import pandas as pd
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
'age': [42, 52, 36, 24, 73],
'preTestScore': [4, 24, 31, 2, 3],
'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name | last_name | age | preTestScore | postTestScore | |
---|---|---|---|---|---|
0 | Jason | Miller | 42 | 4 | 25 |
1 | Molly | Jacobson | 52 | 24 | 94 |
2 | Tina | Ali | 36 | 31 | 57 |
3 | Jake | Milner | 24 | 2 | 62 |
4 | Amy | Cooze | 73 | 3 | 70 |
5 rows × 5 columns
raw_data_2 = {'first_name': ['Sarah', 'Gueniva', 'Know', 'Sara', 'Cat'],
'last_name': ['Mornig', 'Jaker', 'Alom', 'Ormon', 'Koozer'],
'age': [53, 26, 72, 73, 24],
'preTestScore': [13, 52, 72, 26, 26],
'postTestScore': [82, 52, 56, 234, 254]}
df_2 = pd.DataFrame(raw_data_2, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df_2
first_name | last_name | age | preTestScore | postTestScore | |
---|---|---|---|---|---|
0 | Sarah | Mornig | 53 | 13 | 82 |
1 | Gueniva | Jaker | 26 | 52 | 52 |
2 | Know | Alom | 72 | 72 | 56 |
3 | Sara | Ormon | 73 | 26 | 234 |
4 | Cat | Koozer | 24 | 26 | 254 |
5 rows × 5 columns
raw_data_3 = {'first_name': ['Sarah', 'Gueniva', 'Know', 'Sara', 'Cat'],
'last_name': ['Mornig', 'Jaker', 'Alom', 'Ormon', 'Koozer'],
'postTestScore_2': [82, 52, 56, 234, 254]}
df_3 = pd.DataFrame(raw_data_3, columns = ['first_name', 'last_name', 'postTestScore_2'])
df_3
first_name | last_name | postTestScore_2 | |
---|---|---|---|
0 | Sarah | Mornig | 82 |
1 | Gueniva | Jaker | 52 |
2 | Know | Alom | 56 |
3 | Sara | Ormon | 234 |
4 | Cat | Koozer | 254 |
5 rows × 3 columns