import pandas as pd df = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=['A', 'B']) print(df) # prints this: # A B # 0 1 2 # 1 3 4 # 2 5 6 # 3 7 8 # 4 9 10 df.rolling(3, min_periods=3).apply(lambda x: bool(print(x, '\n'))) # prints this, so as expected, only batches of 3 are being evaluated # 0 1.0 # 1 3.0 # 2 5.0 # dtype: float64  #  # 1 3.0 # 2 5.0 # 3 7.0 # dtype: float64  #  # 2 5.0 # 3 7.0 # 4 9.0 # dtype: float64  #  # 0 2.0 # 1 4.0 # 2 6.0 # dtype: float64  #  # 1 4.0 # 2 6.0 # 3 8.0 # dtype: float64  #  # 2 6.0 # 3 8.0 # 4 10.0 # dtype: float64  #  # Out[33]:  # A B # 0 NaN NaN # 1 NaN NaN # 2 0.0 0.0 # 3 0.0 0.0 # 4 0.0 0.0 df.rolling(3, method='table', min_periods=3).apply(lambda x: bool(print(x, '\n')), raw=True, engine='numba') # prints this, where we see that the first two batches of sizes 1 and 2 are also being evaluated # [[1. 2.]] <-- batch of size 1, illegal #  # [[1. 2.] <-- batch of size 2, illegal # [3. 4.]]  #  # [[1. 2.] # [3. 4.] # [5. 6.]]  #  # [[3. 4.] # [5. 6.] # [7. 8.]]  #  # [[ 5. 6.] # [ 7. 8.] # [ 9. 10.]]  # Out[34]:  # A B # 0 NaN NaN # 1 NaN NaN # 2 0.0 0.0 # 3 0.0 0.0 # 4 0.0 0.0