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🐼 Pandas Essential Commands Cheatsheet — Learn the Most Used Functions Fast Whether you’re cleaning data or doing analysis, these commands are your daily essentials in Python Pandas 👇 📥 Load & Inspect Data → pd.read_csv('file.csv') → Load data from a CSV file → df.head() → Display first 5 rows → df.shape → Check dimensions (rows, columns) → df.info() → View datatypes and memory info → df.describe() → Generate summary statistics 📊 Select & Filter Data → df['column'] → Select one column → df[['col1','col2']] → Select multiple columns → df.loc[row_label] → Access rows by label → df.iloc[row_index] → Access rows by index position → df.query('column > value') → Filter using conditions 🧹 Handle Missing Data → df.dropna() → Remove missing values → df.fillna(value) → Fill missing values 📈 Sort, Group & Aggregate → df.sort_values('column') → Sort data → df.groupby('column').agg() → Group and summarize data → df.value_counts() → Count unique values 🔗 Combine & Modify Data → df.merge(df2, on='key') → Merge dataframes → df.rename(columns={'old':'new'}) → Rename columns → df.drop('column', axis=1) → Remove column → df.reset_index() → Reset index 🎓 Learn Pandas in Action (Free): 🔗 https://lnkd.in/dc2p2j_W 🔗 https://lnkd.in/d5iyumu4 ✍️ Credit: Gina Acosta hashtag #Python hashtag #Pandas hashtag #DataAnalysis hashtag #MachineLearning hashtag #DataScience hashtag #ProgrammingValley hashtag 10000 CodersVamsi Enduri Yejra Chandala

Messy data? Meet Pandas 🐼 If you’ve ever worked with raw datasets, you know the pain — missing values, inconsistent columns, weird text formats… the list goes on Last week, I took a messy CSV file from a public dataset and decided to give it a serious cleanup using Python and Pandas. Here’s how it went 👇 🧩 The Problem: The dataset had: Duplicate rows Inconsistent date formats Null values in key columns Irregular capitalization in text fields It wasn’t analysis-ready — and that’s where Pandas came in. The Solution (in a few lines): import pandas as pd # Load data df = pd.read_csv("data.csv") # Remove duplicates df.drop_duplicates(inplace=True) # Fill missing values df['Revenue'] = df['Revenue'].fillna(df['Revenue'].mean()) # Standardize text df['City'] = df['City'].str.title() # Convert date format df['Date'] = pd.to_datetime(df['Date'], errors='coerce') # The Result: After a few transformations, the dataset was clean, structured, and ready for visualization. I even created a quick chart to analyze sales trends by city — and instantly spotted patterns that were hidden in the messy version before! 💡 What I Learned: Small cleaning steps can make a huge difference. Consistency in data formatting is key for meaningful analysis. Pandas makes the entire process fast, readable, and satisfying. Would you like me to share the full notebook and cleaned dataset? I’d be happy to break it down step-by-step. #Python #Pandas #DataCleaning #DataAnalytics #DataVisualization #LearningInPublic

Just wrapped up the “Joining Data with Pandas” course by DataCamp — and it was packed with practical insights for real-world data cleaning in Python. Here are my top takeaways: 1.Core Join Types in pandas.merge() Inner Join: Only matching rows from both tables Left Join: All rows from the left, matched data from the right Right Join: All rows from the right, matched data from the left Outer Join: All rows from both, with NaNs where no match 2.One-to-One vs One-to-Many Joins One-to-One: Each key appears once in both tables One-to-Many: One key in left matches multiple in right — common in real datasets 3. Advanced Join Techniques merge() with suffixes to handle overlapping column names merge() on multiple columns (e.g., ['address', 'zip']) for precise matches merge_ordered() for time-series data with optional forward fill merge_asof() for nearest-key joins — great for aligning timestamps 4.Filtering Joins Semi Join: Keep only rows in left table with matches in right Anti Join: Keep only rows in left table with no matches in right 5.Vertical Concatenation pd.concat() to stack DataFrames Use keys for multi-indexing and ignore_index=True to reset row numbers 6. Data Integrity validate='one_to_one' or 'one_to_many' in merge() to catch unexpected duplicates verify_integrity=True in concat() to avoid index collisions 7.Querying and Reshaping .query() for SQL-like filtering with readable syntax .melt() to reshape wide data into long format for analysis #Python #Pandas #DataScience #DataCleaning #LearningJourney #LinkedInLearning #DataCamp

🚀 Python + SQL + Data Analysis Project on Inventory Data Over the past few days, I worked on analyzing an Inventory Dataset using Python and SQL — combining both data extraction and visualization in one workflow. Here’s what I did in this project: 🔹 Connected SQL database with Python using psycopg2 and SQLAlchemy 🔹 Wrote SQL queries to fetch and summarize inventory data 🔹 Cleaned and transformed data using Pandas 🔹 Performed vendor and product-wise analysis 🔹 Created interactive charts using Matplotlib and Seaborn 🔹 Visualized key insights like top vendors, gross profit, and sales trends This project helped me understand how to bridge database management and data analytics using Python. It was a great hands-on experience for learning end-to-end data flow — from SQL tables to insights and visual reports. 🎥 Here’s a short video showing my workflow and results! Would love to hear your feedback and suggestions. 🙌 #Python #SQL #DataAnalysis #DataVisualization #Pandas #Matplotlib #Analytics #LearningJourney #DataScience Skill Course, Asif Sakali, Satish Dhawale

