Data Cleaning Cheat Sheet for SQL and Python

SQL won't make you a Data Engineer. Excel won't make you a Data Engineer. Python won't make you a Data Engineer. Mastering all 3 will. Excel people are scared of code. Python people forget Excel exists. SQL people think Python is overkill. Then they join their first team and reality hits: → Finance sends a 50MB CSV → 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗘𝘅𝗰𝗲𝗹 → The warehouse has 200 tables → 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗦𝗤𝗟 → The API updates every 5 minutes → 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 The best Data Engineers know how to achieve the same: - Using SQL - Using Excel - Using Python The business doesn't care which tool you used.  It cares that the number is right and on time. --- I made this cheatsheet 𝗦𝗤𝗟 ⇆ 𝗣𝘆𝘁𝗵𝗼𝗻 ⇆ 𝗘𝘅𝗰𝗲𝗹 It's the only one you'll ever need. Have a look to it 👇 --- ♻️ Repost if you found it useful, please! 𝟭𝟬𝟬 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝟯𝟬𝟬 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 + 𝗡𝗼𝘁𝗲𝘀 𝟭𝟬𝟬 𝗘𝘅𝗰𝗲𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗡𝗼𝘁𝗲𝘀 + 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 𝗦𝗵𝗲𝗲𝘁 𝟭𝟱𝟬 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 (𝗡𝘂𝗺𝗣𝘆 + 𝗣𝗮𝗻𝗱𝗮𝘀 + 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯) 𝟭𝟬𝟬 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗗𝗔𝗫 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 + 𝗡𝗼𝘁𝗲𝘀 𝟭𝟬𝟬 𝗧𝗼𝗽 𝗛𝗥 𝗥𝗼𝘂𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 𝟭𝟬𝟬 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗡𝗼𝘁𝗲𝘀 𝗥𝗲𝘀𝘂𝗺𝗲 𝗚𝘂𝗶𝗱𝗲 + 𝟳𝟬𝟬 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗦𝗶𝘁𝗲𝘀 𝗚𝗲𝘁 𝗔𝗰𝗰𝗲𝘀𝘀 𝗛𝗲𝗿𝗲: https://lnkd.in/dyBfCTjK #datascience #excel #sql #python #data #dataanalysis

Your Data Analyst journey starts here 📊 From Statistics → SQL → Python → Excel → BI Tools This roadmap is all you need to break into data. Stop overthinking. Start learning. 👉 Take the first step today. #DataAnalyst #DataScience #LearnData #SQL #PythonForData #ExcelSkills

Expectation vs Reality of a Data Analyst 😂 Sometimes you start thinking you’ll build amazing dashboards and find powerful insights… And then reality hits: cleaning data, fixing errors, asking “why is this NULL?” ☕😂 Still… every problem you solve makes you better. 👉 Be honest… what’s the most frustrating part of working with data? #DataAnalytics #DataAnalyst #SQL #PowerBI #Python #BusinessIntelligence #DataCommunity

Over time, I have realized that working as a data analyst is less about working with tools and more about creating impact by asking the right questions. I feel, these are the questions that we should be asking at each stage of the data analysis life cycle, that will help us with better problem solving and decision making. Hence, the quality of the analysis is directly proportional to the quality of questions we ask. #DataAnalytics #DataAnalyst #BusinessAnalytics #SQL #Python #PowerBI #DataStorytelling #Analytics

🚀 Day 6 of My Data Analyst Journey — Understanding Data with Dictionaries Today I learned one of the most important data structures in Python for data analysis: Dictionaries 📊 This is where data starts to look like real-world information (key → value pairs). 🧩 What I Learned: 🔹 Dictionaries in Python Creating & accessing key-value pairs Modifying data inside dictionaries Using dictionary methods Iterating over keys & values 🔹 Advanced Concepts Nested dictionaries (real-world data structure) Dictionary comprehensions Transforming and filtering data 💻 What I Practiced: Solved 13+ problems based on real data scenarios, including: Creating dictionaries with numbers & their squares Accessing specific keys & values Adding & removing elements Iterating through key-value pairs Creating cubes using dictionary comprehension Merging two dictionaries Working with nested dictionaries (student data) Creating dictionary of lists & tuples Converting dictionary → list of tuples Filtering even keys Swapping keys & values Using default dictionaries ⚙️ Key Realization: Dictionaries are the closest thing to real datasets in Python. They help represent: 👉 Student records 👉 Product data 👉 API responses 📈 Growth Check: Day 1 → Basics Day 2 → Conditions Day 3 → Control Flow Day 4 → Lists Day 5 → Tuples & Sets Day 6 → Dictionaries Now the foundation for data analysis is getting complete 📈 #DataAnalyticsJourney #PythonLearning #Day6 #DataStructures #LearnInPublic #FutureDataAnalyst

