Pandas Class 1 with Krish Naik and Monal Singh

Day 25 Sorting, subsetting, and column transformation: CHECK! ✅ Today’s DataCamp session was a deep dive into Data Manipulation with pandas. These tools are the bread and butter of data cleaning, and I'm loving how much control they give me over my datasets. #DataScience #Python #DataCamp #Lumbinitechmonth

One practical habit that improved my data analysis workflow Before starting any analysis, I create a quick data profiling summary In Python using pandas it takes less than a minute 🗯️ This instantly shows: • statistical distribution • missing data ratio • columns with low or high cardinality It helps me detect problems in the dataset before building any model or visualization #DataAnalysis #Python #DataScience

𝐖𝐡𝐲 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐬 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐅𝐮𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐖𝐨𝐫𝐤👨💻 Recently started using Python for simple data tasks, and one thing I noticed quickly — it makes working with data much easier than doing everything manually. Even basic things like loading a dataset, checking missing values, or calculating averages become much faster with libraries like pandas. Today I practiced reading a dataset, exploring columns, and getting quick summary statistics. Small steps, but it’s interesting to see how quickly you can start extracting useful information from raw data. Slowly getting more comfortable using Python as a tool for analysis rather than just writing code. #Python #DataAnalytics #LearningByDoing #FinalYear

𝐂𝐒𝐕 𝐟𝐢𝐥𝐞 → 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 → 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 𝐚𝐧𝐝 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚. This is Day 4 of #1000DaysOfLearning Yesterday I practiced querying with conditions. Today I learned how indexing works in DataFrames. I understood that the index is separate from columns. Once a column is set as an index, it becomes a row label and still appears on the left even after selecting specific columns. Understanding indexing makes querying feel cleaner. #Python #Pandas #DataScience #LearningInPublic #1000DaysOfLearning

I tried bootstrapping the data before and after cleaning and compared it with the data after using Python to create a machine learning model. There was a change in the standard deviation and a narrowing of the P10 and P90 values. Data source: Alysa Suydam #python #datascience #machinelearning #geoscientist #geoscience #tds 

📊 Starting my Data Analysis Journey with Python! Today I explored a dataset and used Pandas to display the first 10 rows using df.head(10) in Google Colab. 🔍 Observations from the dataset: • Columns like Survived, Age, Fare • Some missing values (NaN) in the Age column • Useful for practicing data cleaning and analysis 💡 Learning step by step: 🐍 Python 📊 Data Analysis 🧠 Problem Solving #Python #DataAnalysis #Pandas #MachineLearning #DataScience #LearningJourney 🚀

One Pandas Cheat Sheet to rule them all. I'm sharing my go-to guide for mastering data manipulation in Python. If you want to level up your Data Science workflow, this is for you. - Clean data faster - Master indexing & filtering - Simplify aggregations Comment "SHEET" below and I’ll DM you the complete version! #AI #DataScience #PythonProgramming #CodingTips

While learning data science tools, I created structured notes and code snippets for NumPy, Pandas, and Matplotlib. Instead of keeping them to myself, I’ve shared everything on GitHub so others can benefit too. If you're learning Python for data analysis, this might help you get started or revise faster. 🔗 Check it out here: https://lnkd.in/d4VTnZSJ Would love your feedback! #DataScience #Python #OpenSource #LearningJourney #GitHub

🚀 Day-56 of #100DaysOfCode 📊 NumPy Practice – Finding Unique Values & Frequency Today I practiced identifying unique elements and counting their occurrences using NumPy. 🔹 Concepts Practiced: ✔ np.unique() ✔ Frequency counting ✔ Handling duplicate values ✔ Efficient array analysis 🔹 Key Learning: Using return_counts=True makes frequency analysis simple and efficient without loops — very useful in data preprocessing. Slowly stepping into data analysis concepts using NumPy 💡🔥 #Python #NumPy #DataAnalysis #ArrayOperations #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

📈 Matplotlib Explained (Visualization Library) Matplotlib is used to create basic plots. 🔹 Important Functions: ✔ plot() → Line chart ✔ bar() → Bar chart ✔ scatter() → Scatter plot ✔ hist() → Histogram ✔ title() → Add title ✔ xlabel(), ylabel() → Axis labels 💡 Visualization helps to understand data easily. #Matplotlib #DataVisualization #Python