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A Comprehensive NumPy Cheat Sheet Of 40 Most Used Methods
...that data scientists use 95% of the time.
 • 
Avi Chawla
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40 NumPy Methods That Data Scientists Use 95% of the Time
Applying the Pareto's principle to NumPy Library.
 • 
Avi Chawla
A Major Limitation of NumPy Which Most Users Aren't Aware Of
..and here's how to address it.
Beware of This Unexpected Behaviour of NumPy Methods
...and here's how to counter it.
 • 
Avi Chawla
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
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
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
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…
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Avi Chawla
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
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
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
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
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