How Can I Reshape a Pandas DataFrame from Wide to Long Format Using `pd.melt()`?

Convert Columns into Rows with Pandas

This question addresses the need to transform a CSV dataset where date-related information is distributed across multiple columns into a format where each date has its own row. To achieve this transformation, the user employs the pd.melt function.

The original CSV structure resembles:

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

The desired outcome is a table with columns location, name, Date, and Value:

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

The pd.melt function provides the means to restructure the data. It takes several arguments:

• id_vars: The columns that remain unchanged as the table is melted. In this case, location and name.
• var_name: The name of the new column that will hold the original column names (as they were before melting). In this case, Date.
• value_name: The name of the column that will hold the values from the original data columns. In this case, Value.

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

The resulting DataFrame matches the desired output, with each month now occupying a separate row.

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