Convert categorical data in pandas dataframe

Question

I have a dataframe with this type of data (too many columns):

col1        int64
col2        int64
col3        category
col4        category
col5        categ         


        

Answer 1

@Quickbeam2k1 ,see below -

dataset=pd.read_csv('Data2.csv')
np.set_printoptions(threshold=np.nan)
X = dataset.iloc[:,:].values

Using sklearn

from sklearn.preprocessing import LabelEncoder
labelencoder_X=LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])

Answer 2

Here multiple columns need to be converted. So, one approach i used is ..

for col_name in df.columns:
    if(df[col_name].dtype == 'object'):
        df[col_name]= df[col_name].astype('category')
        df[col_name] = df[col_name].cat.codes

This converts all string / object type columns to categorical. Then applies codes to each type of category.

Answer 3

For a certain column, if you don't care about the ordering, use this

df['col1_num'] = df['col1'].apply(lambda x: np.where(df['col1'].unique()==x)[0][0])

If you care about the ordering, specify them as a list and use this

df['col1_num'] = df['col1'].apply(lambda x: ['first', 'second', 'third'].index(x))

Answer 4

First, to convert a Categorical column to its numerical codes, you can do this easier with: dataframe['c'].cat.codes.
Further, it is possible to select automatically all columns with a certain dtype in a dataframe using select_dtypes. This way, you can apply above operation on multiple and automatically selected columns.

First making an example dataframe:

In [75]: df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':list('abcab'),  'col3':list('ababb')})

In [76]: df['col2'] = df['col2'].astype('category')

In [77]: df['col3'] = df['col3'].astype('category')

In [78]: df.dtypes
Out[78]:
col1       int64
col2    category
col3    category
dtype: object

Then by using select_dtypes to select the columns, and then applying .cat.codes on each of these columns, you can get the following result:

In [80]: cat_columns = df.select_dtypes(['category']).columns

In [81]: cat_columns
Out[81]: Index([u'col2', u'col3'], dtype='object')

In [83]: df[cat_columns] = df[cat_columns].apply(lambda x: x.cat.codes)

In [84]: df
Out[84]:
   col1  col2  col3
0     1     0     0
1     2     1     1
2     3     2     0
3     4     0     1
4     5     1     1

Answer 5

For converting categorical data in column C of dataset data, we need to do the following:

from sklearn.preprocessing import LabelEncoder 
labelencoder= LabelEncoder() #initializing an object of class LabelEncoder
data['C'] = labelencoder.fit_transform(data['C']) #fitting and transforming the desired categorical column.

Answer 6

One of the simplest ways to convert the categorical variable into dummy/indicator variables is to use get_dummies provided by pandas. Say for example we have data in which sex is a categorical value (male & female) and you need to convert it into a dummy/indicator here is how to do it.

tranning_data = pd.read_csv("../titanic/train.csv")
features = ["Age", "Sex", ] //here sex is catagorical value
X_train = pd.get_dummies(tranning_data[features])
print(X_train)

Age Sex_female Sex_male
20    0          1
33    1          0
40    1          0
22    1          0
54    0          1