How Can I Transform Columns into Rows Using Pandas melt()?

Convert Columns into Rows with Pandas

In data analysis, it can be useful to restructure datasets to improve readability and analysis. A common transformation involves converting columns into rows. This article addresses how to perform this operation using the Pandas library.

Consider a dataset with location and date-specific information stored in column headers, as shown below:

| location | name | Jan-2010 | Feb-2010 | March-2010 |
|---|---|---|---|---|
| A        | "test" | 12 | 20 | 30 |
| B        | "foo"  | 18 | 20 | 25 |

The goal is to reshape the data into a format where each date corresponds to a row, as follows:

| location | name | Date | Value |
|---|---|---|---|
| A        | "test" | Jan-2010 | 12 |
| A        | "test" | Feb-2010 | 20 |
| A        | "test" | March-2010 | 30 |
| B        | "foo"  | Jan-2010 | 18 |
| B        | "foo"  | Feb-2010 | 20 |
| B        | "foo"  | March-2010 | 25 |

To achieve this transformation, Pandas provides the melt function. Simply apply melt to the DataFrame, specifying the columns to keep as row identifiers (id_vars) and the column headers for the new columns (var_name and value_name).

import pandas as pd

df.melt(id_vars=["location", "name"],
        var_name="Date",
        value_name="Value")

For Pandas versions prior to 0.20, a slightly different approach is required, involving a combination of melt and sort:

df2 = pd.melt(df,
                  id_vars=["location", "name"], 
                  var_name="Date",
                  value_name="Value")

df2 = df2.sort(["location", "name"])

Remember to reset the index to ensure a clean output:

df2.reset_index(drop=True)

This technique offers a convenient way to reshape dataframes, facilitating analysis and presentation of tabular data.

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