How can I efficiently add a rolling list column to a Pandas DataFrame?

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

I have a pandas dataframe created with random integers:

PYTHON
import pandas as pd
import numpy as np

np.random.seed(150)
df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=['A', 'B'])

I want to create a new column "C" that, for each row (starting from the third), contains a list of the current value in column "B" and the two preceding values. My current approach uses a list comprehension:

PYTHON
>>> df['C'] = [df['B'].iloc[i-2:i+1].tolist() if i >= 2 else None for i in range(len(df))]
>>> df
   A  B          C
0  4  9       None
1  0  2       None
2  4  5  [9, 2, 5]
3  7  9  [2, 5, 9]
4  8  3  [5, 9, 3]
5  8  1  [9, 3, 1]
6  1  4  [3, 1, 4]
7  4  1  [1, 4, 1]
8  1  9  [4, 1, 9]
9  3  7  [1, 9, 7]

This method works but becomes slow for large datasets. When I try using df.apply with rolling windows:

PYTHON
df['C'] = df['B'].rolling(window=3).apply(lambda x: list(x), raw=False)

TypeError: must be real number, not list

I understand that pandas apply expects a scalar result, so returning a list causes an error. Is there a more efficient method to achieve this?

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Answers 4

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4 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

One simple solution for a performance boost is to use NumPy’s sliding window view. The idea is to use NumPy’s stride tricks to create a rolling view of the column “B” as an array, then convert each window to a list and assign it to the appropriate rows in column “C”. You can do this as follows:

PYTHON
import pandas as pd
import numpy as np

np.random.seed(150)
df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=['A', 'B'])

# Create a sliding window view of column B with window size 3.
# Note: sliding_window_view is available in numpy>=1.20.
from numpy.lib.stride_tricks import sliding_window_view

# This returns an array of shape (n - window_size + 1, window_size)
windows = sliding_window_view(df['B'].values, window_shape=3)

# Assign lists to column 'C' from row index 2 onward
df.loc[2:, 'C'] = [list(window) for window in windows]

print(df)

Explanation

We first import the necessary modules and create the dataframe.
The sliding_window_view function returns an array of shape (len(df['B']) - 3 + 1, 3). Each row in this result is a 3-element view from column “B”.
We then convert each window to a list and assign it to column “C” starting from index 2 (because rows 0 and 1 do not have three observations).

Why Not Use Rolling Apply?

When using df['B'].rolling(3).apply(lambda x: list(x), raw=False), pandas expects the function to return a numerical scalar (since it’s designed to compute aggregated values). Because a list is returned instead of a scalar, it raises a TypeError.

This alternative using sliding_window_view avoids that issue and is much faster for large datasets.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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Answer by PlanetaryEnvoy323 • 1 month ago

PYTHON
import pandas as pd
import numpy as np

# Use sliding_window_view for fast rolling window extraction
from numpy.lib.stride_tricks import sliding_window_view

# Sample Data 
p.random.seed(150)

df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=['A', 'B'])
print(df)

'''  
  A  B
0  4  9
1  0  2
2  4  5
3  7  9
4  8  3
5  8  1
6  1  4
7  4  1
8  1  9
9  3  7
'''  

# Convert column to NumPy array
B_values = df['B'].values

# Apply sliding window
windows = sliding_window_view(B_values, window_shape=3)

# Create a new column, filling the first two rows with None
df['C'] = [None, None] + windows.tolist()

print(df.head(10))

'''  
   A  B          C
0  4  9       None
1  0  2       None
2  4  5  [9, 2, 5]
3  7  9  [2, 5, 9]
4  8  3  [5, 9, 3]
5  8  1  [9, 3, 1]
6  1  4  [3, 1, 4]
7  4  1  [1, 4, 1]
8  1  9  [4, 1, 9]
9  3  7  [1, 9, 7]
'''

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Answer by CometSentinel999 • 1 month ago

I guess you can change your thinking from another way around, say, not row-wise but column-wise sliding windowing, and probably your code could speed up unless you have a large window size N.

For example, you can try

PYTHON
N = 3
nr = len(df)
df['C'] = [None]*(N-1) + np.column_stack([df['B'].iloc[k:nr-N+1+k] for k in range(N)]).tolist()

and you will obtain

PLAINTEXT
    A   B   C
0   4   9   None
1   0   2   None
2   4   5   [9, 2, 5]
3   7   9   [2, 5, 9]
4   8   3   [5, 9, 3]
5   8   1   [9, 3, 1]
6   1   4   [3, 1, 4]
7   4   1   [1, 4, 1]
8   1   9   [4, 1, 9]
9   3   7   [1, 9, 7]

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Answer by EtherealDiscoverer767 • 1 month ago

You can use numpy's sliding_window_view:

PYTHON
from numpy.lib.stride_tricks import sliding_window_view as swv

N = 3
df['C'] = pd.Series(swv(df['B'], N).tolist(), index=df.index[N-1:])

Output:

PLAINTEXT
   A  B          C
0  4  9        NaN
1  0  2        NaN
2  4  5  [9, 2, 5]
3  7  9  [2, 5, 9]
4  8  3  [5, 9, 3]
5  8  1  [9, 3, 1]
6  1  4  [3, 1, 4]
7  4  1  [1, 4, 1]
8  1  9  [4, 1, 9]
9  3  7  [1, 9, 7]

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Discussion

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How can I efficiently add a rolling list column to a Pandas DataFrame?

4 Answers

Explanation

Why Not Use Rolling Apply?

Discussion

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