Fictitious Names¶

Introduction:¶

This time you will create a data again

Special thanks to Chris Albon for sharing the dataset and materials. All the credits to this exercise belongs to him.

In order to understand about it go here.

Step 1. Import the necessary libraries¶

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Step 2. Create the 3 DataFrames based on the following raw data¶

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raw_data_1 = {
        'subject_id': ['1', '2', '3', '4', '5'],
        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 
        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}

raw_data_2 = {
        'subject_id': ['4', '5', '6', '7', '8'],
        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], 
        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}

raw_data_3 = {
        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],
        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}

Step 3. Assign each to a variable called data1, data2, data3¶

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Step 4. Join the two dataframes along rows and assign all_data¶

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Step 5. Join the two dataframes along columns and assing to all_data_col¶

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Step 6. Print data3¶

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Step 7. Merge all_data and data3 along the subject_id value¶

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Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2¶

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Step 9. Merge all values in data1 and data2, with matching records from both sides where available.¶

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