Yejra Chandala’s Post

🐼 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 #Python #Pandas #DataAnalysis #MachineLearning #DataScience #ProgrammingValley #Analytics

🚀 𝐇𝐨𝐰 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐔𝐬𝐞 𝐀𝐏𝐈𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 🐍 As a Data Analyst, we often need data from different websites or apps — that’s where APIs help us! 🌐 🔹 What is an API? An API is a way to connect one system with another and get data easily (mostly in JSON format).\ 🔹 Why APIs are useful: ✅ Get live or real-time data (like weather, stock price, etc.) ✅ Save time – no need to download files again and again ✅ Combine data from many sources for better insights 🔹 How Python helps: 🐍 requests → to call API 📊 pandas → to clean and analyze data 💾 json → to read and use API data Example 👇 import requests import pandas as pd url = "https://lnkd.in/g-E2eRxh" response = requests.get(url) data = response.json() df = pd.DataFrame(data) print(df.head()) ✨𝐖𝐢𝐭𝐡 𝐣𝐮𝐬𝐭 𝐚 𝐟𝐞𝐰 𝐥𝐢𝐧𝐞𝐬 𝐨𝐟 𝐏𝐲𝐭𝐡𝐨𝐧 𝐜𝐨𝐝𝐞, 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐜𝐨𝐥𝐥𝐞𝐜𝐭 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐳𝐞 𝐝𝐚𝐭𝐚 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲! Follow me - Shivam Tripathi 👉 𝗜𝗳 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲𝗱 𝘁𝗵𝗶𝘀, 𝗳𝗼𝗹𝗹𝗼𝘄 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗱𝗮𝘁𝗮 𝘁𝗶𝗽𝘀! #DataAnalyst #Python #API #DataAnalytics #Learning #DataScience

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

🚀 𝐒𝐐𝐋 𝐭𝐨 𝐏𝐲𝐭𝐡𝐨𝐧 — 𝐓𝐡𝐞 𝐐𝐮𝐢𝐜𝐤𝐬𝐭𝐚𝐫𝐭 𝐆𝐮𝐢𝐝𝐞 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝! If you’re transitioning from SQL to Python (Pandas) for data analysis, this visual guide is your cheat sheet! 💡 SQL Certification Course :- https://lnkd.in/dVRSnppf Here’s how common SQL operations translate directly into Python code: 1️⃣ Filtering: WHERE ➜ df[df['column'] == 'value'] 2️⃣ Ordering: ORDER BY ➜ df.sort_values(by='column') 3️⃣ Removing Duplicates: DISTINCT ➜ df.drop_duplicates() 4️⃣ Handling Missing Data: COALESCE() ➜ df['column'].fillna() 5️⃣ Changing Data Types: CAST() ➜ df['column'].astype() 6️⃣ Renaming Columns: AS new_col ➜ df.rename() 7️⃣ Aggregations: SUM(), AVG(), MIN(), MAX() ➜ .sum(), .mean(), .min(), .max() 8️⃣ Counting: COUNT() ➜ .count() 9️⃣ Percentiles: PERCENTILE_CONT() ➜ .quantile() 🔟 Joins & Unions: JOIN, UNION ALL ➜ pd.merge(), pd.concat()

📌 Master Data Cleaning with Pandas: From Messy to Marvelous! Dealing with messy datasets is a fundamental part of any data analyst's job. Raw data is often filled with inconsistencies, missing values, and duplicates that can skew your analysis and lead to incorrect conclusions. The Pandas library in Python provides a powerful and intuitive toolkit for tackling these issues efficiently. One of the first steps is handling missing data using methods like `isnull()` to detect gaps and `fillna()` to impute values with a statistic like the mean or median. Next, you'll want to remove duplicate rows that can artificially inflate your counts; the `drop_duplicates()` function is perfect for this. Data type inconsistencies are another common problem; always use `dtypes` to check and `astype()` to convert columns, ensuring numbers are not stored as objects. String columns often need standardization—applying `str.lower()` or `str.strip()` ensures uniform text formatting. For more complex cleaning, you can use the `apply()` function to run custom operations on entire columns. Renaming columns with `rename()` makes your DataFrame more readable, while the `replace()` function is excellent for swapping incorrect categorical values. Mastering these Pandas techniques transforms a chaotic dataset into a clean, reliable source for your analysis, saving you hours of manual work and preventing critical errors. What is the most challenging data cleaning issue you've faced in a project? #DataCleaning #PandasPython #DataAnalysis #DataWrangling #PythonForData

