132
_KHow to get the values on a bar plot for easy analysis #sales #dataanalysis #datascience
@_k_lite_
#Python Programming Code On Laptop Screen Data Analysis
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27Трендовые Reels
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15.6K
LONobody shows you how statistics actually works in real data analysis (until now) 📊
So everyone says “learn statistics” but then you’re stuck with textbook formulas that don’t connect to actual work. Here’s the exact statistical pipeline I run on every customer dataset:
Start with descriptive statistics. Calculate mean, median, mode, standard deviation, and variance for transaction amounts. This shows you central tendency and spread. You need to know what “normal” looks like before you find what’s weird.
Calculate percentiles and quartiles. Use the quantile function to find 25th, 50th, 75th, and 90th percentiles. This helps you understand distribution and spot outliers fast.
Detect outliers using the IQR method. Calculate Q1, Q3, and IQR. Then identify transactions that fall below Q1 minus 1.5 times IQR or above Q3 plus 1.5 times IQR. This catches the weird stuff automatically.
Perform correlation analysis. Calculate Pearson correlation coefficient between transaction amount and customer age. This measures the strength and direction of linear relationships between variables.
Analyze frequency distribution by creating bins. Use the cut function to group transaction amounts into ranges and count occurrences. This shows you the distribution pattern visually.
Calculate z scores to identify statistical anomalies. Compute how many standard deviations each transaction is from the mean. Flag extreme values that need investigation.
This is how you actually use statistics in the real world. Not memorizing formulas for a test.
Comment “CODE” and I’ll send you the full Python script. 🎯
#StatisticsForDataScience #PythonForDataAnalysis #DataAnalyticsTutorial #StatisticalAnalysis
@loresowhat
637
CO📊 Infographic Elements That Every Data Person Should Master 🚀
After years of working with data, I can tell you one thing:
👉 The chart ou choose is as important as the data itself.
Here’s your quick visual toolkit 👇
🔹 Timelines
* Sequential ⏩ great for processes
* Scaled ⏳ best for real dates/events
🔹 Circular Charts
* Donut 🍩 & Pie 🥧 for proportions
* Radial 🌌 for progress or cycles
* Venn 🎯 when you want to show overlaps
🔹 Creative Comparisons
* Bubble 🫧 & Area 🔵 for impact by size
* Dot Matrix 🔴 for colorful distributions
* Pictogram 👥 when storytelling matters most
🔹 Classic Must-Haves
* Bar 📊 & Histogram 📏 (clear, reliable)
* Line 📈 for trends
* Area 🌊 & Stacked Area for the “big picture”
🔹 Advanced Tricks
* Stacked Bar 🏗 when categories add up
* Span 📐 for ranges
* Arc 🌈 for relationships
{Data Visualization, Infographic, Coding Tips, Developer Tools, Programming Knowledge, Data Science, Tech Explained}
#viral#explorepage#trending#techcontent#instagood
@codingwithmee_18
104
THWhat data requires a Pie Chart? A tip to better data storytelling.
#datanalysis #daretopost @stanforcreators
@thamuallan
2.9K
PYTurn your data into stunning visuals in 3 lines! 📊
Matplotlib makes data visualization super easy.
Perfect for reports and presentations.
Save this for your next data project!
Comment “YES” if this helped 👇
#PythonDataViz #MatplotlibTutorial #DataVisualization #LearnPython #PythonForBeginners
@pythonsnippets.py
1
ASWelcome to Episode 17 of Python for Data Analysis. In this episode, you will learn one of the most important fundamentals in data science: how to load and understand datasets using Python.
Working with data files is a core skill for data analysts, data scientists, and machine learning engineers. Most real-world datasets are stored in formats such as CSV files and Excel spreadsheets, so learning how to read them properly is essential.
In this tutorial, we explore how Python libraries like pandas and csv allow you to import and work with datasets efficiently. The video explains the differences between CSV and Excel files and demonstrates how to load them into Python for analysis.
You will also learn how to explore your dataset using pandas functions such as head(), info(), and describe() to quickly understand the structure of your data. The episode also introduces important data preparation concepts including handling missing values, converting data types, and removing duplicates.
Another key topic covered is file paths in Python, including the difference between absolute paths and relative paths, and how they help Python locate datasets in your project folders.
By the end of this episode, you will understand how to:
• Load CSV files using Python
• Load Excel files using pandas
• Work with DataFrames for data analysis
• Explore datasets using pandas functions
• Clean and prepare data for analysis
• Understand absolute and relative file paths
This episode is perfect for beginners starting their journey in data analysis, Python programming, or data science.
If you are following the Python for Data Analysis course, this lesson builds the foundation for upcoming topics such as data visualization, machine learning, and advanced analytics.
Subscribe to the channel and follow the full course playlist to continue learning step by step.
#Python
#PythonForDataAnalysis
#DataAnalysis
#DataScience
#Pandas
#PythonTutorial
#CSVFiles
#ExcelFiles
#PythonCSV
#PythonExcel
#ReadCSV
#ReadExcel
#Dataset
#DataCleaning
#DataExploration
#PythonProgramming
#MachineLearning
#DataAnalytics
#Coding
#Programming
#PythonBeginner
#LearnPython
#PythonCourse
#DataScienceCourse
#PythonPan
@assignmentonclick
128
TH📊 Bar Chart vs Pie Chart: When Should You Use Each?
