How important is data cleaning before visualization?
An example of an AI chatbot conversation about data cleaning before visualization.
How important is data cleaning before visualization?
Data cleaning is crucial before visualization because it ensures accuracy and clarity in your insights.
What are some common data cleaning techniques?
Common techniques include removing duplicates, handling missing values, and correcting inconsistencies.
Can you give me an example of how to handle missing values?
Sure! You can handle missing values by either removing the affected rows or filling them with the mean or median.
What tools can I use for data cleaning?
Popular tools include Python libraries like Pandas, R, and software like OpenRefine.
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