110 lines
3.3 KiB
Markdown
110 lines
3.3 KiB
Markdown
# Pandas Melt
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Performs an unpivot operation (melt) on a pandas DataFrame.
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## Understanding Melt (Unpivot)
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The melt operation converts a DataFrame from **wide format** (where multiple columns represent separate variables) into **long format** (where values are stacked under a single column).
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### Example: Frozen Food Prices Table
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Given the following DataFrame:
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```
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Frozen Food Prices
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Manufacturer Pizza Burger
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---------------------------------------------------
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Aimee Italian Foods 10 12
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Olivia Cuisine 11 8
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Maddie Foods 12 7
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```
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To demonstrate **melting a single column**, we will **only transform the `"Pizza"` column** and **temporarily exclude `"Burger"`** from the output. `"Burger"` is not affected in this step because we are only selecting `"Pizza"` for melting.
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```
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Frozen Food Prices
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Manufacturer X Y
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---------------------------------------------------
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Aimee Italian Foods Pizza 10
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Olivia Cuisine Pizza 11
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Maddie Foods Pizza 12
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```
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Here:
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- Column `X` is assigned to `"Item"` (`var_name`).
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- Column `Y` is assigned to `"Price"` (`value_name`).
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- `"Pizza"` is explicitly specified in `value_vars` as the column being unpivoted.
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- `"Manufacturer"` remains unchanged and is included in `id_vars` to preserve row identity.
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The final transformed DataFrame:
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```
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Frozen Food Prices
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Manufacturer Item Price
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----------------------------------------------------
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Aimee Italian Foods Pizza 10
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Olivia Cuisine Pizza 11
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Maddie Foods Pizza 12
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```
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### Expanding the Melt Operation
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If we want to **melt both `"Pizza"` and `"Burger"` together**, we specify both columns in `value_vars`:
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```
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Frozen Food Prices
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Manufacturer Item Price
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----------------------------------------------------
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Aimee Italian Foods Pizza 10
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Olivia Cuisine Pizza 11
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Maddie Foods Pizza 12
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Aimee Italian Foods Burger 12
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Olivia Cuisine Burger 8
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Maddie Foods Burger 7
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```
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Here, we explicitly define:
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- `"Pizza"` and `"Burger"` in `value_vars`.
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- A new `"Item"` column (`var_name`), which holds the original column names (`"Pizza"`, `"Burger"`).
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- A `"Price"` column, which stores the corresponding values.
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To specify both columns in this node, provide:
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```
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Pizza,Burger
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```
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This node automatically converts the string into a list and passes it to `pandas.DataFrame.melt()`.
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Refer to the [Pandas documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.melt.html) for more information.
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## Limitation
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MultiIndex columns are not supported.
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## Input
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| Name | Data type |
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| dataframe | DataFrame |
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| id_vars | String |
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| value_vars | String |
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| var_name | String |
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| value_name | String |
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| ignore_index | Boolean |
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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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