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Speedup NumPy Methods 25x With Bottleneck
NumPy's methods are already highly optimized for performance. Yer, here's how you can further speed them up. Bottleneck provides a suite of optimized…
Avi Chawla
Feb 18
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Speedup NumPy Methods 25x With Bottleneck
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Pandas and NumPy Return Different Values for Standard Deviation. Why?
Pandas assumes that the data is a sample of the population and that the obtained result can be biased towards the sample. Thus, to generate an unbiased…
Avi Chawla
Jan 30
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Pandas and NumPy Return Different Values for Standard Deviation. Why?
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Speed-up Pandas Apply 5x with NumPy
While creating conditional columns in Pandas, we tend to use the 𝐚𝐩𝐩𝐥𝐲() method almost all the time. However, 𝐚𝐩𝐩𝐥𝐲() in Pandas is nothing but…
Avi Chawla
Jan 1
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Speed-up Pandas Apply 5x with NumPy
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Speed-up NumPy 20x with Numexpr
Numpy already offers fast and optimized vectorized operations. Yet, it does not support parallelism. This provides further scope for improving the…
Avi Chawla
Dec 28, 2022
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Speed-up NumPy 20x with Numexpr
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An Elegant Way To Perform Matrix Multiplication
Matrix multiplication is a common operation in machine learning. Yet, chaining repeated multiplications using 𝐦𝐚𝐭𝐦𝐮𝐥 function makes the code…
Avi Chawla
Dec 26, 2022
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An Elegant Way To Perform Matrix Multiplication
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Difference Between Dot and Matmul in NumPy
The 𝐧𝐩.𝐦𝐚𝐭𝐦𝐮𝐥() and 𝐧𝐩.𝐝𝐨𝐭() methods produce the same output for 2D (and 1D) arrays. This makes many believe that they are the same and can…
Avi Chawla
Dec 22, 2022
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Difference Between Dot and Matmul in NumPy
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Don't Print NumPy Arrays! Use Lovely-NumPy Instead.
We often print raw numpy arrays during debugging. But this approach is not very useful. This is because printing does not convey much information about…
Avi Chawla
Dec 8, 2022
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Don't Print NumPy Arrays! Use Lovely-NumPy Instead.
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Don't Create Conditional Columns in Pandas with Apply
While creating conditional columns in Pandas, we tend to use the 𝐚𝐩𝐩𝐥𝐲() method almost all the time. However, 𝐚𝐩𝐩𝐥𝐲() in Pandas is nothing but…
Avi Chawla
Oct 28, 2022
5
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Don't Create Conditional Columns in Pandas with Apply
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Polynomial Linear Regression with NumPy
Polynomial linear regression using Sklearn is tedious as one has to explicitly code its features. This can get challenging when one has to iteratively…
Avi Chawla
Oct 15, 2022
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Polynomial Linear Regression with NumPy
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