How do I rename items from a column according to criteria?-CodePudding

I have the following dataframe:

      import pandas as pd
      df = pd.DataFrame({'ID': [1,2,3,4,5],
                         'Name_columns': ['1', '2', '2022', '1', '1']})

      print(df)

      ID    Name_columns
       1       1
       2       2
       3       2022
       4       1
       5       1

I would like to replace the values in the "Name_columns" column. Where is 1 I would like to write "SENSOR" and where is 2 write "ACTUATOR". So I tried to do the following code using replace():

      df['Name_columns'] = df['Name_columns'].str.replace('1', 'SENSOR')
      df['Name_columns'] = df['Name_columns'].str.replace('2', 'ACTUADOR')

The resulting output obtained is:

      print(df)

      ID    Name_columns
      1      SENSOR
      2      ACTUADOR
      3      ACTUADOR0ACTUADORACTUADOR
      4      SENSOR
      5      SENSOR

I would like to ask how can I keep the implementation for values 1 and 2 and, if different from these values, how can I write "Invalid"?

CodePudding user response：

Use a dictionary to map the values and fillna with a default string:

d = {'1': 'SENSOR', '2': 'ACTUADOR'}

df['Name_columns'] = df['Name_columns'].astype(str).map(d).fillna('Invalid')

NB. if the column is already a string, you an leave out the astype(str) output:

   ID Name_columns
0   1       SENSOR
1   2     ACTUADOR
2   3      Invalid
3   4       SENSOR
4   5       SENSOR

CodePudding user response：

A pandas.Series has a map method that accepts a dictionary as an argument, and leaves the keys not on the dictionary as nan

df['Name_columns'].map({'1': 'SENSOR', '2': 'ACTUATOR'}).fillna('INVALID')

# 0      SENSOR
# 1    ACTUATOR
# 2     INVALID
# 3      SENSOR
# 4      SENSOR