101 lines
1.8 KiB
Markdown
101 lines
1.8 KiB
Markdown
# Pandas Pivot
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Computes a pivot table from a pandas DataFrame.
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## Note on a pivot table
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A pivot table is easier to understand if we start off from the table itself:
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In the below DataFrame:
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```
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Column labels
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X Y Z
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Index
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A 1 2 3
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B 4 5 6
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C 7 8 9
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```
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We have index `["A", "B", "C"]` and column labels `["X", "Y", "Z"]`.
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Values in this DataFrame can be accessed by specifying the index and the column label.
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For example:
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```python
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df.loc["A", "X"] # 1
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df.loc["A", "Y"] # 2
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```
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Now, let's restructure the table by converting column labels into row values:
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```
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A X 1
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A Y 2
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A Z 3
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B X 4
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B Y 5
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B Z 6
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C X 7
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C Y 8
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C Z 9
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```
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Let's add a column label to each of these columns:
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```
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R C V
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--------
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A X 1
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A Y 2
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A Z 3
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B X 4
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B Y 5
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B Z 6
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C X 7
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C Y 8
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C Z 9
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```
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Now, the first two columns uniquely identify the value in the third column.
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**Pivoting** is the reverse of this operation.
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- You start with the table above and produce the original one.
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- In pandas, this means telling it to use:
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- R column: `A`, `B`, `C` as the index (row labels).
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- C column: `X`, `Y`, `Z` as the column labels.
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- V column: The numbers (`1, 2, 3, ...`) as the values for each cell indexed by `[A, B, or C]` and `[X, Y, or Z]`.
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This can be done using:
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```python
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df.pivot(index="R", columns="C", values="V")
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```
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The result:
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```
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X Y Z
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A 1 2 3
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B 4 5 6
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C 7 8 9
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```
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Now the data is back to its original, structured form.
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## Input
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| Name | Data type |
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|---|---|
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| dataframe | DataFrame |
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| index | String |
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| columns | String |
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| values | String |
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## Output
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| Data type |
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| DataFrame |
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<HR>
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Category: Transformation
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