We have 4 cameras “camA”, “camB”, “camC”, “camD” installed at every junction along a road and in this sequence. The flow of traffic is such “camA” is at the beginning, and “camD” is at the end. Whenever a vehicle passes a junction or makes a turn at the junction, the camera is positioned to record down the license plate number.
Tag: missing-data
Missing value Imputation based on regression in pandas
i want to inpute the missing data based on multivariate imputation, in the below-attached data sets, column A has some missing values, and Column A and Column B have the correlation factor of 0.70. So I want to use a regression kind of realationship so that it will build the relation between Column A and Column B and impute the
Filling missing data using a custom condition in a Pandas time series dataframe
Below is a portion of mydataframe which has many missing values. I would like to replace the NANs in each column using a specific backward fill condition . For example, in column (A,a) missing values appear for dates 16th, 17th, 18th and 19th. The next value is ‘4’ against 20th. I want this value (the next non missing value in
Missing observations and clustered standard errors in Python statsmodels?
What’s the cleanest, most pythonic way to run a regression only on non-missing data and use clustered standard errors? Imagine I have a Pandas dataframe all_data. Clunky method that works (make a dataframe without missing data): I can make a new dataframe without the missing data, make the model, and fit the model: This feels a bit clunky (esp. when