One small decision can make your data either clear or confusing: choosing the right chart.
Here’s a simple rule I use when analyzing program data.
Use a Bar Chart when you want to compare values.
Bar charts are perfect when you need to show differences between categories.
Examples:
• Comparing Food Consumption Scores across counties
• Beneficiaries reached by project location
• Market prices across different markets
Bar charts make it easy to see which category is higher, lower, or changing.
Use a Pie Chart when you want to show proportions of a whole. Pie charts work best when the goal is to show how something is divided.
Examples:
• Gender distribution of beneficiaries
• Budget allocation across project components
• Main food sources for households
Pie charts help the audience quickly understand percentage composition.
💡 A simple rule to remember:
If you’re comparing categories → use a Bar Chart.
If you’re showing parts of a whole → use a Pie Chart.
Good data analysis is not just about formulas or tools.
It’s also about presenting data in a way that people immediately understand.
#DataAnalytics #Excel #DataVisualization #MonitoringAndEvaluation #daretopost @stanforcreators
@thamuallan
117
ANA Python script can make creating pie charts easier. It helps to visualize data in a simple, understandable way, saving time and effort. #python #coding #programming #dataVisualization #pieChart
@anote.tech
158
SCSave it ✔️... Share it 🚀
Compress image using Python
Don't forget to save this post for later and follow @scripts_kart
for more such information.
************************************************
Buy me a Coffee:
https://www.buymeacoffee.com/scriptskart
Follow for more source codes!
For any inquiries, please contact us via email at scriptskarthelp@gmail.com
or send a direct message on Instagram.
***********************************************
[SQL, Python, R, Excel, Pandas, data analysis, data analytics, business intelligence, data cleaning, data transformation, data querying, relational databases, data frames, tabular data, analytics tools, reporting, dashboards, ETL, joins, aggregation, filtering, sorting, grouping, missing values, data preparation, analytics workflow, analytics skills, analyst tools, BI tools, data logic, cross tool comparison, learning data, analytics concepts, analytics reference, analyst learning, data operations, data skills, automation, AI, Artificial Intelligence, machine learning, python for beginners]
Hashtags- #python #DataAnalyst #DataScience #computerscience #programmers
@scripts_kart
2
ASWelcome to Episode 19 of the Python for Data Analysis Series.
In this episode, we explore one of the most important data manipulation skills every data analyst must learn — sorting and organizing data.
Whether you are working with Excel spreadsheets or Python datasets, knowing how to properly organize your data will make analysis faster, cleaner, and more efficient.
In this video you will learn:
✔ How to sort data values in ascending and descending order
✔ How to rename columns to make datasets clearer
✔ How to drop unnecessary columns to clean datasets
✔ How to perform these operations using Python Pandas
These are essential skills for data analysts, data scientists, and business analysts working with large datasets.
This tutorial is perfect for:
• Python beginners
• Data analysis learners
• Students learning Pandas
• Anyone working with Excel or datasets
By mastering these simple techniques, you can significantly improve data clarity, workflow efficiency, and analysis accuracy.
📌 Episode 19 Topic: Sorting & Organizing Data
📌 Tools Used: Python, Pandas, Excel
📌 Series: Python for Data Analysis
If you enjoy learning Data Science and Python, don’t forget to:
👍 Like the video
🔔 Subscribe for more data analysis tutorials
💬 Comment if you have questions
#Python #DataAnalysis #DataScience #DataAnalytics #Pandas #Excel #BigData #MachineLearning #BusinessAnalytics #DataSkills
@assignmentonclick
3.4K
PLSlicing Data Like a Pro! (Quartile vs Decile)
There are many ways to divide a dataset, but the two most powerful tools for any analyst are Quartiles and Deciles.
Think of it like slicing a Pizza to understand how your data is distributed! 🍕
1. The Quartile (The 4 Big Slices) 📐
"Quart" means "one-fourth." A Quartile cuts your data into 4 equal parts.
Each part covers 25% of the data.
If your test score is in the "Top Quartile," it means you performed better than 75% of the students!
2. The Decile (The 10 Precise Slices) 📏
"Deci" means "one-tenth." A Decile cuts your data into 10 equal parts.
Each part covers exactly 10% of the data.
This is much more precise. If you want to identify the "top 10%" of earners or the "bottom 10%" of performers, Deciles are your best friend.
The Main Difference: 🤔
Quartiles are great for a broad overview (High, Middle, and Low-income groups).
Deciles are for deep dives (comparing the richest 10% vs. the poorest 10%).
The Hidden Connection: 🤝
Did you know they meet in the middle? The 2nd Quartile (Q2) and the 5th Decile (D5) are exactly the same point—and they both equal the Median of your data!
Stop guessing where your data stands and start slicing it correctly! 👇
Follow @plotlab01 for more Data Science & Statistics made simple! 🚀
Quartile vs Decile, Statistics for Beginners, Data Science Basics, Measures of Position, Percentiles Explained, Business Analytics, Math for Data Science, Quartile Formula, Decile Calculation, Data Visualization, Plotlab01.
#Statistics #DataScience #MathFacts #Analytics #StudyGram
@plotlab01
2
INList methods in python tutorial
Python/data analysis tutorial
#BusinessTips #DataAnalytics #DataVisualization #LearnData
@integratedoasis
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