Clean Data = Smart Insights! Ever opened an Excel or CSV file and noticed the same value repeated again and again? 😅 That’s what we call duplicates — and they can completely mess up your analysis! Let’s see how Python (using Pandas) can fix that in seconds 🚀 🧩 Remove Duplicate Rows If your entire row is repeated (same name, amount, date, etc.), just use this: import pandas as pd df = pd.read_csv("sales.csv") # Remove all duplicate rows df = df.drop_duplicates() ✅ Boom! Now your dataset keeps only unique rows. 🔍 Remove Duplicate Values in One Column Maybe your “Customer Name” or “Email” column has duplicates — you can target just that: df = df.drop_duplicates(subset=['CustomerName']) This keeps the first unique value and removes the rest. You can even keep the last one by adding: df = df.drop_duplicates(subset=['CustomerName'], keep='last') 💬 Why it matters: Duplicates = misleading results. Clean data = clear insights. And the best part? You can clean thousands of records in just one line of code! 🧠✨ Let’s be honest — who doesn’t love a quick fix that makes data look instantly smarter? 😎 If you found this helpful, drop a 💬 below and tell me your favorite data cleaning trick in Python! #Python #DataAnalysis #DataCleaning #pandas #DataScience #Analytics #LearningWithPython

📊 Pandas Merge vs Merge_Ordered — What’s the Difference? If you’ve worked with pandas, you’ve probably used merge() — but have you explored merge_ordered()? 🤔 Here’s a quick breakdown 👇 🔹 merge() Used for combining any two DataFrames based on common columns or indexes. ➡️ Works just like SQL joins (inner, left, right, outer) ➡️ Does not care about order — it just matches keys. pd.merge(df1, df2, on='id', how='inner') 🔹 merge_ordered() Used when order matters — ideal for time-series or sequential data. ➡️ Performs an ordered merge (keeps data sorted). ➡️ Has fill_method to handle missing values (like forward fill). pd.merge_ordered(df1, df2, on='date', fill_method='ffill') 💡 In short: Use merge() → when combining data by key (like SQL joins). Use merge_ordered() → when merging chronological or ordered data while preserving sequence. #DataScience #Python #Pandas #DataAnalytics #LearningEveryday

𝗗𝗮𝘆 𝟮𝟭: 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴, 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 & 𝗝𝗼𝗶𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮. Today was about control and connections - getting exactly what you need from your data and combining it intelligently. This is where pandas starts feeling like SQL in Python. 🧵 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 & 𝗦𝘂𝗯𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀 Learned how to filter DataFrames with precision. Applied conditions to grab exactly the rows I need. Then discovered .query() - and it clicked. It’s basically SQL WHERE clauses in pandas. Same logic, different syntax. If you know SQL, this feels natural. If you don’t, it’s still cleaner than stacking conditions. 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 & 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 Covered sorting data - ascending, descending, multiple columns. Then combined sorting with filtering. Filter first, then sort. Or sort first, then filter. Depends on what you need. Small thing, but knowing when to do what saves time. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀 & 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 Went into creating custom DataFrames from scratch. Sometimes you’re not loading data - you’re building it. Understanding how to structure data yourself matters more than you’d think. It’s how you test ideas before touching production data. 𝗠𝗲𝗿𝗴𝗶𝗻𝗴 & 𝗝𝗼𝗶𝗻𝗶𝗻𝗴 This is where it got real. Data rarely lives in one place. You need to combine tables. Covered all the join types: - Inner join: only matching records - Left join: all from left, matching from right - Right join: all from right, matching from left - Outer join: everything from both If you know SQL joins, this is the same concept. If you don’t, now you do. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Real analysis uses multiple data sources. Customer data in one table. Transaction data in another. Product data somewhere else. You can’t analyze what you can’t combine. Master joins and you unlock the ability to answer way more complex questions. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 .query() makes filtering cleaner and more readable. Sorting and filtering together is a power move. Joins are non-negotiable. Learn them in pandas. Learn them in SQL. Same logic, different tools. 𝗗𝗮𝘆 𝟮𝟭 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲. What’s your preferred join type and why? #DataEngineering #Python #Pandas #DataCleaning #SQL #LearningInPublic #BuildingInPublic #Datafam

