CODEWITHSUNDEEP
pythonpandasnumpyreplacecategories
JT28
JT28

Reputation: 479

How to encode categorical values

Hi I'm new to python and pandas.

I have extracted the unique values of one of the column using pandas. Now after getting the unique values of the column, which are string.

['Others, Senior Management-Finance, Senior Management-Sales'
  'Consulting, Strategic planning, Senior Management-Finance'
  'Client Servicing, Quality Control - Product/ Process, Strategic       
   planning'
  'Administration/ Facilities, Business Analytics, Client Servicing'
  'Sales & Marketing, Sales/ Business Development/ Account Management,    
  Sales Support']

I want to replace the string values with the unique integer value.

for simplicity I can give you the dummy input and output.

Input:

Col1
  A
  A
  B
  B
  B
  C
  C

Unique df value will come as below

[ 'A' 'B' 'C' ]

after replacing the column should look like this

Col1
  1
  1
  2
  2
  2
  3
  3

Please suggest me the way how can I do it by using loop or any other way because I have more than 300 unique values.

Upvotes: 9

Views: 8907

Answers (1)

jezrael
jezrael

Reputation: 862406

Use pd.factorize:

df['Col1'] = pd.factorize(df.Col1)[0] + 1
print (df)
   Col1
0     1
1     1
2     2
3     2
4     2
5     3
6     3

Factorizing values

Another numpy.unique solution, but slower in huge dataframe:

_,idx = np.unique(df['Col1'],return_inverse=True) 
df['Col1'] = idx + 1
print (df)
   Col1
0     1
1     1
2     2
3     2
4     2
5     3
6     3

Last you can convert values to categorical - mainly because less memory usage:

df['Col1'] = pd.factorize(df.Col1)[0]
df['Col1'] = df['Col1'].astype("category")
print (df)
  Col1
0    0
1    0
2    1
3    1
4    1
5    2
6    2

print (df.dtypes)
Col1    category
dtype: object

Upvotes: 8

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