Pandas.DataFrame.stack() function is used to reshape the given DataFrame by transposing specified column level into row level. By default, it transposes the innermost column level. This is one of the techniques for reshaping the DataFrame. When we want to analyze or reshape, Pandas provides in-built functions. Among those functions stack() and unstack() functions are the most popular functions for transposing row level to column level and vice versa.
In this article, I will explain the Pandas stack() function and using this syntax and parameters how we can transpose the single or multi-level column to row level with examples.
Key Points –
- Pandas
stack()function reshapes a DataFrame by pivoting the columns into rows, effectively converting wide-format data to long-format data. - It operates on DataFrame objects in Pandas, allowing users to stack specified levels of the column labels.
- The
stack()function is beneficial for transforming hierarchical columnar data into a more manageable and analytically friendly form. - The
stack()function complements theunstack()function, which does the opposite operation, converting long-format data to wide-format data by pivoting rows into columns.
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Quick Examples of stack() Function
If you are in a hurry, below are some quick examples of Pandas stack() function.
# Quick examples of stack() function
# Example 1: Apply stack on single level column DataFrame
df = pd.DataFrame([["30-12-2010", 40.7],
["31-12-2010", 40.5]],
columns = ["Date","Temp"],
index = ['Seattle', 'Sanfrancesco']
)
print(df)
print("Stacked DataFrame:\n", df.stack())
# Example 2: Stack the multi-level DataFrame
multi_col = pd.MultiIndex.from_tuples(
[('Temp', 'Min'), ('Temp', 'Max')]
)
df = pd.DataFrame(
[[38.2, 40.1], [40.4, 43.3]],
index = ['Seattle', 'Sanfrancesco'],
columns = multi_col
)
print(df)
print("Stacked DataFrame:\n", df.stack())
# Example 3: Stack the multi-level DataFrame
multi_col1 = pd.MultiIndex.from_tuples(
[('Temp', 'Min'), ('Wind', 'Mph')]
)
df = pd.DataFrame(
[[38.6, 8], [40.2, 6]],
index = ['Seattle', 'Sanfrancesco'],
columns=multi_col1
)
print(df)
print("Stacked DataFrame:\n", df.stack())
# Example 4: Stack specified level DataFrame
print("Stacked DataFrame:\n", df.stack(level = 0))
# Example 5: Use Dropna param in stacking
print("Stacked DataFrame:\n", df.stack(dropna = False))
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Syntax of Pandas stack()
Following is the syntax of unstack() function.
# Syntax of Pandas stack()
DataFrame.stack(level=- 1, dropna=True)
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Parameters of the Pandas stack()
Following are the parameters of stack() function. It has two parameters.
level :(int, str, list of int, and list of str)By default it is -1 i.e. last level can be stacked. If we pass specified level, it will stack those level from the column level to the row level.dropna :(bool, default True) Is used to manage the NaN values which are formed by stacking of DataFrame.
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Return Value
It returns a stacked Series or a DataFrame.
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Pandas stack() Usage
In Pandas df.stack() function reshapes the given DataFrame by converting the column label to a row index. It returns a Series object. It is transposed form of the original DataFrame. This function is reverse to unstack() function where stacking is done from row level to column level.
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Stack SingleLevel Pandas DataFrame
Let's create DataFrame with a single level column and apply the Pandas stack() function, it will return the stacked Pandas Series.
# Create DataFrame with single level column
import pandas as pd
df = pd.DataFrame([["30-12-2010", 40.7],
["31-12-2010", 40.5]],
columns = ["Date","Temp"],
index = ['Seattle', 'Sanfrancesco']
)
print(df)
# Apply stack on single level column DataFrame
print("Stacked DataFrame:\n", df.stack())
Yields below output.
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Stack Multi-Level Pandas DataFrame
Using stack() function we can reshape the DataFrame that has multi-level columns. Pandas provide the easiest way to create multi indexes DataFrame for both column and row using pandas.MultiIndex.from_tuples().
Let's use this function to add the multi-level columns to DataFrame and apply the stack() function to it. By default, it will stack the innermost column level into row level.
# Stack the Multi-level DataFrame
multi_col = pd.MultiIndex.from_tuples(
[('Temp', 'Min'), ('Temp', 'Max')]
)
df = pd.DataFrame(
[[38.2, 40.1], [40.4, 43.3]],
index = ['Seattle', 'Sanfrancesco'],
columns = multi_col
)
print(df)
print("Stacked DataFrame:\n", df.stack())
Yields below output.
# Output:
Temp
Min Max
Seattle 38.2 40.1
Sanfrancesco 40.4 43.3
Stacked DataFrame:
Temp
Seattle Max 40.1
Min 38.2
Sanfrancesco Max 43.3
Min 40.4
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Stack Specified Level Pandas DataFrame
As we know from the above, by default(level = -1) it will stack the innermost column level. When we want to stack a specified level we have to set level param with a specified level or list of levels. It will stack specified column level to row level. For example,
# Stack specified level DataFrame
multi_col1 = pd.MultiIndex.from_tuples(
[('Temp', 'Min'), ('Wind', 'Mph')]
)
df = pd.DataFrame(
[[38.6, 8], [40.2, 6]],
index = ['Seattle', 'Sanfrancesco'],
columns=multi_col1
)
print(df)
print("Stacked DataFrame:\n", df.stack(level = 0))
Yields below output.
# Output:
Temp Wind
Min Mph
Seattle 38.6 8
Sanfrancesco 40.2 6
Stacked DataFrame:
Min Mph
Seattle Temp 38.6 NaN
Wind NaN 8.0
Sanfrancesco Temp 40.2 NaN
Wind NaN 6.0
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Stack DataFrame Use dropna Param
By default, while stacking we can get a row with NaN values. To overcome this problem set False to dropna Param.
# Use Dropna param in stacking
multi_col1 = pd.MultiIndex.from_tuples(
[('Temp', 'Min'), ('Wind', 'Mph')]
)
df = pd.DataFrame(
[[None, 8], [40.2, 6]],
index = ['Seattle', 'Sanfrancesco'],
columns=multi_col1
)
print(df)
print("Stacked DataFrame:\n", df.stack())
print("Stacked DataFrame:\n", df.stack(dropna = False))
Yields below output.
# Output:
Temp Wind
Min Mph
Seattle NaN 8
Sanfrancesco 40.2 6
Stacked DataFrame:
Temp Wind
Seattle Mph NaN 8.0
Sanfrancesco Min 40.2 NaN
Mph NaN 6.0
Stacked DataFrame:
Temp Wind
Seattle Min NaN NaN
Mph NaN 8.0
Sanfrancesco Min 40.2 NaN
Mph NaN 6.0
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Frequently Asked Questions on Pandas stack() Function
The stack() function in Pandas reshapes a DataFrame by pivoting the columns into rows. It effectively converts wide-format data into long-format data by stacking the specified levels of column labels onto the DataFrame's index.
When the stack() function is applied to a DataFrame with hierarchical column labels (MultiIndex), it stacks the specified levels of column labels onto the index, resulting in a DataFrame with a hierarchical index.
The primary purpose of using the stack() function in Pandas is to reshape a DataFrame by pivoting the columns into rows, effectively converting wide-format data into long-format data.
One consideration when using the stack() function is that it may result in a DataFrame with a hierarchical index, which can affect subsequent data manipulation and analysis.
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Conclusion
In this article, I have explained the Pandas stack() function and using this syntax and parameters how we can transpose the single/multi-level column level to row level with examples.
Happy learning!!
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