CODEWITHSUNDEEP
pythonpandasbinarymultiple-conditions
S Hendricks
S Hendricks

Reputation: 119

How to replace different binary values across columns into 1/0

I have a dataset contains multiple binary values.

df = pd.DataFrame({"a": ["y", "n"], "b": ["t", "f"], 
                   "c": ["known", "unknown"], "d": ['found', 'not found']})

I want to replace all the binary columns to be 1/0, while not affect other numeric columns. Are there any simple solutions using one or two lines? The dataset contains over 500 columns, which is difficult to check and replace them one by one. Thanks.

Upvotes: 0

Views: 522

Answers (1)

ALollz
ALollz

Reputation: 59579

Can use pd.get_dummies with drop_first=True credit to @piRSquared

pd.get_dummies(df, drop_first=True)

#   a_y  b_t  c_unknown  d_not found
#0    1    1          0            0
#1    0    0          1            1

If this needs to be done for only binary object columns subset first.

df = pd.DataFrame({'a': ['y', 'n', 'c'], 
                   'b': ['t', 'f', 't'], 
                   'c': ['known', 'unknown', 'known'],
                   'd': ['found', 'not found', 'found'],
                   'e': [1, 2, 2]})

pd.get_dummies(df.loc[:, df.agg('nunique') == 2].select_dtypes(include='object'), 
               drop_first=True)

#   b_t  c_unknown  d_not found
#0    1          0            0
#1    0          1            1
#2    1          0            0

If there are a small number of binary responses across columns, consider creating a dictionary and mapping the values:

d = {'y': 1, 'n': 0,
     't': 1, 'f': 0,
     'known': 1, 'unknown': 0,
     'found': 1, 'not found': 0}

s = (df.agg('nunique') == 2) & (df.dtypes == 'object')
for col in s[s].index:
    df[col] = df[col].map(d)

#   a  b  c  d  e
#0  y  1  1  1  1
#1  n  0  0  0  2
#2  c  1  1  1  2
#   |
#  `a` not mapped because trinary

Upvotes: 1

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