Data Preparation Techniques PCAD-31 Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   An analyst is preparing a spreadsheet for a stakeholder who will review it in Excel. The data has a 'Date' column currently stored as text in the format 'DD/MM/YYYY'. The analyst wants to ensure the spreadsheet follows best practices for readability. What should the analyst do?

   • ASplit the date into three separate columns: Day, Month, Year
   • BRemove the 'Date' column entirely to avoid format confusion
   • CConvert the 'Date' column to a proper datetime type and format it consistently as YYYY-MM-DD
   • DLeave the dates as text but ensure they all use the same DD/MM/YYYY format
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2. 12application · medium

   A company stores customer data in a PostgreSQL database. An analyst needs to pull a subset of records (customers from the 'West' region) into a pandas DataFrame for analysis. Which approach is most appropriate?

   • AUse pd.read_sql_query('SELECT * FROM customers WHERE region = %s', engine, params=('West',))
   • BUse pd.read_excel('customers.xlsx') and filter with df[df['region'] == 'West']
   • CUse pd.read_csv('customers.csv') after exporting the entire table manually
   • DUse pd.read_json('https://api.example.com/customers') without any parameters
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4. 13foundation · easy

   Which pandas method is used to convert a column to a different data type, such as from string to integer?

   • Adf.convert_dtypes()
   • Bdf.cast()
   • Cdf.to_numeric()
   • Ddf.astype()
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5. 14foundation · easy

   Which pandas method is used to remove duplicate rows from a DataFrame?

   • Adf.dropna()
   • Bdf.drop_duplicates()
   • Cdf.unique()
   • Ddf.duplicated()
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6. 15application · medium

   A data analyst receives a file named 'sales_data' with no extension from a colleague. Opening it in a text editor shows lines like '2024-01-15,North,4500' and '2024-01-16,South,3200'. The analyst needs to load this into pandas for analysis. What is the most appropriate first step?

   • ARename the file to 'sales_data.xlsx' and use pd.read_excel
   • BUse pd.read_json('sales_data') since the data appears structured
   • CUse pd.read_csv('sales_data') and inspect the resulting DataFrame
   • DUse pd.read_xml('sales_data') to parse the structured rows
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