Sklearn changing string class label to int

you can use factorize method:

In [13]: df['fruit'] = pd.factorize(df['fruit'])[0].astype(np.uint16)

In [14]: df
Out[14]:
   index  fruit  quantity  price
0      0      0         5   0.99
1      1      0         2   0.99
2      2      1         4   0.89
3      4      2         1   1.64
4  10023      3        10   0.92

In [15]: df.dtypes
Out[15]:
index         int64
fruit        uint16
quantity      int64
price       float64
dtype: object

alternatively you can do it this way:

In [21]: df['fruit'] = df.fruit.astype('category').cat.codes

In [22]: df
Out[22]:
   index  fruit  quantity  price
0      0      0         5   0.99
1      1      0         2   0.99
2      2      3         4   0.89
3      4      1         1   1.64
4  10023      2        10   0.92

In [23]: df.dtypes
Out[23]:
index         int64
fruit          int8
quantity      int64
price       float64
dtype: object

Use factorize and then convert to categorical if necessary:

df.fruit = pd.factorize(df.fruit)[0]
print (df)
   fruit  quantity  price
0      0         5   0.99
1      0         2   0.99
2      1         4   0.89
3      2         1   1.64
4      3        10   0.92

---

df.fruit = pd.Categorical(pd.factorize(df.fruit)[0])
print (df)
  fruit  quantity  price
0     0         5   0.99
1     0         2   0.99
2     1         4   0.89
3     2         1   1.64
4     3        10   0.92

print (df.dtypes)
fruit       category
quantity       int64
price        float64
dtype: object

Also if need count from 1:

df.fruit = pd.Categorical(pd.factorize(df.fruit)[0] + 1)
print (df)
  fruit  quantity  price
0     1         5   0.99
1     1         2   0.99
2     2         4   0.89
3     3         1   1.64
4     4        10   0.92

You can use sklearn.preprocessing

from sklearn import preprocessing

le = preprocessing.LabelEncoder()
le.fit(df.fruit)
df['categorical_label'] = le.transform(df.fruit)

Transform labels back to original encoding.

le.inverse_transform(df['categorical_label'])