CODEWITHSUNDEEP
pythonpandasdummy-variable
Jurgy
Jurgy

Reputation: 2360

get_dummies of dataframe with incomplete data using pandas

I have this DataFrame with both categorical and non-categorical data and I would like to dummy encode it but not all dummy values that I know are possible are present in the data.

For example let's use the following DataFrame:

>>> df = pd.DataFrame({"a": [1,2,3], "b": ["x", "y", "x"], "c": ["h", "h", "i"]})
>>> df
   a  b  c
0  1  x  h
1  2  y  h
2  3  x  i

Column a has a non-categorical values but both column b and c are categorical.

Now let's say column b can contain the categories x, y and z and column c the categories h, i, j and k

>>> dummy_map = {"b": ["x", "y", "z"], "c": ["h", "i", "j", "k"]}

I want to encode it so that the resulting dataframe is as follows:

>>> df_encoded
    a  b_x   b_y   b_z  c_h   c_i   c_j   c_k
0   1   1     0     0    1     0     0     0
1   2   0     1     0    1     0     0     0
2   3   1     0     0    0     1     0     0

My current solution is as follows:

df_encoded = pd.get_dummies(df)
for k, v in dummy_map.items():
  for cat in v:
    name = k + "_" + cat
    if name not in result:
      df_encoded[name] = 0

But it seems to me a bit inefficient and inelegant. So is there a better solution for this?

Upvotes: 1

Views: 224

Answers (1)

jezrael
jezrael

Reputation: 862611

Use Index.union with vae values generated by list comprehension and f-strings and DataFrame.reindex:

c = [f'{k}_{x}' for k, v in dummy_map.items() for x in v]
print (c)
['b_x', 'b_y', 'b_z', 'c_h', 'c_i', 'c_j', 'c_k']

df_encoded = pd.get_dummies(df)

vals = df_encoded.columns.union(c, sort=False)
df_encoded = df_encoded.reindex(vals, axis=1, fill_value=0)
print (df_encoded)
   a  b_x  b_y  c_h  c_i  b_z  c_j  c_k
0  1    1    0    1    0    0    0    0
1  2    0    1    1    0    0    0    0
2  3    1    0    0    1    0    0    0

If values should be sorted in union:

df_encoded = pd.get_dummies(df)

vals = df_encoded.columns.union(c)
df_encoded = df_encoded.reindex(vals, axis=1, fill_value=0)
print (df_encoded)
   a  b_x  b_y  b_z  c_h  c_i  c_j  c_k
0  1    1    0    0    1    0    0    0
1  2    0    1    0    1    0    0    0
2  3    1    0    0    0    1    0    0

Upvotes: 1

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