5-1: Create two data frames based on the following figures.
In [2]:
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import pandas as pd
data1= pd.DataFrame({
'id': [10, 20, 30, 40,50],
'name': ['John', 'Sam', 'Elizabeth', 'Ryan', 'Liam'],
})
data2 = pd.DataFrame({
'id': [10, 20, 30, 40],
'age': [42, 31, 28, 53],
'gender': ['male', 'male', 'female', 'male']
})
import pandas as pd data1= pd.DataFrame({ 'id': [10, 20, 30, 40,50], 'name': ['John', 'Sam', 'Elizabeth', 'Ryan', 'Liam'], }) data2 = pd.DataFrame({ 'id': [10, 20, 30, 40], 'age': [42, 31, 28, 53], 'gender': ['male', 'male', 'female', 'male'] })
5-2: Perform merging of the data frames using left, right, inner, and outer joins.
In [3]:
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pd.merge(data1, data2, on='id', how='left')
pd.merge(data1, data2, on='id', how='left')
Out[3]:
| id | name | age | gender | |
|---|---|---|---|---|
| 0 | 10 | John | 42.0 | male |
| 1 | 20 | Sam | 31.0 | male |
| 2 | 30 | Elizabeth | 28.0 | female |
| 3 | 40 | Ryan | 53.0 | male |
| 4 | 50 | Liam | NaN | NaN |
In [4]:
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pd.merge(data1, data2, on='id', how='right')
pd.merge(data1, data2, on='id', how='right')
Out[4]:
| id | name | age | gender | |
|---|---|---|---|---|
| 0 | 10 | John | 42 | male |
| 1 | 20 | Sam | 31 | male |
| 2 | 30 | Elizabeth | 28 | female |
| 3 | 40 | Ryan | 53 | male |
In [5]:
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pd.merge(data1, data2, on='id', how='inner')
pd.merge(data1, data2, on='id', how='inner')
Out[5]:
| id | name | age | gender | |
|---|---|---|---|---|
| 0 | 10 | John | 42 | male |
| 1 | 20 | Sam | 31 | male |
| 2 | 30 | Elizabeth | 28 | female |
| 3 | 40 | Ryan | 53 | male |
In [6]:
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pd.merge(data1, data2, on='id', how='outer')
pd.merge(data1, data2, on='id', how='outer')
Out[6]:
| id | name | age | gender | |
|---|---|---|---|---|
| 0 | 10 | John | 42.0 | male |
| 1 | 20 | Sam | 31.0 | male |
| 2 | 30 | Elizabeth | 28.0 | female |
| 3 | 40 | Ryan | 53.0 | male |
| 4 | 50 | Liam | NaN | NaN |