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🐍 𝟓 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐍𝐞𝐞𝐝𝐬 𝐢𝐧 𝐚𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 🐍 🔸 𝐏𝐚𝐧𝐝𝐚𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 Messy data is a common challenge in any organization. Pandas has become my go-to for sorting it all out! Pandas helps me 𝐜𝐥𝐞𝐚𝐧, 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐞, 𝐚𝐧𝐝 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐝𝐚𝐭𝐚 efficiently, making it ready for analysis. 🔸 𝐍𝐮𝐦𝐏𝐲 𝐟𝐨𝐫 𝐐𝐮𝐢𝐜𝐤 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 When we need to calculate things quickly, like averages or totals from big datasets,  NumPy is a lifesaver. Whether it’s 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐝𝐚𝐭𝐚, 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐚𝐫𝐫𝐚𝐲𝐬, 𝐨𝐫 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬. NumPy helps me perform calculations in seconds. 🔸 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛 & 𝐒𝐞𝐚𝐛𝐨𝐫𝐧 Turning raw data into easy-to-understand charts is key in my role. I use Matplotlib and Seaborn to create visualizations that help my stakeholders easily understand the insights. 🔸 𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 Sometimes I need to pull specific data from a database for analysis. By using SQL with Python, I can directly fetch the data I need. For example, if I need customer data for a report, I can run a quick 𝐒𝐐𝐋 𝐪𝐮𝐞𝐫𝐲, 𝐜𝐨𝐦𝐛𝐢𝐧𝐞 𝐢𝐭 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧, and get the analysis done faster. 🔸 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 Sometimes code doesn’t work as expected, which can slow down progress. Using 𝐭𝐫𝐲 and 𝐞𝐱𝐜𝐞𝐩𝐭 to catch errors early helps me avoid bigger issues later. For instance, if I'm running a script that imports data, I use error handling to catch problems right away #Python #DataAnalyst #DataAnalytics #DataScience #DataVisualization #PythonForData

Hands-On Project – Basic Exploratory Data Analysis (EDA) with Python Libraries Today’s focus is on applying your data science knowledge through a hands-on project. You'll perform a basic Exploratory Data Analysis (EDA) using Python’s powerful libraries. This step-by-step project will help you get comfortable with real-world data exploration, uncovering insights, and preparing data for future modeling. 1. Key Python Libraries for EDA: ◾ Pandas: A versatile library for data manipulation, Pandas makes it easy to load, clean, and analyze data using data frames. ◾ Matplotlib: A foundational visualization library that helps create static, animated, and interactive plots. ◾ Seaborn: Built on top of Matplotlib, Seaborn offers more advanced and aesthetic visualizations for data exploration. 2. Steps for Basic EDA in Python: ◾ Data Loading: Use Pandas to load your dataset from CSV or other file formats into a data frame. 🔘 EXAMPLE: import pandas as pd data = pd.read_csv('file.csv') ◾ Data Cleaning: Identify and handle missing values using Pandas’ isnull() or fillna() functions. 🔘 EXAMPLE: data.fillna(0, inplace=True) ◾ Descriptive Statistics: Summarize your data with basic statistics such as mean, median, and standard deviation. 🔘 EXAMPLE: data.describe() ◾ Data Visualization: Create visualizations like histograms, scatter plots, and box plots using Matplotlib and Seaborn. 🔘 EXAMPLE: import matplotlib.pyplot as plt import seaborn as sns sns.histplot(data['column']) plt.show() 3. What You'll Learn: ◽ Data Insights: Uncover patterns, trends, and relationships within your dataset. ◽ Data Preparation: Clean and prepare data for advanced analysis and modeling. ◽ Hands-On Experience: Solidify your understanding of Python libraries like Pandas, Matplotlib, and Seaborn through practical application. This hands-on project will empower you with the skills to perform EDA, a fundamental step in any data science process. Whether you're a beginner or experienced, mastering EDA will enhance your data analysis capabilities. #EDA #PythonEDA #DataScienceProject #DataVisualization

