Fictitious Names

Check out Fictitious Names Exercises Video Tutorial to watch a data scientist go through the exercises

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 to here.

Step 1. Import the necessary libraries

In [1]:

import pandas as pd

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

In [2]:

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

In [12]:

data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])
data2 = pd.DataFrame(raw_data_2, columns = ['subject_id', 'first_name', 'last_name'])
data3 = pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])

data3

Out[12]:

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

In [9]:

all_data = pd.concat([data1, data2])
all_data

Out[9]:

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

In [10]:

all_data_col = pd.concat([data1, data2], axis = 1)
all_data_col

Out[10]:

Step 6. Print data3

In [13]:

data3

Out[13]:

Step 7. Merge all_data and data3 along the subject_id value

In [15]:

pd.merge(all_data, data3, on='subject_id')

Out[15]:

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

In [16]:

pd.merge(data1, data2, on='subject_id', how='inner')

Out[16]:

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

In [17]:

pd.merge(data1, data2, on='subject_id', how='outer')

Out[17]: