import pandas as pd
from sklearn import svm, metrics
from sklearn.model_selection import train_test_split

csv = pd.read_csv('iris.csv')

csv_data = csv[["SepalLength", "SepalWidth", "PetalLength", "PetalWidth"]]
csv_label = csv["Name"]

train_data, test_data, train_label, test_label = train_test_split(csv_data, csv_label)

clf = svm.SVC(gamma = 'auto')
clf.fit(train_data, train_label)
pre = clf.predict(test_data)

Error:
Traceback (most recent call last):
  File "iris-train2.py", line 9, in <module>
    csv_data = csv[["SepalLength", "SepalWidth", "PetalLength", "PetalWidth"]]
  File "C:\Users\KarinSugiura\Desktop\MachineLearning\learning\lib\site-packages\pandas\core\frame.py", line 2934, in __getitem__
    raise_missing=True)
  File "C:\Users\My name\lib\site-packages\pandas\core\indexing.py", line 1354, in _convert_to_indexer
    return self._get_listlike_indexer(obj, axis, **kwargs)[1]
  File "C:\Users\My name\lib\site-packages\pandas\core\indexing.py", line 1161, in _get_listlike_indexer
    raise_missing=raise_missing)
  File "C:\Users\My name\lib\site-packages\pandas\core\indexing.py", line 1246, in _validate_read_indexer
    key=key, axis=self.obj._get_axis_name(axis)))
KeyError: "None of [Index(['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'], dtype='object')] are in the [columns]"

X = csv[[...column names go here...]].values # gets numpy array
y = csv["grouping variable name goes here"].values # gets numpy array

# you need to encode grouping variable y, e.g. using LabelEncoder from scikit-learn.
# you probably need to do column-wise scaling of your data,
# e.g. using StandardScaler or Pandas facilities. 

# do splitting, training and testing with X, y and scikit-learn