Challenge: Clean Sales Data
Data cleaning is a foundational step in business data analysis. Without careful cleaning, your analyses may be skewed by missing values or inconsistent formatting, leading to inaccurate insights and decisions. For business analysts, ensuring that sales records are complete and standardized—such as by filling in missing sales numbers and making product names consistent—is essential for producing reliable reports and recommendations. Small inconsistencies, like varying capitalization or blank fields, can have a significant impact when aggregating or comparing data across products and periods. By mastering these cleaning techniques, you set the stage for more advanced analysis and trustworthy business intelligence.
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You are given a list of sales records, each as a dictionary with keys 'date', 'product', 'units_sold', and 'revenue'. Some records may have missing values (None) for 'units_sold' or 'revenue', and product names may use inconsistent capitalization. Your function must:
- Replace any missing
'units_sold'or'revenue'values with0. - Standardize all
'product'names to title case (first letter uppercase, others lowercase). - Return a new list of cleaned records.
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What are some common data cleaning techniques used by business analysts?
Can you give examples of how inconsistent data can affect business analysis?
How can I automate the data cleaning process for large datasets?
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Data cleaning is a foundational step in business data analysis. Without careful cleaning, your analyses may be skewed by missing values or inconsistent formatting, leading to inaccurate insights and decisions. For business analysts, ensuring that sales records are complete and standardized—such as by filling in missing sales numbers and making product names consistent—is essential for producing reliable reports and recommendations. Small inconsistencies, like varying capitalization or blank fields, can have a significant impact when aggregating or comparing data across products and periods. By mastering these cleaning techniques, you set the stage for more advanced analysis and trustworthy business intelligence.
Swipe to start coding
You are given a list of sales records, each as a dictionary with keys 'date', 'product', 'units_sold', and 'revenue'. Some records may have missing values (None) for 'units_sold' or 'revenue', and product names may use inconsistent capitalization. Your function must:
- Replace any missing
'units_sold'or'revenue'values with0. - Standardize all
'product'names to title case (first letter uppercase, others lowercase). - Return a new list of cleaned records.
Рішення
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