors. More trust in the numbers. #DataAnalytics #Python #Excel #Automation #DataQuality #AnalyticsInAction

🐍 90 Days of Python – Day 23 Dictionaries in Python | Key–Value Data Structures Today’s focus was on Dictionaries, one of the most powerful and commonly used data structures in Python, especially for real-world data handling and analytics. What I learned today: ✅ Creating dictionaries using key–value pairs ✅ Accessing values using keys ✅ Adding, updating, and deleting elements ✅ Iterating through keys, values, and items ✅ Common dictionary methods (keys(), values(), items(), get()) ✅ Understanding real-world use cases (JSON, APIs, configs, datasets) Dictionaries are essential because they: Store data in a structured key → value format Provide fast lookups Are heavily used in data analytics, machine learning, and backend systems This topic connects directly to working with datasets, APIs, and predictive analytics workflows. 📌 Day 23 completed — learning how to structure data efficiently. 👉 Where have you used dictionaries the most — APIs, data processing, or projects? #90DaysOfPython #PythonDictionaries #LearningInPublic #PythonForData #DataAnalytics #PredictiveAnalyticsJourney

23rd's Python Class – Data Types, map() & Input Handling In a recent Python session, we explored how Python handles different data structures and how functional tools can process collections efficiently. 🔹 Basic Data Structures Identified data types using type(): List [] Tuple () Dictionary {} Set set() Understood the difference between empty dictionary {} and empty set set() 🔹 Filtering Data Used filter(None, iterable) to remove: Empty values None False-equivalent elements Learned how Python treats truthy and falsy values 🔹 map() Function Applied map() to process elements from multiple collections Used built-in functions like max() and min() with map() Created new collections based on element-wise comparison 🔹 User Input Handling Took input as strings and integers Used split() and list comprehension for multiple inputs Observed how data type conversion affects output This class strengthened my understanding of Python collections and functional programming basics, making data handling more effective and clean 🚀 #Python #DataStructures #map #filter #PythonBasics #FunctionalProgramming #CodingPractice #StudentLearning Pooja Chinthakayala

Moving ahead from sql to python worked on towards analyzing and training the datasets over a sample size. Methodology Cleaning and Pre_Eda Checks Merging and Sorting EDA Distribution across Bar,Box and Scatter Plot Working on Features and training model for finding accuracy and confusion matrix to optimise further performance. Key Learnings Tabular & Non-Tabular Datasets Continuous and Discrete Values Mean,InterQuartileRange,Upper vs lower Quartile,Outliers,Z-scores. Usecase of Classification and Regression Accuracy,Precision,Recall,F1-Score,model-coefficients Underfitting vs Over fitting. Summary of Insights 1) Clean Data is valuable for getting better predictions. 2) EDA provides brief insight for detecting patterns. 3) Good Feature provides better input and Target output for model to interpret values and is designed considering the problem statement and requirement. 4) Scenarios when models can be accurate and still be wrong. Open to Feedback. https://lnkd.in/gV5BNqcq

From messy data to clean insights—Python makes it simple! Cleaning data is one of the most time-consuming tasks for any analyst, but with Pandas, it becomes fast and efficient: Remove duplicates in seconds ✅ Fill missing values automatically ✅ Standardize formats effortlessly ✅ Even small Python scripts save hours of manual work and make your analysis more accurate. #Python #Pandas #DataCleaning #DataAnalytics #DataDriven #SQL

🔷 Python Strings – Single, Double & Multiline Strings are used to store text data in Python. They are written inside single quotes (' ') or double quotes (" "). 🔹 1️⃣ Single Quotes & Double Quotes Both single and double quotes work the same in Python. ▶ Example print('hi') print("hi") ✔ Output: hi 🔹 2️⃣ Using Quotes Inside Strings We can use single quotes inside double quotes and vice versa. ▶ Example print("my programming language is 'python'") print('my programming language is "python"') ✔ Output: my programming language is 'python' my programming language is "python" 🔹 3️⃣ Storing Strings in Variables We can store strings in variables. ▶ Example name = 'neena' print(name) ✔ Output: neena 🔹 4️⃣ Multiline Strings (Triple Quotes) To write strings in multiple lines, we use triple quotes (""" """). ▶ Example a = """ qwer aasfghjkk zxvnm """ print(a) ✔ Output: qwer aasfghjkk zxvnm 📌 Learning strings is important for working with text, messages, and user input. #Python #PythonStrings #LearningPython #DataAnalyticsJourney #CodingLife