Given that this is the top Google result when searching for "Pandas replace is not working" I'd like to also mention that:

replace does full replacement searches, unless you turn on the regex switch. Use regex=True, and it should perform partial replacements as well.

This took me 30 minutes to find out, so hopefully I've saved the next person 30 minutes.

As of pandas 1.3, this bug should not occur anymore.

If you are still using pandas <1.3, this bug was caused by the "string" dtype, so use dtype=str or dtype="str" instead:

df = pd.read_csv(f_name, dtype=str)
#                              ---
df = df[df.columns.dropna()]
df[cols_int] = df[cols_int].replace({"[\$]": "", "[,]": ""}, regex=True)
df[cols_int] = df[cols_int].astype(int)

As far as I remember, Python Pandas was changed a little bit in replace. You should try passing over a regex keyword argument.

Like so;

report_df['Owner'].replace({'\r\nName: hwowneremail, dtype: object':''},regex=True)

Given:

                                Movie Name                                      others
0         James Bond The Spy Who Loved Me                                1977) [1080p]
1             James Bond Live And Let Die                                1973) [1080p]
2                          No Time To Die        2021) [1080p] [BluRay] [5.1] [YTS.MX]
3  James Bond The Man With The Golden Gun                                1974) [1080p]
4                           Casino Royale   2006) [2160p] [4K] [BluRay] [5.1] [YTS.MX]
5                    James Bond Moonraker                                1979) [1080p]
6              James Bond Licence To Kill                                1989) [1080p]
7             James Bond A View To A Kill                                1985) [1080p]
8         James Bond The Living Daylights                                1987) [1080p]

To clean this up I would do:

df['Movie Name'] = df['Movie Name'].str.strip()
df[['Year', 'Resolution']] = df['others'].str.extract('(\d{4})\).*\[(.*p)]')

print(df[['Movie Name', 'Year', 'Resolution']])

Output:

                               Movie Name  Year Resolution
0         James Bond The Spy Who Loved Me  1977      1080p
1             James Bond Live And Let Die  1973      1080p
2                          No Time To Die  2021      1080p
3  James Bond The Man With The Golden Gun  1974      1080p
4                           Casino Royale  2006      2160p
5                    James Bond Moonraker  1979      1080p
6              James Bond Licence To Kill  1989      1080p
7             James Bond A View To A Kill  1985      1080p
8         James Bond The Living Daylights  1987      1080p