fillna

问题 I have a column with na values that I want to fill according to values from another data frame according to a key. I was wondering if there is any simple way to do so. Example: I have a data frame of objects and their colors like this: object color 0 chair black 1 ball yellow 2 door brown 3 ball **NaN** 4 chair white 5 chair **NaN** 6 ball grey I want to fill na values in the color column with default color from the following data frame: object default_color 0 chair brown 1 ball blue 2 door

问题 I have a column with na values that I want to fill according to values from another data frame according to a key. I was wondering if there is any simple way to do so. Example: I have a data frame of objects and their colors like this: object color 0 chair black 1 ball yellow 2 door brown 3 ball **NaN** 4 chair white 5 chair **NaN** 6 ball grey I want to fill na values in the color column with default color from the following data frame: object default_color 0 chair brown 1 ball blue 2 door

问题 I have a data frame with some values by year and type . I want to replace all NaN values in each year with the mean of values in that year with a specific type. I would like to do this in the most elegant way possible. I'm dealing with a lot of data so less computation would be good as well. Example: df =pd.DataFrame({'year':[1,1,1,2,2,2], 'type':[1,1,2,1,1,2], 'val':[np.nan,5,10,100,200,np.nan]}) I want ALL nan's regardless of their type to be replaced with their respective year mean of all

问题 I have a data frame with some values by year and type . I want to replace all NaN values in each year with the mean of values in that year with a specific type. I would like to do this in the most elegant way possible. I'm dealing with a lot of data so less computation would be good as well. Example: df =pd.DataFrame({'year':[1,1,1,2,2,2], 'type':[1,1,2,1,1,2], 'val':[np.nan,5,10,100,200,np.nan]}) I want ALL nan's regardless of their type to be replaced with their respective year mean of all

问题 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 I could not find out how to do it with fillna .) Data before: Day Cat1 Cat2 1 cat mouse 2 dog elephant 3 cat giraf 4 NaN ant Data after: Day Cat1 Cat2 1 cat mouse 2 dog elephant 3 cat giraf 4 ant ant 回答1: You can provide this column to fillna (see docs), it will use

问题 Update : It seems due to .loc ,if i uses the original df from pd.read_excel, it is fine. I have a dataframe with Dtypes as follows. This is csv for the dataframe : CSV File Date datetime64[ns] Amout float64 Currency object ID object I used the following code to replace NaT, NaN a=np.datetime64('2000-01-01') values={'Date':a,'Amount':0,'Currency':'0','ID':'0'} df.fillna(value=values,inplace=True) However, I got the error : TypeError: only integer scalar arrays can be converted to a scalar

来源： https://stackoverflow.com/questions/62327956/how-to-fill-missing-values-in-a-dataframe-based-on-group-value-counts

来源： https://stackoverflow.com/questions/50992193/pandas-dataframe-forward-fill-with-decay

来源： https://stackoverflow.com/questions/50992193/pandas-dataframe-forward-fill-with-decay

来源： https://stackoverflow.com/questions/50912255/change-axis-for-pandas-replace-ffill