How Can Pandas' `melt()` Function Restructure Tabular Data from Columns to Rows?

Restructure Tabular Data with Pandas: Converting Columns to Rows

Dealing with data organized in columns for different dates or categories can be cumbersome. Pandas provides a powerful solution to convert such columns into easily readable rows. This article addresses the specific challenge of transforming a CSV with location information and multiple date columns into a desired format.

Problem Statement:

Consider a dataset with information organized by location, each with multiple dates as column headers. The goal is to restructure the data into a format where each row represents a unique location, name, date, and its corresponding value.

Pandas Solution:

Pandas offers a highly efficient method to achieve this transformation using its melt function.

Code:

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

Explanation:

• id_vars: Specifies the columns that remain unchanged as rows.
• var_name: Assigns a new column name for the melted dates.
• value_name: Provides a name for the column containing the original values.

Result:

Applying the melt function to the provided dataset yields the desired output:

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

This new arrangement makes it easier to analyze the data by location, name, and date.

Note:

For older versions of Pandas (0.20 or earlier), the following alternate approach can be used:

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

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