Sep 22, 2019 · I wrap the na_kalman into a function to apply lapply which is useful should I want to apply the model to multivariate time series data. That is you might have a data set with many explanatory variables for each time stamp and one or more variables will have missing values. The function should impute across the columns of your data frame. Therefore, this package aids the Python user by providing more clarity to the imputation process, making imputation methods more accessible, and measuring the impact imputation methods have in supervised regression and classification. In doing so, this package brings missing data imputation methods to the Python world and makes them work nicely ... All such methods have a skipna option signaling whether to exclude missing data ( True by default): In [57]: df.sum(0, skipna=False) Out [57]: one NaN three NaN two 3.954464 dtype: float64 In [58]: df.sum(axis=1, skipna=True) Out [58]: a 2.577893 b 3.640333 c -0.079609 d 0.847998 dtype: float64. This module provides a simple way to time small bits of Python code. It avoids a number of common traps for measuring execution times. See also Tim Peters' introduction to the "Algorithms" chapter in the Python Cookbook, published by O'Reilly.Oct 23, 2017 · As data pre-processing, we frequently need to deal with missing values. There are some ways to deal with those and one of them is to complement those by representative values. On Python, by scikit-learn, we can do it.