NaN Equivalence Comparison Misused
Be careful when using the NaN equivalence comparison in NumPy and Pandas.
error-prone
Chain Indexing
Avoid using chain indexing in Pandas.
Merge API Parameter Not Explicitly Set
Explicitly specify on, how and validate parameter for df.merge() API in Pandas for better readability.
In-Place APIs Misused
Remember to assign the result of an operation to a variable or set the in-place parameter in the API.
Dataframe Conversion API Misused
Use df.to_numpy() in Pandas instead of df.values() for transform a DataFrame to a NumPy array.
No Scaling Before Scaling-sensitive Operation
Check whether feature scaling is added before scaling-sensitive operations.
Hyperparameter not Explicitly Set
Hyperparameters should be set explicitly.
Missing the Mask of Invalid Value
Add a mask for possible invalid values. For example, developers should add a mask for the input for tf.log() API.
TensorArray Not Used
Use tf.TensorArray() in TensorFlow 2 if the value of the array will change in the loop.
Training / Evaluation Mode Improper Toggling
Call the training mode in the appropriate place in PyTorch code to avoid forgetting to toggle back the training mode after the inference step.
Gradients Not Cleared before Backward Propagation
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.
Data Leakage
Use Pipeline() API in Scikit-Learn or check data segregation carefully when using other libraries to prevent data leakage.