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47 lines
1.9 KiB
47 lines
1.9 KiB
2 years ago
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# Exercise 2 Merge
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The goal of this exercise is to learn to merge DataFrames
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The logic of merging DataFrames in Pandas is quite similar as the one used in SQL.
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Here are the two DataFrames to merge:
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```python
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#df1
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df1_dict = {
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'id': ['1', '2', '3', '4', '5'],
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'Feature1': ['A', 'C', 'E', 'G', 'I'],
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'Feature2': ['B', 'D', 'F', 'H', 'J']}
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df1 = pd.DataFrame(df1_dict, columns = ['id', 'Feature1', 'Feature2'])
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#df2
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df2_dict = {
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'id': ['1', '2', '6', '7', '8'],
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'Feature1': ['K', 'M', 'O', 'Q', 'S'],
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'Feature2': ['L', 'N', 'P', 'R', 'T']}
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df2 = pd.DataFrame(df2_dict, columns = ['id', 'Feature1', 'Feature2'])
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```
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1. Merge the two DataFrames to get this output:
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| | id | Feature1_x | Feature2_x | Feature1_y | Feature2_y |
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|---:|-----:|:-------------|:-------------|:-------------|:-------------|
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| 0 | 1 | A | B | K | L |
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| 1 | 2 | C | D | M | N |
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2. Merge the two DataFrames to get this output:
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| | id | Feature1_df1 | Feature2_df1 | Feature1_df2 | Feature2_df2 |
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|---:|-----:|:---------------|:---------------|:---------------|:---------------|
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| 0 | 1 | A | B | K | L |
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| 1 | 2 | C | D | M | N |
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| 2 | 3 | E | F | nan | nan |
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| 3 | 4 | G | H | nan | nan |
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| 4 | 5 | I | J | nan | nan |
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| 5 | 6 | nan | nan | O | P |
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| 6 | 7 | nan | nan | Q | R |
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| 7 | 8 | nan | nan | S | T |
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