> For the complete documentation index, see [llms.txt](https://fennaf.gitbook.io/bfvm19prog1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fennaf.gitbook.io/bfvm19prog1/data-wrangling/reshape-with-melt.md).

# Reshape with melt

The pandas.DataFrame.melt() function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id\_vars), while all other columns, considered measured variables (value\_vars). The function “unpivotes” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’. 

To demonstrate this we will work with the EEG brain dataset. The values are in the X\<number> columns, the variable of interest is the 'y'&#x20;

```python
import numpy as np
import pandas as pd

from bokeh.plotting import figure, output_file, show
df = pd.read_csv('data/eeg_data.csv').rename(columns={"Unnamed: 0": "ID"})
df = df.drop(columns='ID')
df.head()
```

|   | X1   | X2   | X3  | X4   | X5  | X6  | X7   | X8   | X9  | X10 | ... | X170 | X171 | X172 | X173 | X174 | X175 | X176 | X177 | X178 | y |
| - | ---- | ---- | --- | ---- | --- | --- | ---- | ---- | --- | --- | --- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | - |
| 0 | 135  | 190  | 229 | 223  | 192 | 125 | 55   | -9   | -33 | -38 | ... | -17  | -15  | -31  | -77  | -103 | -127 | -116 | -83  | -51  | 4 |
| 1 | 386  | 382  | 356 | 331  | 320 | 315 | 307  | 272  | 244 | 232 | ... | 164  | 150  | 146  | 152  | 157  | 156  | 154  | 143  | 129  | 1 |
| 2 | -32  | -39  | -47 | -37  | -32 | -36 | -57  | -73  | -85 | -94 | ... | 57   | 64   | 48   | 19   | -12  | -30  | -35  | -35  | -36  | 5 |
| 3 | -105 | -101 | -96 | -92  | -89 | -95 | -102 | -100 | -87 | -79 | ... | -82  | -81  | -80  | -77  | -85  | -77  | -72  | -69  | -65  | 5 |
| 4 | -9   | -65  | -98 | -102 | -78 | -48 | -16  | 0    | -21 | -59 | ... | 4    | 2    | -12  | -32  | -41  | -65  | -83  | -89  | -73  | 5 |

5 rows × 179 columns

We can melt this by the function melt. It will keep the 'y' value and put all the other columns in the variable column

```python
dfm = df.melt(id_vars=['y'])
dfm.head()
 
```

|   | y | variable | value |
| - | - | -------- | ----- |
| 0 | 4 | X1       | 135   |
| 1 | 1 | X1       | 386   |
| 2 | 5 | X1       | -32   |
| 3 | 5 | X1       | -105  |
| 4 | 5 | X1       | -9    |

This might be handy if I want for instance to groupby y-value to discover the differences in counts, mean or standard deviation. I also can make a graphical overview&#x20;

```python
dfm.y.value_counts()
dfm.groupby('y').std()
dfm.groupby('y').mean()
```

```python
import seaborn as sns
sns.violinplot(x=dfm.y, y=dfm.value)
```

![violinplot of dfm](/files/-MKjLqT51omv7_3EUzLh)


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