Python vs SQL — which one should you learn first as a data analyst? I got asked this 3 times this week alone. Here's my honest answer. 🧵 Short answer: SQL first. Always. Long answer 👇 Here's exactly when I use each one: 🟦 Use SQL when: → Querying data from a database → Filtering, grouping, aggregating large datasets → Joining multiple tables together → Building reports and dashboards → Answering business questions fast 🟨 Use Python when: → Cleaning messy, unstructured data → Building machine learning models → Automating repetitive tasks → Creating custom visualizations → Doing statistical analysis beyond basic aggregations The real truth nobody tells you: 90% of daily data analyst work is SQL. Python becomes essential when SQL hits its limits. Think of it this way: SQL = asking questions to your database Python = doing things your database can't do They're not competitors. They're teammates. My personal workflow: ✅ Extract & explore → SQL ✅ Clean & transform complex data → Python ✅ Visualize → Power BI / Matplotlib If you're starting out — master SQL first. Get comfortable with Python second. Then combine both and you become unstoppable. 💪 What did you learn first — SQL or Python? Drop it below 👇 #SQL #Python #DataAnalytics #DataAnalyst #DataScience #LearnSQL #LearnPython #DataCommunity

➡️Customer Churn Analysis Project Hello Everyone👋🏻, hope you all are doing well. I Just finished working on a data analysis project focused on understanding customer churn using Python and Jupyter Notebook. In this project, I performed data cleaning, exploratory data analysis (EDA), and identified key factors influencing churn such as contract type, tenure, services used, and payment methods. # Key Insights: 1. Higher churn observed in month-to-month contract customers 2. Customers with shorter tenure are more likely to leave 3. Certain services and payment methods significantly impact retention # Skills I Learned: 1. Data cleaning and preprocessing 2. Exploratory Data Analysis (EDA) 3. Working with real-world datasets 4. Extracting business insights from data # Skills I Gained: a) Python (Pandas, NumPy) b) Data Visualization (Matplotlib, Seaborn) c) Analytical Thinking d) Problem Solving Looking forward to applying these learnings to real-world business problems. #DataAnalytics #Python #EDA #DataScience #BusinessInsights

🚀 Day 13/20 — Python for Data Engineering GroupBy in Pandas (SQL → Python Connection) If you know SQL… 👉 This is where things start to click. 🔹 What is GroupBy? GroupBy is used to: 👉 group data based on a column 👉 perform aggregation (sum, avg, count, etc.) 🔹 Simple Example import pandas as pd data = { "department": ["IT", "HR", "IT", "HR"], "salary": [50000, 40000, 60000, 45000] } df = pd.DataFrame(data) df.groupby("department")["salary"].mean() 👉 Output: IT → 55000 HR → 42500 🔹 SQL vs Pandas SQL: SELECT department, AVG(salary) FROM employees GROUP BY department; Pandas: df.groupby("department")["salary"].mean() 👉 Same concept. Different syntax. 🔹 Common Aggregations df.groupby("department")["salary"].sum() df.groupby("department")["salary"].count() df.groupby("department")["salary"].max() 🔹 Why This Matters Summarizing data Generating insights KPI calculations Data reporting 🔹 Real-World Use 👉 Raw Data → Group → Aggregate → Insights 💡 Quick Summary GroupBy helps you turn raw data into meaningful summaries. 💡 Something to remember If filtering gives you the right data… Grouping helps you understand it. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 10 – Data Analyst Journey Today I focused on improving my data handling and visualization skills using Excel and Python. 📊 Excel Skills Covered: - Applied Sorting (single & multi-level) to organize datasets - Used Filtering to extract meaningful insights from large data 🐍 Pandas (Python) Concepts: - Worked with DataFrames & Series - Data loading using "read_csv()" - Data exploration using "head()", "info()", "describe()" - Data cleaning: - Handling missing values ("dropna()", "fillna()") - Removing duplicates - Data selection using "loc[]" and "iloc[]" - Applied groupby() for aggregation and insights - Introduction to merge() (combining datasets) 📈 Matplotlib Concepts: - Created basic visualizations: - Line chart - Bar chart - Histogram - Scatter plot - Added chart elements: - Title, labels, legend - Basic customization (grid, markers) 💡 Today’s learning helped me move deeper into real-world data analysis by combining data cleaning, transformation, and visualization. #DataAnalytics #Python #Pandas #Matplotlib #Excel #LearningJourney #FutureDataAnalyst #PlacementPrep

One of the most overlooked skills in data analytics? 👉 Knowing when not to trust your data. Everyone talks about SQL, Python, Tableau… But almost nobody talks about this: Some of the most confident-looking numbers are completely wrong. Not because of bugs— Because of assumptions. A few I’ve seen firsthand: - A “daily total” that double-counted users due to a bad join - A KPI that looked amazing… until you realized the denominator changed - A “trend” that was just a delayed data refresh And the scary part? No one questioned it—because the dashboard looked clean. Real analysts do something different: - They sanity check totals before analyzing trends - They question definitions, not just queries - They look for what shouldn’t be happening Because accuracy isn’t about writing perfect SQL. It’s about having the instinct to say: 👉 “This doesn’t make sense.” That instinct is what separates a report builder from a decision-maker. Curious—what’s a time you caught data that looked right but was completely wrong?