Remap values in pandas column with a dict

Question

I have a dictionary which looks like this: di = {1: \"A\", 2: \"B\"}

I would like to apply it to the \"col1\" column of a dataframe similar to:

Answer 1

Adding to this question if you ever have more than one columns to remap in a data dataframe:

def remap(data,dict_labels):
    """
    This function take in a dictionnary of labels : dict_labels 
    and replace the values (previously labelencode) into the string.

    ex: dict_labels = {{'col1':{1:'A',2:'B'}}

    """
    for field,values in dict_labels.items():
        print("I am remapping %s"%field)
        data.replace({field:values},inplace=True)
    print("DONE")

    return data

Hope it can be useful to someone.

Cheers

Answer 2

A nice complete solution that keeps a map of your class labels:

labels = features['col1'].unique()
labels_dict = dict(zip(labels, range(len(labels))))
features = features.replace({"col1": labels_dict})

This way, you can at any point refer to the original class label from labels_dict.

Answer 3

Or do apply:

df['col1'].apply(lambda x: {1: "A", 2: "B"}.get(x,x))

Demo:

>>> df['col1']=df['col1'].apply(lambda x: {1: "A", 2: "B"}.get(x,x))
>>> df
  col1 col2
0    w    a
1    1    2
2    2  NaN
>>> 

Answer 4

A more native pandas approach is to apply a replace function as below:

def multiple_replace(dict, text):
  # Create a regular expression  from the dictionary keys
  regex = re.compile("(%s)" % "|".join(map(re.escape, dict.keys())))

  # For each match, look-up corresponding value in dictionary
  return regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], text) 

Once you defined the function, you can apply it to your dataframe.

di = {1: "A", 2: "B"}
df['col1'] = df.apply(lambda row: multiple_replace(di, row['col1']), axis=1)