Pandas: Replace value of column with previous row value if condition is met

Question

I have a DataFrame imported from a txt file with the following structure:

            ID   Place            Name        Other
0    123456789    1100           NAME1      5468.85
1      NUMBER1    1100    DESCRIPTION1     
2         STR1            DESCRIPTION2       
3      NUMBER2                            OTHER_STR
4    987654321    1100           NAME2      4566.65
1      NUMBER1    1100    DESCRIPTION1     
2         STR1            DESCRIPTION2     

I want to check something like the code below, but I understand that iterating through a df is a bad practice, and I'm not an expert in Pandas:

for row in df:
    if row['Other'] == '' or row['Place'] == '':
        row['ID'] == previous_row['ID']

The output should look like this:

            ID   Place            Name        Other
0    123456789    1100           NAME1      5468.85
1    123456789    1100    DESCRIPTION1     
2    123456789            DESCRIPTION2       
3    123456789                            OTHER_STR
4    987654321    1100           NAME2      4566.65
1    987654321    1100    DESCRIPTION1     
2    987654321            DESCRIPTION2     

Note that any row can be either a STR, an INT or blank. The data set is a bit more than a million rows by 15 columns, so it needs to be fast.

I've tried what's suggested here , but it doesn't quite determines a condition for the value of a column to be updated.

Answer 1

Using pandas.Series.ffill :

s = df["Place"].eq("") | df["Other"].eq("")
df.loc[s, "ID"] = pd.np.nan
df["ID"].ffill(inplace=True)
print(df)

Output:

          ID Place          Name      Other
0  123456789  1100         NAME1    5468.85
1  123456789  1100  DESCRIPTION1           
2  123456789        DESCRIPTION2           
3  123456789                      OTHER_STR
4  987654321  1100         NAME2    4566.65
1  987654321  1100  DESCRIPTION1           
2  987654321        DESCRIPTION2