What are some common mistakes in exploratory data analysis?
An example of an AI chatbot conversation about common mistakes in exploratory data analysis.
What are some common mistakes in exploratory data analysis?
Some common mistakes include not understanding the data, skipping data cleaning, and ignoring outliers.
Can you elaborate on why ignoring outliers is a mistake?
Ignoring outliers can lead to misleading results, as they may represent important variations or errors in data.
What steps can I take to avoid these mistakes?
To avoid mistakes, ensure thorough data cleaning, visualize data distributions, and always question your assumptions.
Are there any tools that can help with EDA?
Yes, tools like Pandas, Matplotlib, and Seaborn in Python are great for performing exploratory data analysis.
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