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# 1. Import pandas as pd and read the CFPB CSV into a dataframe assigned variable 'df'.
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# 2. Select a single column via bracket notation []. What is the type of the column?
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# 3. Select two or more columns using double bracket notation (list inside of brackets).
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# 4. Use the dataframe's loc[] attribute to get the first row of the df by index.
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# 5. Use iloc[] to get the last row of the dataframe by number.
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# 6. # Use df[] notation to get a True/False series for 'Product == Mortgage'. Assign to 'mask', then show.
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# 7. Use boolean indexing by putting the mask inside df[] notation to filter out everything but mortgage.
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# 8. Do the mask operation in steps 6 and 7 in a single line.
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# 9. Use df[(condition_1) | (condition_2)] notation to get Mortgage and Credit card products.
# Hint: separate conditions must be wrapped in () (and use | or & like notation).
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# 10. Drop a row from your df.
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# 11. Drop a column from your df.
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# 12. Use your df's loc[] attribute to get specific rows and columns using loc[NUMBER, NUMBER] notation.
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# 13. User your dataframe's set_index() method to use a ['Product', 'Sub-product']. Then sort_index().
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# 14. Use loc[] with a number like 1000000, and add a row to the dataframe.
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# 15. Add a column to the dataframe named "Custom" that copies the values in another column.
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# 16. Select based on a condition using the loc[CONDITION, COLUMN_NAME] notation.
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# 17. Select using loc[] and a list of index values.
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# 18. Use the [CONDITIONAL] selection to get all complaints from 2015.