更新数据框列中的信息

Question

I have a filtered dataset, new_df , like this

    Label  New_Label    Username    Look_up
59  1.0    True         vald21      val
67  1.0    True         2512        2512
75  1.0    True         Christine   Chris

which was created to assign a new label ( New_Label ) when some conditions were met. I have also another dataset ( df ) which includes all the data (this dataset is where the data above where extract from) but has not information about the New_Label (as the dataset above was created for this reason, by filtering based on specific conditions).

        Label   Username    Look_up
    59  1.0     vald21      val
    67  1.0     2512        2512
    67  0.0     faehr6542   faehr
...
    75  1.0     Christine   Chris
   122  0.0     starogm     starogm

I would like to change the Label from my original dataset df to those rows in new_df , when it is the case Label and New_Label do not match.

        Label   Username    Look_up
    59  0       vald21      val
    67  0       2512        2512
    67  0       faehr6542   faehr
...
    75  0       Christine   Chris
   122  0     starogm     starogm

where True in new_df corresponds to 0 and False to 1 in Label column. I do not want to change the other values, only those ones in the new_df dataset (my key would be Username).

Could you explain me, please, how to change information in the original dataset?

Thanks

Answer 1

You can try merging two dataframe and then using .assign along with np.where . When merging with outer , the values not present will have NA so np.where with notnull() can be used:

pd.merge(df, new_df, how='outer').assign(Label = lambda row:np.where(row['New_Label'].notnull(), 0, 1))

If you do not want New_Label , you can drop the column with .drop('New_Label', axis=1) . Something like below (if written in one line):

pd.merge(df, new_df, how='outer').assign( Label = lambda row:  np.where(row['New_Label'].notnull(), 0, 1)).drop('New_Label', axis=1)

Answer 2

如果我理解您的问题，您想翻转'New_Label' ，将其转换为 int 并将其分配给'Label' ：

new_df['Label'] = (new_df['New_Label']==False).astype(int)