Fill DataFrame based on value from another column-CodePudding

Given the following python pandas dataframe:

province	district
Total	example
NaN	other
Other	NaN
NaN	example
Result	example
NaN	example

If the province column is NaN and the value for that row is 'example', I want to fill the province gap with 'example'. The rest of the rows stay as they are.

DataFrame result:

province	district
Total	example
NaN	other
Other	NaN
example	example
Result	example
example	example

CodePudding user response：

You can use .fillna() conditionally with np.where:

df["province"] = np.where(
    df["district"] == "example", 
    df["province"].fillna(value="example"), 
    df["province"]
)

CodePudding user response：

You could use loc to find the rows with NaN in your province column and 'example' in your district column and update the values in your province column to be 'example':

df.loc[(df.province.isnull()) & (df.district.eq('example')),'province'] = 'example'

prints:

  province district
0    Total  example
1      NaN    other
2    Other      NaN
3  example  example
4   Result  example
5  example  example