Kapuganti Deepak’s Post

Day 24/75 — This one Python function helped me understand my data better 👇 When I started analyzing datasets, I felt overwhelmed. Too many rows. Too much information. Then I discovered this: df.groupby('city')['price'].mean() 💡 What it does: 👉 Groups data by a category 👉 Calculates insights (like average, sum, count) Example: Instead of looking at thousands of rows… I can instantly see: 📊 Average price per city 🚨 Why this is powerful: • Turns raw data into insights • Helps you compare groups easily • Makes analysis faster and clearer 👨💻 Now I use it all the time to: • Compare categories • Find patterns • Simplify data Small function… But a big upgrade in how I analyze data. Have you used groupby() before? 👇 #DataScience #Python #Pandas #DataAnalysis #LearningInPublic

Started learning Pandas — and now data actually makes sense After working with NumPy, I realized something: Handling real-world data (like CSV files) still felt a bit messy. That’s where Pandas comes in. It’s a Python library designed to make working with structured data simple and efficient. 📊 What’s happening here: • read_csv() loads data into a table-like structure • head() shows the first few rows • info() gives a summary of the dataset 💡 What I understood today: – Pandas organizes data in a structured format (DataFrame) – It makes reading and exploring data very easy – This is exactly how real datasets are handled in Data Science This feels like a big step from writing basic programs to actually understanding data. Next: Selecting specific columns and filtering data in Pandas #Python #Pandas #DataAnalysis #MachineLearning #LearningInPublic #DataScience Here is the code:

🚀#Day10 of #Learning Today I continued exploring Pandas DataFrames and practiced several useful functions for analyzing and organizing data. 🔹 DataFrame Functions – Worked with built-in functions for exploring and understanding data. 🔹 value_counts() – Used value counts to analyze frequency distributions in data. 🔹 sort_values() – Sorted data based on column values. 🔹 Sorting by Multiple Columns – Learned how to sort using more than one column for more refined organization. 🔹 sort_index() – Practiced sorting data based on index labels. 🔹 set_index() and reset_index() – Learned how to set columns as an index and reset them when needed. Today’s learning improved my understanding of organizing, summarizing, and structuring data efficiently Github Repo : https://lnkd.in/gZ8r-ku4 #Python #Pandas #MachineLearning #LearningJourney

🚨 The pandas fatal Mistake BAD NEWS: This does not filter NULL or None values in pandas: ❌ df[df['user_id'] !=None] In pandas, missing values are represented as NaN  or None, but the '!=' operator doesn't handle them as you might expect. The expression will return True for NaN values (since NaN != None is True), so it won't filter them out. 📍 To filter, use notna() or dropna() ✔️ df[df['user_id'].notna()] 🔑 Master pandas and other important Python libraries for data analysis : 🔗 https://lnkd.in/ecpg9u_S

🐍 Working with data? Save this. Honest truth — I keep coming back to these commands more than I'd like to admit. In most data projects, cleaning takes up more time than the actual analysis, and having the right commands at hand makes a real difference. This Python Data Cleaning cheat sheet covers the 5 essentials I rely on constantly: ✅ Handling nulls and duplicates ✅ Quickly inspecting your dataset ✅ Renaming, converting & cleaning columns ✅ Filtering and slicing rows efficiently ✅ Merging and grouping data If you work with pandas regularly, this should always be within reach. Which of these do you use the most? 👇 #Python #DataScience #DataCleaning #Pandas #DataAnalytics

🐼 Most people learn Pandas… But forget the syntax when they actually need it. While working on real data, constant searching = wasted time. That’s why this Pandas Cheat Sheet helps 👇 📌 Covers: • Import (CSV, Excel, SQL) • Data inspection • Cleaning & filtering Perfect for: • Interviews • Projects • Quick revision 💡 The right cheat sheet can save hours. #Python #Pandas #DataAnalytics #DataScience #LearnPython

Worked on a small but practical data analysis task today using Pandas in Python 📊🐍 The goal was to extract meaningful insights using: • Datetime conversion • Multi-column filtering • Calculations Here’s what I did: # Convert to datetime df["Order_Date"] = pd.to_datetime(df["Order_Date"], errors="coerce") # Filter data (Region + Date condition) filtered_df = df[ (df["Region"] == "West") & (df["Order_Date"].dt.month == 1) ] # Calculation total_sales = filtered_df["Sales"].sum() 💡 What this shows: 👉 Converting raw date data into usable format 👉 Applying multiple conditions to filter relevant data 👉 Performing calculations to generate insights This type of workflow is very common in real-world Data Analytics. Key takeaway: Data analysis is not about one function — it’s about combining multiple steps to solve a problem. Step by step improving practical skills in Python and Pandas 🚀 #Python #Pandas #DataAnalytics #EDA #LearningJourney

Most pandas slowdowns aren't caused by bad data-they're caused by the loop you wrote to process it. `𝗶𝘁𝗲𝗿𝗿𝗼𝘄𝘀()` is the default most analysts reach for when they need row-level logic. The problem: it converts each row into a Python Series, creating a new Python object per iteration and bypassing the vectorized NumPy operations that make pandas fast in the first place. 𝗩𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 fixes this - operating on entire columns at once, no Python loop required. → Slow (iterrows): ```python for idx, row in df.iterrows():   df.at[idx, 'margin'] = row['revenue'] - row['cost'] ``` → Fast (vectorized): ```python df['margin'] = df['revenue'] - df['cost'] ``` Same result. On a 1M-row dataset, the vectorized version runs 50–100× faster. This applies to new column calculations, conditional row flags, string transformations , any operation where you're currently writing a loop. 📌 𝗣𝗿𝗼 𝘁𝗶𝗽: When your logic genuinely requires row-level access, `.apply(axis=1)` is a solid middle ground and still slower than pure vectorization, but dramatically faster than `iterrows()`. What's one loop in your current pipeline you could replace today? #DataAnalytics #Python #Data #DataScience #Analytics #DataEngineering #BI