api-specific

Be careful when using the NaN equivalence comparison in NumPy and Pandas.

Avoid using chain indexing in Pandas.

Use df.to_numpy() in Pandas instead of df.values() for transform a DataFrame to a NumPy array.

When the multiply operation is performed on two-dimensional matrixes, use np.matmul() instead of np.dot() in NumPy for better semantics.

Explicitly select columns and set DataType in Pandas.

When a new empty column is needed in a DataFrame in Pandas, use the NaN value in Numpy instead of using zeros or empty strings.

Explicitly specify on, how and validate parameter for df.merge() API in Pandas for better readability.

Use the broadcasting feature in TensorFlow 2 to be more memory efficient.

Use tf.TensorArray() in TensorFlow 2 if the value of the array will change in the loop.

Use self.net() in PyTorch to forward the input to the network instead of self.net.forward().

Call the training mode in the appropriate place in PyTorch code to avoid forgetting to toggle back the training mode after the inference step.

Use optimizer.zero_grad(), loss_fn.backward(), optimizer.step() together in order in PyTorch. Do not forget to use optimizer.zero_grad() before loss_fn.backward() to clear gradients.