Day 51/75 day Data Analysis Challenge :Time series analysis with pandas What is Time Series Data? Time series data is a sequential arrangement of data points organized in consecutive time order. Time-series analysis consists of methods for analyzing time-series data to extract meaningful insights and other valuable characteristics of the data. Importance of time series analysis Time-series data analysis is becoming very important in so many industries, like financial industries, pharmaceuticals, social media companies, web service providers, research, and many more. To understand the time-series data, visualization of the data is essential. In fact, any type of data analysis is not complete without visualizations, because one good visualization can provide meaningful and interesting insights into the data. T ime Series Data Visualization using Python We will use Python libraries for visualizing the data. The link for the dataset can be found here. We will perform the visualization step by step, as we do in any time-series data project. I mporting the Libraries We will import all the libraries that we will be using throughout this article in one place so that do not have to import every time we use it this will save both our time and effort. Numpy – A Python library that is used for numerical mathematical computation and handling multidimensional ndarray, it also has a very large collection of mathematical functions to operate on this array. Pandas – A Python library built on top of NumPy for effective matrix multiplication and dataframe manipulation, it is also used for data cleaning, data merging, data reshaping, and data aggregation. Matplotlib – It is used for plotting 2D and 3D visualization plots, it also supports a variety of output formats including graphs for data. #75_Days_of_Data_Analysis #Day_51 #Python #DataModeling #DataAnalysis #TechSkills #LearingJourney #BusinessIntelligence #PythonTips #PythonProgramming #DataSciencewithPython  #DataInsights #EntriElevate #Entri #Dr.JithaPNair

DATACAMP - Introduction to Data Visualization with Seaborn - Associate Data Scientist in Python track 4h training Scatterplot(), countplot() Subplots hue, hue_order, palette Relplot() + kind Subplots col, row, col_wrap, col/row_order Customizing subgroups (color, size, style, transparency(alpha), dashes, markers Lineplots() Confidence interval and standard deviation (ci) Catplot() + category_order Boxplot() + sym(outliers), changing whiskers Pointplots() + join, estimator, capsize, ci Changing plot style, color and scale sns.set_style(), sns.set_palette(), sns.set_context() Adding titles and labels FacetGrid vs AxesSubplot object g.fig.suptitle() vs g.set_title() g.set(xlabel=, ylabel=) plt.xticks(rotation=90) #datacamp #data #python #seaborn

A Quick Data Analysis and Simple Data Science Example Introduction This guide provides a comprehensive overview of conducting basic data analysis using Python, with a practical example based on the popular Titanic dataset from Kaggle. This tutorial is ideal for beginners who are interested in data science and machine learning. Step: Setting Up Your Environment Ensure Python and Jupyter notebooks are installed on your computer via Anaconda for an interactive coding environment. Step: Install Required Libraries !pip install numpy pandas matplotlib seaborn scikit-learn Step: Load the Data import pandas as pd data = pd.read_csv('path/to/your/titanic.csv') data.head() Step: Data Exploration data.info() data.describe() Step: Data Cleaning data['Age'].fillna(data['Age'].mean(), inplace=True) data.drop(['Cabin', 'Ticket'], axis=1, inplace=True) Step: Data Visualization import seaborn as sns import matplotlib.pyplot as plt sns.countplot(x='Survived', data=data) plt.show() Step: Model Building from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # Prepare data for training X = data.drop('Survived', axis=1) y = data['Survived'] # Split data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Initialize and train the logistic regression model model = LogisticRegression() model.fit(X_train, y_train) # Predict and evaluate the model predictions = model.predict(X_test) print(f"Accuracy: {accuracy_score(y_test, predictions)}") Step: Conclusion Review the model’s performance and consider ways to enhance the analysis with more complex models or techniques. Watch for our next article on this! #pythonprogramming #datascience

🌟 Day 11 of #LearningMachineLearningInPublic 🌟 Today, I delved into the fascinating world of data visualization with Matplotlib, exploring various methods to bring my data to life! 🎯 Today's Highlights: 1. Introduction to Matplotlib: Took my first steps into creating captivating visualizations with Matplotlib, a powerful plotting library in Python. 2. Seamless Integration with Jupyter Notebook: Leveraged `%matplotlib inline` to seamlessly display plots within Jupyter Notebook, enhancing the interactive data exploration experience. 3. Exploring Different Plotting Methods: Experimented with multiple plotting methods, including line plots, scatter plots, bar graphs, histograms, and subplots, unlocking endless possibilities for visualizing data. 🔍 Key Learnings: - Data Preparation: Prepared data for visualization, understanding the importance of clean and well-structured data. - Plot Customization: Explored various customization options to tailor plots according to specific requirements, including titles, labels, and plot sizes. - Exploratory Data Analysis: Embarked on an exciting journey of exploratory data analysis, uncovering insights and trends hidden within the data through visualization. 💡 Key Takeaways: - Visualization is a powerful tool for data exploration and communication, enabling us to gain valuable insights and convey complex information effectively. - Matplotlib offers a wide range of plotting functionalities, making it a go-to choice for data visualization tasks in Python. 🚀 Next Steps: - Dive deeper into advanced visualization techniques, such as interactive plots and geographic mapping. - Apply visualization skills to real-world datasets and projects, gaining practical experience and honing my data storytelling abilities. The journey of learning and exploration continues, fueled by curiosity and a passion for data-driven insights! 💻📊 #MachineLearning #Python #DataScience #Matplotlib #DataVisualization #LearningInPublic

