'KeyError:' 迭代 Pandas 数据框时？

Question

I have two lists Y_train and Y_test . At the moment they hold categorical data. Each element is either Blue or Green . They are going to be the targets for a Random Forest classifier. I need them encoded as 1.0s and 0.0s.

Here is a print(Y_train) to show you what the data frame looks like. The random numbers down the side are because the data has been shuffled. ( Y_test is the same, just smaller):

183      Blue
126      Blue
1        Blue
409      Blue
575    Green
         ...   
396      Blue
192      Blue
578    Green
838    Green
222      Blue
Name: Colour, Length: 896, dtype: object

To encode this I was going to simply loop over them and change each element to their encoded values:

for i in range(len(Y_train)):
        if Y_train[i] == 'Blue':
            Y_train[i] = 0.0
        else:
            Y_train[i] = 1.0

However, when I do this, I get the following:

Traceback (most recent call last):
  File "G:\Work\Colours.py", line 90, in <module>
    Main()
  File "G:\Work\Colours.py", line 34, in Main
    RandForest(X_train, Y_train, X_test, Y_test)
  File "G:\Work\Colours.py.py", line 77, in RandForest
    if Y_train[i] == 'Blue':
  File "C:\Users\Me\AppData\Roaming\Python\Python37\site-packages\pandas\core\series.py", line 1068, in __getitem__
    result = self.index.get_value(self, key)
  File "C:\Users\Me\AppData\Roaming\Python\Python37\site-packages\pandas\core\indexes\base.py", line 4730, in get_value
    return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
  File "pandas\_libs\index.pyx", line 80, in pandas._libs.index.IndexEngine.get_value
  File "pandas\_libs\index.pyx", line 88, in pandas._libs.index.IndexEngine.get_value
  File "pandas\_libs\index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 992, in pandas._libs.hashtable.Int64HashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 998, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 6

The weird thing is that it produces this error at different times. I've used flags and prints to see how far it gets. Sometimes it will get quite a few iterations into the loop, and then other times it will only do one or two iterations before breaking.

I'm assuming I just don't quite understand how you're supposed to iterate over data frames properly. If someone with more experience with this stuff could help me out that would be great.

Answer 1

Try:

 Y_train[Y_train == 'Blue']=0.0
 Y_train[Y_train == 'Green']=1.0

That should solve your issues.

Answer 2

In cases where you even have more number of labels than your current example(Blue and Green in your case), sklearn provides a label encoder that allows you to do this very easily using

from sklearn import preprocessing 

label_encoder = preprocessing.LabelEncoder() 

# Transforms the 'column' in your dataframe df
df['column']= label_encoder.fit_transform(df['column'])

Answer 3

If you are using a your own method to label encoding,it is better to create a separate encoded column rather than modifying original column.After that you can assign encoded column to your dataframe. As a example for your scenario.

encoded = np.ones((Y_train.shape[0],1))
for i in range(Y_train.shape[0]):
        if Y_train[i] == 'Blue':
            encoded[i] = 0

Note that this will only work for if you have two categories.

for multiple categories,you can use sklearn or pandas methods.

For multiple categories

Another approach is using pandas cat.codes .You can convert pandas series to a category and get the category codes.

Y_train = pd.Series(Y_train)
encoded = Y_train.astype("category").cat.codes

You can use sklearn Labelencoder to encode categorical data as well.

from sklearn.preprocessing import  LabelEncoder 
le = LabelEncoder()
encoded = le.fit_transform(Y_train)