Day 20: Data Analysis with Python using Pandas and Matplotlib

Column transformation + groupby changed how I analyze data 📊 Raw data doesn’t give insights. Prepared data does. While working with Pandas, I realized how powerful simple column transformations are: • Cleaning percentage columns and converting them to numeric • Creating new logic-based columns (BONUS vs NO BONUS) • Adding derived columns instead of touching raw data Once the columns made sense, groupby unlocked the patterns. Grouping by department and aggregating values revealed insights that were invisible at the row level. Big lesson: ➡️ Clean columns first ➡️ Group second ➡️ Insights follow Question for data folks: Do you transform your columns before groupby — or learn this the hard way? 😅 #DataAnalytics #Python #Pandas #GroupBy #LearningInPublic

𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐚𝐲 43: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s work focused on exploring time series data by resampling, visualizing trends with line and bar plots, and applying a rolling average to smooth short-term fluctuations and highlight longer-term patterns. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #SQL #Learning #ostinatorigore

📊 Python Data Visualization Cheat Sheet Data tells a story — visualization is how we make it speak. This cheat sheet brings together the most-used plots from Matplotlib and Seaborn, all in one place for quick reference and daily practice. From line plots and bar charts to heatmaps and KDEs, these are the visuals every data analyst and data scientist should feel comfortable with. Simple concepts, strong foundations. 🚀 Save it, revisit it, and keep building clarity through visuals. #Python #DataVisualization #Matplotlib #Seaborn #DataScience #DataAnalytics #EDA #LearningInPublic #TechSkills #Consistency

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

Making Head()s and Tail()s of Your Data 🐼📊 Ever feel overwhelmed when first looking at a massive dataset? You don't need to load the whole thing to get a feel for it. That's where two of my favorite functions in the pandas library come in! df.head(): This function quickly shows you the first 5 rows of your DataFrame by default, providing an initial glimpse into the structure and data types. df.tail(): Conversely, this one displays the last 5 rows, which is super helpful for checking out recently added data or final entries. It's a simple, yet powerful, trick every data professional uses to start their data exploration and analysis journey on the right foot. #DataScience #Python #Pandas #DataAnalytics #DataManipulation #SQL #MachineLearning #LearningJourney# Abhishek kumar # Harsh Chalisgaonkar # SkillCircle™

When KPIs suddenly look amazing, it’s tempting to celebrate 😅 Then my data reflex says: confirm the level of detail first. If our data is more detailed than we think, joins/merges and aggregations can quietly multiply rows and inflate metrics with zero errors. In PySpark/Python, I quickly check it by doing a groupBy(key).count() to spot duplicates, compare row counts before vs after the transformation, and sanity check a small sample end-to-end. Moral of the story : Celebrate after the checks, not before. #DataEngineering #PySpark #Python #DataQuality

I’m excited to share my latest data analysis project where I focused on visualizing data to uncover meaningful insights 📊 The objective was to create clear and effective bar charts / histograms to understand the distribution of categorical or continuous variables using Python. 📂 Check out the repository here: 🔗 https://lnkd.in/dJQ-y2m7 💡 What I worked on: Data cleaning and preprocessing Exploratory Data Analysis (EDA) Building visualizations using Pandas and Matplotlib Interpreting patterns from the data This project helped me strengthen my fundamentals in data visualization and analytical thinking. Looking forward to building more data-driven solutions! 👨💻 #DataScience #Python #EDA #DataVisualization #GitHub #MachineLearning #LearningJourney #ProdigyInfotech DS TASK 01

📊 [Business Intelligence: Driving Growth Through Data Analytics] This week, I have significantly enhanced my skill in Python's Pandas library, Exploratory Data Analysis (EDA), also data collecting & cleaning through intensive hands-on practice. Pandas is a powerful open-source Python library designed for data manipulation. It enables efficient handling, cleaning, and analysis of complex tabular datasets. Mastering Exploratory Data Analysis (EDA) has provided me with a systematic approach to uncovering data patterns and crafting more precise, impactful visualizations. I have also refined my mindset regarding data collection and cleaning, ensuring that every dataset is ready for analysis. For a more detailed breakdown, our team has compiled a pitch deck below for your review📃 #DigitalSkola #LearningProgressReview #BusinessIntelligence

Implemented advanced Data Visualization techniques under Code Alpha, transforming raw datasets into insight-driven visual representations. Designed line charts, bar graphs, pie charts, box plots, and scatter plots to evaluate sales trends, category performance, regional distribution, and student performance correlations using Matplotlib and Seaborn. 🔗 GitHub Repository: https://lnkd.in/gUCDR_ST � #CodeAlpha #DataVisualization #Python #Matplotlib #Seaborn #DataStorytelling