Analyzing Financial Strength & TAD Impact on Total Assets with Python

𝐀 𝐬𝐦𝐚𝐥𝐥 𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐩𝐚𝐧𝐝𝐚𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞𝐝 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐭𝐢𝐦𝐞 While working with a dataset in Python today, I came across something simple but very useful — value_counts() in pandas. Instead of writing multiple filters or loops just to see how frequently different values appear in a column, value_counts() gives a quick frequency breakdown instantly. For example, if you want to see how many records belong to each category, city, or product type, one line can show the entire distribution. It’s a small function, but it makes exploring a new dataset much faster. Slowly realizing that data analysis is really about knowing these small but powerful tools. #Python #Pandas #DataAnalytics #LearningJourney

In real-world data workflows, simple Python list methods do a lot of the heavy lifting. append(), insert(), pop(), count(), index(), reverse(), clear() From quick data cleaning to handling transformations, these basics help keep code efficient and reliable. Strong fundamentals always scale. #Python #DataScience #Data

Running statistical analysis with Python 📊 Used Statsmodels to perform ANOVA testing to examine the relationship between Total Liabilities and TAD (Total Asset Dummy) in the financial dataset. Exploring how statistical models help uncover insights from financial data. #Python #DataAnalytics #Statsmodels #FinancialAnalysis #LearningByDoing 🚀

🏠 Built a Housing Price Prediction model using Linear Regression to estimate property prices based on housing features. Performed EDA, feature analysis, and model evaluation using Python and Scikit-learn. Using data to understand real estate price patterns 📈 #Oasis Infobyte #MachineLearning #Python #DataScience #LinearRegression

📊 Exploring Data Relationships using Python! Implemented Correlation Matrix Visualization using Heatmaps and Pair Plots to understand relationships between features in the California Housing Dataset. Also applied Principal Component Analysis (PCA) to reduce dimensionality from 4 features to 2 in the Iris dataset. Tools used: Python | Pandas | Seaborn | Matplotlib | Scikit-learn #DataScience #MachineLearning #Python #DataVisualization #PCA #AI

𝐐𝐮𝐢𝐜𝐤 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐭𝐨𝐝𝐚𝐲 𝐰𝐡𝐢𝐥𝐞 𝐮𝐬𝐢𝐧𝐠 𝐩𝐚𝐧𝐝𝐚𝐬 While practicing data analysis in Python, I realized how useful groupby() in pandas actually is. At first it looked confusing, but once it clicked, it felt like one of the most practical tools for analysis. You can quickly split data by categories and calculate things like averages, counts, or totals for each group. For example, instead of manually filtering a dataset multiple times, a simple groupby() can show things like average sales per city or number of users per category in seconds. Small function, but it really shows how powerful pandas can be when working with real datasets. #Python #Pandas #DataAnalytics #LearningByDoing

Python moment that aged me 10 years: Spent 2 hours debugging why my Pandas merge was returning empty. merged = df1.merge(df2, on='user_id', how='inner') The problem? Column was actually named 'User_ID' (capital U and I). Python is case-sensitive. I am not emotionally stable. Now I triple-check column names like my life depends on it. #Python #Pandas #DataEngineering #Debugging #TechLife #Humour #WomenInTech #MomInTech #DataAnalyst #LearningEveryday

Still slicing DataFrames the hard way? Here are the three essentials: grab one column with df['col'], pick multiple with df[['a','b']], then filter rows by building a boolean mask like df[df['age'] >= 18]. Master these and inspecting/subsetting data becomes effortless.#pandas #python #datascience #dataanalysis

One practical habit that improved my data analysis workflow Before starting any analysis, I create a quick data profiling summary In Python using pandas it takes less than a minute 🗯️ This instantly shows: • statistical distribution • missing data ratio • columns with low or high cardinality It helps me detect problems in the dataset before building any model or visualization #DataAnalysis #Python #DataScience

🐍 Day 75 — Variance Day 75 of #python365ai 📐 Variance measures how spread out data is. Example: np.var(data) 📌 Why this matters: Variance helps understand how much values differ from the mean. 📘 Practice task: Calculate the variance of a small dataset. #python365ai #Variance #DataScience #Python