Data-Analysis-and-Manipulation-with-Python

you'll find various examples of how to work with data in Python, ranging from data preprocessing, exploration, and analysis to advanced visualization techniques. The goal is to provide useful scripts and Jupyter notebooks for anyone interested in data science or data manipulation tasks.

Data Cleaning: Handling missing values, outliers, duplicates, and inconsistent data types

Data Manipulation: Filtering, grouping, merging, reshaping, and aggregating datasets

Exploratory Data Analysis (EDA): Descriptive statistics, correlation analysis, and distribution plots

Data Visualization: Creating clear and informative charts and graphs to understand data trends

Feature Engineering: Creating new features from existing data, including scaling, encoding, and transformation

Time Series Analysis: Analyzing temporal data with methods for handling seasonality and trends

Text Analysis: Basic Natural Language Processing (NLP) tasks such as tokenization, sentiment analysis, and word cloud generation