Pandas.DataFrame.unstack() is used to reshape the given Pandas DataFrame by transposing specified row level to column level. By default, it transposes the innermost row level into a column level. This is one of the technique for reshaping the DataFrame. When we want to analyze or reshape the data, Pandas provides in-built functions. Among those functions stack() and unstack() functions are the most popular functions for transposing row level to column and vice versa.
In this article, I will explain Pandas unstack() function and using its syntax and parameters how we can transpose the single/multi-level row to column level with examples.
1. Quick Examples of unstack() Function
If you are in a hurry, below are some quick examples unstack() function.
# Below are the quick examples
# Example 1: Unstack default level(-1) of pandas Series
print("Unstacked DataFrame :\n", ser.unstack())
# Example 2: Unstack specified level
print("Unstacked DataFrame :\n", ser.unstack(level = 0))
# Example 3: Unstack default level(-1) of pandas DataFrame
print("Unstacked DataFrame :\n", df.unstack())
# Example 4: Unstack specified level
print("Unstacked DataFrame :\n", df.unstack(level = 0))
# Example 5: Fill NaN value set fill_value
print("Unstacked DataFrame :\n", df.unstack(level = 0, fill_value = '-'))
2. Syntax of Pandas unstack()
Following is the syntax of unstack() function.
# Syntax of Pandas unstack()
DataFrame.unstack(level=- 1, fill_value=None)
2.1 Parameters
Following are the parameters of unstack() function. It has two parameters.
level:(int, str, list of int, and list of str)By default, it is set to -1 i.e. the last level can be unstacked. If we pass the specified level, it will unstack those levels.fill_value :( int, str or dict) It replaces NaN values( which are produced from unstacking) with specified values.
2.2 Return Value
It returns an unstacked DataFrame.
3. Usage of unstack() Function
In Pandas df.unstack() function reshapes the given DataFrame by converting the row index to a column label. It returns a transposed DataFrame. This function is reverse to stack() function where stacking is done from column level to 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 Series
import pandas as pd
index = 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.
3.1 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 Series
print("Unstacked DataFrame :\n", ser.unstack())
4. Unstack Specified Level of Pandas Series
As we know from the above, by default(level = -1) it will unstack the innermost row level onto a column level. When we want to unstack a specified level, we can set level param with a specified level or list of levels. It will unstack specified row level to column level. For example,
# Unstack specified level
print("Unstacked DataFrame :\n", ser.unstack(level = 0))
# Output:
# Unstacked DataFrame :
# Sanfrancesco Seattle
# Date 31-12-2010 30-12-2010
# Temp 40.5 40.7
5. 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 DataFrame
multi_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]
6. Unstack Specified Level of Pandas DataFrame
The below example will unstack the specified row level to the column level. For example,
# Unstack specified level
print("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
7. 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_value
print("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
8. 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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