How to convert certain categorical values from a DataFrame to numerical(int) in python? -

i have dataframe multiple columns , categorical data in want assign numerical (int) value in order proceed data clean-up need do.

e.g. want cells in column oldvalue & newvalue containing "1st call" have value of 2, "2nd call" have value of 3, , on...

i post screenshot of dataframe understand mean.

i new programming languages hence if please put practical example answer of huge help.

you may use replace , passing dictinary maps each category on numerical value , add new column dataframe:

df['oldvalueint'] = df['oldvalue'].replace( {'1st call attempted': 2, '2nd call attempted': 3})

example:

df = pd.dataframe([['a','x'],['b','x'],['a','y']], columns=['ab','xy'])  df['abint'] = df['ab'].replace('a': 1, 'b': 2)  print df

which yields

  ab xy  abint 0   x      1 1  b  x      2 2   y      1

or if want replace multiple columns:

df[['ab','xy']] = df.replace( {'ab': {'a': 1, 'b': 2},                                 'xy': {'x': 2, 'y': 3}} )

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