How to Transform Pandas DataFrame Columns into Rows with a 'Date' and 'Value' Column?

Convert Columns into Rows with Pandas

To reshape a dataset from columns to rows, where each column represents a different date and the desired output requires a "Date" column and "Value" column, use the Pandas melt function.

Solution:

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

Example:

import pandas as pd

df = pd.DataFrame(
    {
        "location": ["A", "B"],
        "name": ["test", "foo"],
        "Jan-2010": [12, 18],
        "Feb-2010": [20, 20],
        "March-2010": [30, 25],
    }
)

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

print(result)

Output:

  location  name        Date  Value
0        A  test    Jan-2010     12
1        B   foo    Jan-2010     18
2        A  test    Feb-2010     20
3        B   foo    Feb-2010     20
4        A  test  March-2010     30
5        B   foo  March-2010     25

For Older Versions of Pandas (<0.20):

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

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

# Optionally, reset the index
# df2 = df2.reset_index(drop=True)

This code will sort the output by "location" and "name" and provide a clean output with no index.

Note: In newer versions of Pandas, use sort_values instead of sort.

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