Week 4 : Day 03 — Data Visualization with Matplotlib 🧠 What is Matplotlib? Matplotlib is a Python library used to create static, interactive, and animated visualizations. 📦 Installation pip install matplotlib 🔹 Basic Scatter Plot import matplotlib.pyplot as plt hours = [2, 4, 6, 8, 10] marks = [30, 50, 70, 90, 110] plt.scatter(hours, marks) plt.xlabel("Hours Spent") plt.ylabel("Marks Obtained") plt.title("Hours vs Marks") plt.show() 🔹 Multiple Data Series math_marks = [30, 50, 70, 90, 110] science_marks = [40, 60, 80, 100, 120] plt.scatter(hours, math_marks, label="Math") plt.scatter(hours, science_marks, label="Science") plt.xlabel("Hours Spent") plt.ylabel("Marks Obtained") plt.title("Subject Performance Comparison") plt.legend() plt.show() Day 04 — More About Data Visualization 🧰 Python Visualization Libraries TypeLibraryDescriptionBasicMatplotlibLow-level, customizable plotsAdvancedSeabornStatistical and elegant visualsInteractivePlotlyInteractive, web-based charts 🧰 Non-Python Visualization Tools ToolDescriptionTableauDrag-and-drop data visualizationPower BIMicrosoft BI toolGoogle Looker StudioCloud-based data visualizationData WrapperQuick online charts and maps 🎨 Color Resources: ColorBrewer Adobe Color Wheel Pinterest Color Picker Day 05 — Popular Python Libraries Data Science: NumPy, Pandas, Matplotlib, Scikit-learn, PyTorch, TensorFlow APIs: Requests, Flask, FastAPI Web Development: Flask, Django, Streamlit Web Scraping: BeautifulSoup, Selenium, Scrapy Computer Vision: OpenCV, Pillow, MoviePy, Ultralytics Day 06 — Important Resources 📚 Reading & Practice W3Schools GeeksforGeeks 🧩 Practice Platforms Hackerrank, Leetcode, CodeChef 🎥 YouTube Channels The New Boston, Telusko, freeCodeCamp, Krish Naik #Python #DataScience #DataEngineer #DataAnalytics #AzureDataEngineer

👋 Hi everyone! 🎨 Today’s Topic : Data Visualization with Python - Grouped (Clustered) Bar Chart Data visualization is one of the most powerful aspects of data analytics. It transforms complex datasets into clear, actionable insights through charts and visuals. 📊 Today, I focused on the Grouped (Clustered) Bar Chart, using it to compare the number of orders by Age Group and Gender in Python with Matplotlib and Seaborn. After cleaning my dataset, this visualization helped me quickly identify how order patterns vary between different age groups and genders — a key insight for understanding customer behavior and business performance. If you haven’t seen my Data Cleaning post yet, check it out here! 👇 🔗[https://lnkd.in/egFGZSyT] 🧠 Key Steps Followed : ✅ Created a grouped bar chart using sns.countplot() ✅ Added data labels with ax.bar_label() for better clarity ✅ Used palette="colorblind" for accessibility-friendly colors ✅ Customized titles, axis labels, and legend for a professional look 📈 Grouped Bar Charts are great for comparing multiple categories side by side — simple, insightful, and presentation-ready. 💬 Which chart would you like to see next? (Line chart, histogram, or donut chart?) Comment below! 👇 #DataVisualization #Python #Seaborn #Matplotlib #DataAnalytics #DataScience #PowerBI #Excel #NareshDailyPost

𝐎𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 — 𝐋𝐢𝐬𝐭𝐬!🧠 Lists allow us to store multiple items in a single variable, making them super useful for handling collections of data. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 👇 🔹𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚 𝐋𝐢𝐬𝐭: items = ["bread", "pasta", "fruits", "veggies"] Lists are enclosed in square brackets [] and can store multiple values. 🔹𝐀𝐩𝐩𝐞𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: append() adds a new element at the end of the list. items.append('butter') # ['bread', 'pasta', 'fruits', 'veggies', 'butter'] 🔹𝐑𝐞𝐦𝐨𝐯𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: remove() deletes a specific item from the list. items.remove('butter') # ['bread', 'pasta', 'fruits', 'veggies'] 🔹𝐈𝐧𝐬𝐞𝐫𝐭 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: insert(index, value) adds an element at a specific position. items.insert(1, 'butter') # ['bread', 'butter', 'pasta', 'fruits', 'veggies'] 🔹𝐒𝐨𝐫𝐭 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: sort() arranges elements in ascending order by default. expenses = [30, 1200, 45, 300] expenses.sort() # [30, 45, 300, 1200] 🔹𝐑𝐞𝐯𝐞𝐫𝐬𝐞 𝐎𝐫𝐝𝐞𝐫: Use sort(reverse=True) to sort in descending order. expenses.sort(reverse=True) # [1200, 300, 45, 30] 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬:✨ ✅ Lists allow you to store sequential data. ✅ Lists are ordered and mutable which you can add, remove, or modify elements. ✅ Lists can hold mixed data types like strings, numbers, or even other lists. ✅ You can access specific ranges of data using slicing (list[start:end:step]). Loving how simple yet powerful Python data structures are!📘 #Python #DataAnalytics #LearningJourney #Codebasics #SQL #Powerbi #PWC