📌 Pandas for Data Science – Essential Cheatsheet for Beginners Pandas is a powerful Python library for data manipulation and analysis. Here’s what you’ll master from this cheat sheet: ⬇️ Core Concepts Covered: 1️⃣ Pandas Data Structures Series: 1D labeled array DataFrame: 2D labeled data structure 2️⃣ Data Selection By position: .iloc[] By label: .loc[] Boolean indexing and slicing 3️⃣ Retrieving Information .shape, .columns, .info(), .describe() .sum(), .mean(), .median() 4️⃣ Sorting & Ranking sort_values(), sort_index(), rank() 5️⃣ I/O Operations Read/write CSV → read_csv(), to_csv() Read/write Excel → read_excel(), to_excel() SQL database queries using read_sql() and to_sql() 6️⃣ Function Applications Use apply() for custom logic Use lambda for inline processing 7️⃣ Data Alignment & Fill Align misaligned indexes Use fill methods: fill_value=0 in operations 🎓 Recommended Courses to Master Pandas: 🟦 Python & Data Science Foundations Microsoft Python Development Certificate → https://lnkd.in/dDXX_AHM Google IT Automation with Python → https://lnkd.in/dyJ4mYs9 IBM Data Science → https://lnkd.in/dhtTe9i9 📈 Pandas & Analytics Focus Python Data Analysis by Meta → https://lnkd.in/dTdWqpf5 Data Analysis with Python → https://lnkd.in/dc2p2j_W Data Visualization & Pandas → https://lnkd.in/d8e7aQCQ Credit: DataCamp https://www.datacamp.com #Python #Pandas #DataScience #FreeCourses #ProgrammingValley #Analytics #MachineLearning #DataCleaning #PythonLibraries #DataCamp

From Python to SQL — I just did EDA using only SQL! Last night, I challenged myself with something different. Instead of doing Exploratory Data Analysis (EDA) in Python (like I usually do with pandas), I tried doing it using only SQL. At first, it felt unusual — no df.describe(), no isnull(), no hist()... just queries! But as I started writing step by step, something clicked. I realized SQL is not just for databases — it’s actually a powerful analytical tool too. 💡 Here’s what I explored 👇 🔹 Checked my dataset using Head, Tail & Random Sample queries 🔹 Created a Five-number summary (Min, Q1, Median, Q3, Max) using Window Functions 🔹 Detected Outliers using the IQR method 🔹 Found Missing Values directly in SQL 🔹 Built Price Buckets (Histogram) using CASE WHEN 🔹 Did Bivariate Analysis — like which company sells the most touchscreen laptops It felt like doing EDA with pandas… but through pure SQL logic. 🧠 💭 Why this matters: Understanding how to perform data analysis inside SQL builds a deeper connection with the raw data. You don’t just “load and clean” — you truly understand how data behaves in its native environment. ✨ Key takeaway: You don’t always need Python to explore your data. Sometimes, a few smart SQL queries can reveal just as much. Would you be interested if I share the exact SQL queries and breakdown for each EDA step? #DataAnalysis #SQL #EDA #LearningJourney #DataAnalytics #DataScience #PythonToSQL #BhoopendraVishwakarma

📊 Top 20 Python Functions for Data Analysis Master these essential functions to clean, explore, and visualize data effectively 👇 ➡️ Data Cleaning & Transformation • head() – View the first few rows of your dataset • info() – Check column types and non-null counts • describe() – Get summary statistics (mean, min, max, quartiles) • dropna() – Remove missing values • fillna() – Fill missing values with a specific value or method • rename() – Rename columns for clarity ➡️ Data Filtering & Selection • loc[] – Select rows/columns by label • iloc[] – Select rows/columns by index position • query() – Filter rows using conditions • isin() – Filter rows that match specific values ➡️ Aggregation & Grouping • groupby() – Group data for aggregation • agg() – Apply multiple aggregation functions • sum() – Add up column or group values • mean() – Calculate average • count() – Count rows or non-null values ➡️ Merging & Joining • merge() – Join DataFrames on common columns (like SQL JOIN) • concat() – Combine datasets vertically/horizontally • join() – Merge DataFrames by index keys ➡️ Exploration & Visualization • value_counts() – Count unique values • pivot_table() – Create Excel-like summaries • plot() – Visualize data (line, bar, scatter, etc.) 🎓 Learn Python for Data Analysis 1️⃣ Python for Everybody → https://lnkd.in/dNB4GthH 2️⃣ Data Analysis with Python → https://lnkd.in/dc2p2j_W 3️⃣ IBM Data Science Certificate → https://lnkd.in/dhtTe9i9 Credit: Esther Anagu #Python #DataAnalysis #DataScience #MachineLearning #Pandas #ProgrammingValley #Analytics #BigData #LearnPython #Visualization

📊 Python for Data Analysis Brought to you by programmingvalley.com Data analysis isn’t just about writing code — it’s about cleaning, exploring, and visualizing data efficiently. This quick reference shows the essential Python functions every analyst should know for: → Data Cleaning Remove missing values, fix data types, handle NaN values, and reshape datasets with: dropna(), fillna(), astype(), nan_to_num(), reshape(), unique() → Exploratory Data Analysis (EDA) Summarize, group, and explore data patterns using: describe(), groupby(), corr(), plot(), hist(), scatter(), sns.boxplot() → Data Visualization Turn insights into visuals with: bar(), xlabel(), ylabel(), sns.barplot(), sns.violinplot(), sns.lineplot(), plotly.express.scatter() 🎓 Recommended Courses to Master Data Analysis → IBM Data Science Professional Certificate https://lnkd.in/dhtTe9i9 → Google Data Analytics Professional Certificate https://lnkd.in/dTu5tMBK → Microsoft Python Development Professional Certificate https://lnkd.in/dDXX_AHM → Meta Data Analyst Professional Certificate https://lnkd.in/dTdWqpf5 → SQL for Data Science https://lnkd.in/d6-JjKw7 💡 Save this post for future reference and share it with your network. #Python #DataAnalysis #DataScience #Analytics #MachineLearning #ProgrammingValley #PythonLearning

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