Opposite of factorize function (Map numeric to categorical values)-CodePudding

I am searching for a way to map some numeric columns to categorical features.

All columns are of categorical nature but are represented as integers. However I need them to be a "String".

e.g.

col1 col2 col3 ->  col1new col2new col3new
0     1    1   ->  "0"     "1"     "1"
2     2    3   ->  "2"     "2"     "3"
1     3    2   ->  "1"     "3"     "2"

It does not matter what kind of String the new column contains as long as all distinct values from the original data set map to the same new String value.

Any ideas?

I have a bumpy representation of my data right now but any pandas solution would be also helpful.

Thanks a lot!

CodePudding user response：

You can use applymap method. Cosider the following example:

df = pd.DataFrame({'col1': [0, 2, 1], 'col2': [1, 2, 3], 'col3': [1, 3, 2]})
df.applymap(str)

     col1 col2 col3
0    0    1    1
1    2    2    3
2    1    3    2

You can convert all elements of col1, col2, and col3 to str using the following command:

df = df.applymap(str)

CodePudding user response：

you can modify the type of the elements in a list by using the dataframe.apply function which is offered by pandas-dataframe-apply.

frame = pd.DataFrame(np.random.randint(0, 90, size =(5000000, 3)), columns =['col1', 'col2', 'col3'])

in the new dataframe you can define columns and the value by:

updated_frame = pd.DataFrame(np.random.randint(0, 90, size =(5000000, 3)), columns =['col1new', 'col2new', 'col3new'])
updated_frame['col1new'] = frame['col1'].apply(str)
updated_frame['col2new'] = frame['col2'].apply(str)
updated_frame['col3new'] = frame['col3'].apply(str)

CodePudding user response：

You could use the .astype method. If you want to replace all the current columns with a string version then you could do (df your dataframe):

df = df.astype(str)

If you want to add the string columns as new ones:

df = df.assign(**{f"{col}new": df[col].astype(str) for col in df.columns})