How to use Pandas unstack() Function (2024)

Pandas.DataFrame.unstack() is used to reshape a DataFrame by transposing specified row levels into column levels. By default, it transposes the innermost row level into a column level, but you can specify other levels if needed. Reshaping the DataFrame is one of the crucial techniques in data analysis, and Pandas provides a rich set of in-built functions for this purpose.

Among the various functions provided by Pandas for reshaping DataFrames, stack() and unstack() are indeed among the most popular ones for transposing row levels to columns and vice versa.

In this article, I will explain the pandas unstack() function and using its syntax, parameters, and usage how we can transpose single or multi-level rows to column levels with examples.

Key Points –

• Pandas unstack() function reshapes a hierarchical index DataFrame by pivoting the innermost level of the index labels to become columns, effectively converting a multi-index into a DataFrame with a two-dimensional structure.
• The unstack() function works with both Series and DataFrame objects in Pandas, allowing for flexible manipulation of hierarchical data structures.
• It is commonly used in data preprocessing and analysis tasks, especially when dealing with datasets with multi-level or hierarchical indexes.
• The function provides options for handling missing data, allowing users to specify how to deal with NaN values resulting from the unstacking operation.

Quick Examples of unstack() Function

If you are in a hurry, below are some quick examples of unstack() functions.

# Quick examples of unstack() function# Example 1: Unstack default level(-1) of pandas Seriesprint("Unstacked DataFrame :\n", ser.unstack())# Example 2: Unstack specified levelprint("Unstacked DataFrame :\n", ser.unstack(level = 0))# Example 3: Unstack default level(-1) of pandas DataFrameprint("Unstacked DataFrame :\n", df.unstack())# Example 4: Unstack specified levelprint("Unstacked DataFrame :\n", df.unstack(level = 0))# Example 5: Fill NaN value set fill_valueprint("Unstacked DataFrame :\n", df.unstack(level = 0, fill_value = '-'))

Pandas unstack() Introduction

Following is the syntax of unstack() function.

# Syntax of Pandas unstack()DataFrame.unstack(level=- 1, fill_value=None)

Parameters of the unstack()

Following are the parameters of thepandas unstack() function.

• level : This parameter specifies the level of the index to unstack. By default, it unstacks the innermost level (level=-1). You can also pass the name or position of the level as an integer or label.
• fill_value : ( int, str or dict) It replaces NaN values( which are produced from unstacking) with specified values.

Return Value

It returns an unstacked DataFrame.

Usage of unstack() Function

In pandas, the unstack() function is used to reshape a DataFrame by converting one or more levels of the row index into column labels. It essentially transposes the DataFrame, turning rows into columns. This function is the reverse operation of the stack() function, where data is stacked from the column level to the row level.

Let’s create Pandas Series with a multi-level index and apply the Pandas unstack() function, it will return the unstacked Pandas DataFrame. Set the index of multiple level using pandas.MultiIndex.from_tuples().

# Create multi index Pandas Seriesimport pandas as pdindex = pd.MultiIndex.from_tuples([ ('Seattle', 'Date'), ('Seattle', 'Temp'), ('Sanfrancesco', 'Date'), ('Sanfrancesco', 'Temp')])ser = pd.Series(["30-12-2010", 40.7, "31-12-2010", 40.5], index=index)print(ser)

Yields below output.

Unstack Panda Series

Apply unstack() function on a multi-indexed Pandas Series, by default it unstacked innermost row level to column level. It returns the unstacked DataFrame where the column labels are the innermost row indexes of the original Series.

# Unstack default level(-1) of pandas Seriesprint("Unstacked DataFrame :\n", ser.unstack())

Unstack Specified Level of Pandas Series

When you don’t specify a level, unstack() by default unstacks the innermost row level onto a column level, which is equivalent to setting level=-1. However, if you want to unstack a specific row level (or levels) to the column level, you can specify the level(s) using the level parameter.

# Unstack specified levelprint("Unstacked DataFrame :\n", ser.unstack(level = 0))# Output: # Unstacked DataFrame :# Sanfrancesco Seattle# Date 31-12-2010 30-12-2010# Temp 40.5 40.7

In the above example, by setting level=0, we’re unstacking the first level of the multi-index (‘Seattle’ and ‘Sanfrancisco’) onto column level, resulting in a DataFrame where these cities become column labels.

Unstack Pandas DataFrame

Apply unstack() function on a multi-indexed Pandas DataFrame. It returns the unstacked DataFrame where the column labels are the innermost row indexes of original DataFrame.

Let’s create Pandas DataFrame with multi-level index,

# Unstack default level(-1) of pandas DataFramemulti_index = pd.MultiIndex.from_tuples([("Index1","Seattle"), ("Index1","Sanfrancesco"), ("Index2","Newyork"), ("Index2","Washington")])df = pd.DataFrame({"Date":["30-12-2010", "30-12-2010", "30-12-2010", "30-12-2010"],"Temp":[40.2, 40.5, 41.4, 42.1]}, index=multi_index)print(df)print("Unstacked DataFrame :\n", df.unstack())# Output:# Date Temp# Index1 Seattle 30-12-2010 40.2# Sanfrancesco 30-12-2010 40.5# Index2 Newyork 30-12-2010 41.4# Washington 30-12-2010 42.1## Unstacked DataFrame :# Date ... Temp # Newyork Sanfrancesco Seattle ... Sanfrancesco Seattle Washington# Index1 NaN 30-12-2010 30-12-2010 ... 40.5 40.2 NaN# Index2 30-12-2010 NaN NaN ... NaN NaN 42.1## [2 rows x 8 columns]

Unstack Specified Level of Pandas DataFrame

The below example will unstack the specified row level to the column level. For example,

# Unstack specified levelprint("Unstacked DataFrame :\n", df.unstack(level = 0))# Output:# Date Temp # Index1 Index2 Index1 Index2# NewYork NaN 30-12-2010 NaN 41.4# Sanfrancesco 30-12-2010 NaN 40.5 NaN# Seattle 30-12-2010 NaN 40.2 NaN# Washington NaN 30-12-2010 NaN 42.1

Fill NaN Values with Fill_value Param

Use fill_value param with specified value into unstack() function to replace NaN value with a specific value.

# Fill NaN value set fill_valueprint("Unstacked DataFrame :\n", df.unstack(level = 0, fill_value = '-'))# Output:# Unstacked DataFrame :# Date Temp # Index1 Index2 Index1 Index2# Newyork - 30-12-2010 - 41.4# Sanfrancesco 30-12-2010 - 40.5 -# Seattle 30-12-2010 - 40.2 -# Washington - 30-12-2010 - 42.1

Frequently Asked Questions onPandas unstack() Function

Conclusion

In this article, I have explained the Pandas unstack() function and using its syntax and parameters how we can transpose row level to column level in a Series/DataFrame with examples.

Happy learning!!

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References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.unstack.html