𝐖𝐡𝐞𝐧 𝐢𝐭 𝐜𝐨𝐦𝐞𝐬 𝐭𝐨 𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠🧑🏫 𝐚𝐧𝐝 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠📊 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐚 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭, 𝐭𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐦𝐚𝐧𝐲 𝐨𝐩𝐭𝐢𝐨𝐧𝐬 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞. From 𝑀𝑎𝑡𝑝𝑙𝑜𝑡𝑙𝑖𝑏 to 𝑆𝑒𝑎𝑏𝑜𝑟𝑛, each package has its own strengths. For simple interactive dashboards🎛️, I personally used 𝑃𝑙𝑜𝑡𝑙𝑦 and 𝐷𝑎𝑠ℎ with great success to track manufacturing process. Recently, I came across an amazing article📄 that describes additional resources for creating interactive dashboards in data science. It is amazing🪄 how many cool things could be made with ease nowadays. Take a few minutes to read the article, let me know what you think🤝. #datascience #dataanalysis #businessanalytics #business #manufacturing #python #datavisualization #js #jsdeveloper #webdevelopment #softwaredevelopment #hr #management #manufacturing #productioncontrol 👉 https://lnkd.in/gmrv6GNm

Dear Data enthusiasts, 🚀Important Python Topics for Data Analysts🚀 1️⃣ Python Basics:- 🔹Variables and Data Types 🔹Control Structures (if, for, while) 🔹Functions and Lambda Expressions 🔹List Comprehensions 2️⃣ Data Types:- 🔹Numeric types:- int, float, complex 🔹Sequence types:- list, tuple, range 🔹Text type:- str 🔹Mapping type:- dict 🔹Set types:- set, frozen set 🔹Boolean type:- bool 🔹Binary types: bytes , byte array, memory view. 3️⃣ Data Manipulation with pandas:- 🔹DataFrames and Series. 🔹Indexing and Selecting Data. 🔹Merging, joining, and Concatenating Data. 🔹GroupBy Operations. 🔹Data Cleaning and preprocessing. 4️⃣ Data Visualization:- 🔹Matplotlib:- Basic plotting and Customization. 🔹Seaborn:- Statistical plots and Aesthetic visualizations. 5️⃣ Working with NumPy:- 🔹Array Operations and manipulations. 🔹Indexing and Slicing. 🔹Broadcasting. 🔹Linear Algebra Operations. 6️⃣ Handling Missing Data:- 🔹Detecting Missing Values. 🔹Imputation Techniques. 🔹Handling Missing Data with pandas. 7️⃣ Date and Time Handling:- 🔹Date and Time Manipulation with datetime and pandas. 🔹Handling Time Series Data. 🔹Time Zone Conversion. ✅Follow Korrapati Jaswanth for such more content!! #Python #DataAnalysis #DataScience #Pandas #NumPy #DataVisualization #Automation #DataCollection

Are you effectively visualizing your data? Data visualization is a powerful tool that transforms raw data into intuitive visuals, making it easier to comprehend complex information and identify patterns. This document shows how to leverage Pandas, a versatile library in Python, for data visualization. Pandas not only excels in data manipulation but also provides robust capabilities for creating various types of plots, such as line charts, bar charts, histograms, and scatter plots. By integrating Pandas with Matplotlib and Seaborn, you can produce even more sophisticated and interactive visualizations. Here are a few key takeaways -- ☑ Line Charts - Ideal for tracking changes over time. Pandas makes it simple to plot time series data with just a few lines of code ☑ Bar Charts - Great for comparing categories. Use Pandas to quickly generate bar charts that highlight differences between groups ☑ Histograms - Perfect for understanding the distribution of a dataset. Pandas allows you to create histograms that reveal the frequency of data points within different ranges ☑ Scatter Plots - Useful for examining relationships between variables. With Pandas, you can easily plot scatter plots to visualize correlations and trends The beauty of using Pandas for data visualization lies in its seamless integration with data analysis workflows. You can manipulate, analyze, and visualize your data all within the same framework, streamlining the entire process. How are you using data visualization to drive your decision-making? Thanks to Abhishek Mishra #data #datavisualization #theravitshow