使用熊猫在.csv中编辑整行并将其馈送到KNeighborsClassifier

Question

我是应用机器学习的新手，这里有一个数据集，其中包含一列巧克力中可可的百分比。 但是，当我将该列提供给KNeighborsClassifer的fit（）函数时，它将引发以下错误；

Traceback (most recent call last):
  File "/home/himanshu/ML Tut-2/ML_tut2.py", line 13, in <module>
    knn.fit(X_train, y_train)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/neighbors/base.py", line 765, in fit
    X, y = check_X_y(X, y, "csr", multi_output=True)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 542, in check_X_y
    ensure_min_features, warn_on_dtype, estimator)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 402, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: invalid literal for float(): 72%

我的代码是这个；

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd

choco = pd.read_csv('flavors_of_cacao.csv')

X = choco['Cocoa']
y = choco['Name']

X_train, y_train, X_test, y_test = train_test_split(X, y, random_state=0)

knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(X_train, y_train)

很明显，fit（）函数在可可列中需要一个浮点数，但是它正在获取'％'符号以及未经操作就无法转换为浮点数的数字。

请帮助我解决此问题。

编辑：

我已经编辑了CSV并从中删除了“％”符号，但是现在出现以下错误；

Traceback (most recent call last):
  File "/home/himanshu/ML Tut-2/ML_tut2.py", line 14, in <module>
    knn.fit(X_train, y_train)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/neighbors/base.py", line 765, in fit
    X, y = check_X_y(X, y, "csr", multi_output=True)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 552, in check_X_y
    check_consistent_length(X, y)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 173, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [1346, 449]

新的代码是；

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np

df = pd.read_csv('chocos.csv')

X = df[['Cocoa']]
y = df['Name']

X_train, y_train, X_test, y_test = train_test_split(X, y, random_state=0)

knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(X_train, y_train)

供参考，数据集是this 。

Answer 1

只需使用该列中没有百分号的值即可：

X = [[float(val.replace('%',''))] for val in choco['Cocoa']]