Unnecessary Iteration
Avoid unnecessary iterations. Use vectorized solutions instead of loops.
Code Smells
NaN Equivalence Comparison Misused
Be careful when using the NaN equivalence comparison in NumPy and Pandas.
Chain Indexing
Avoid using chain indexing in Pandas.
Columns and DataType Not Explicitly Set
Explicitly select columns and set DataType in Pandas.
Empty Column Misinitialization
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.
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.
Matrix Multiplication API Misused
When the multiply operation is performed on two-dimensional matrixes, use np.matmul() instead of np.dot() in NumPy for better semantics.
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.
Memory not Freed
Free memory in time.
Deterministic Algorithm Option Not Used
Set deterministic algorithm option to True during the development process, and use the option that provides better performance in the production.
Randomness Uncontrolled
Set random seed explicitly during the development process whenever a possible random procedure is involved in the application.
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.
Broadcasting Feature Not Used
Use the broadcasting feature in TensorFlow 2 to be more memory efficient.
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.
Pytorch Call Method Misused
Use self.net() in PyTorch to forward the input to the network instead of self.net.forward().
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.
Threshold-Dependent Validation
Use threshold-independent metrics instead of threshold-dependent ones in model evaluation.