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They introduce two primary data structures, Series and Data Frames, which facilitate handling structured data seamlessly. With Pandas, you can easily clean, transform, and analyse data. Its flexibility allows you to produce high-quality graphs and charts, making it perfect for exploratoryDataAnalysis.
DataWrangling and Cleaning Interviewers may present candidates with messy datasets and evaluate their ability to clean, preprocess, and transform data into usable formats for analysis. However, there are a few fundamental principles that remain the same throughout. Here is a brief description of the same.
For DataAnalysis you can focus on such topics as Feature Engineering , DataWrangling , and EDA which is also known as ExploratoryDataAnalysis. Feature Engineering plays a major part in the process of model building.
Kaggle datasets) and use Python’s Pandas library to perform data cleaning, datawrangling, and exploratorydataanalysis (EDA). Extract valuable insights and patterns from the dataset using data visualization libraries like Matplotlib or Seaborn.
D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. DataWrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.
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