fillna

Python Dataframe fillna with value on left column

Python Dataframe fillna with value on left column Question: I have an excel spreadsheet where there are merged cells. I would like to build a dictionary of Product_ID – Category – Country. But for that I need to get, I believe, Python to be able to read an excel file with horizontally merged cells. import …

Total answers: 2

Pandas fillna throws ValueError: fill value must be in categories

Pandas fillna throws ValueError: fill value must be in categories Question: Discription: both features are in categorical dtypes. and i used this code in a different kernal of same dateset was working fine, the only difference is the features are in flote64. later i have converted these feature dtypes into Categorical because all the features …

Total answers: 4

How to Pandas fillna() with mode of column?

How to Pandas fillna() with mode of column? Question: I have a data set in which there is a column known as ‘Native Country’ which contain around 30000 records. Some are missing represented by NaN so I thought to fill it with mode() value. I wrote something like this: data[‘Native Country’].fillna(data[‘Native Country’].mode(), inplace=True) However when …

Total answers: 8

How to pass another entire column as argument to pandas fillna()

How to pass another entire column as argument to pandas fillna() Question: I would like to fill missing values in one column with values from another column, using fillna method. (I read that looping through each row would be very bad practice and that it would be better to do everything in one go but …

Total answers: 7

Pandas fill missing values in dataframe from another dataframe

Pandas fill missing values in dataframe from another dataframe Question: I cannot find a pandas function (which I had seen before) to substitute the NaN’s in a dataframe with values from another dataframe (assuming a common index which can be specified). Any help? Asked By: user308827 || Source Answers: If you have two DataFrames of …

Total answers: 6