Since the question was not very clear to begin with and attempts to explain it were going in vain, I decided to download the dataset and do it for myself. So just to make sure we are working with the same dataset iris.head() will give you or something similar, a few names might be changed and a few values, but overall strucure will be the same.

Now the first four columns are features and the fifth one is target/output.

Now you will need your X and Y as numpy arrays, to do that use

X = iris[ ['sepal length:','sepal Width:','petal length','petal width']].values
Y = iris[['Target']].values

Now since Y is categorical Data, You will need to one hot encode it using sklearn's LabelEncoder and scale the input X to do that use

label_encoder = LabelEncoder()
Y = label_encoder.fit_transform(Y)
X = StandardScaler().fit_transform(X)

To keep with the norm of separate train and test data, split the dataset using

X_train , X_test, y_train, y_test = train_test_split(X,Y)

Now just train it on your model using X_train and y_train

clf = SVC(C=1.0, kernel='rbf').fit(X_train,y_train)

After this you can use the test data to evaluate the model and tune the value of C as you wish.

Edit Just in case you don't know where the functions are here are the import statements

from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler

First, I think you have an error in your CSV in the first row:

25.3, 12.4, 2.35, 4.89. 1, 2.35, 5.65, 7, 6.24, 5.52, M

I just assumed it should be 4.89, 1, and not 4.89. 1.

Second, I recommend you to use pandas to read that CSV, and then do this:

import pandas as pd
data = pd.read_csv('prueba.csv', header=None, usecols=[i for i in range(11)])
# the usecols=[i for i in range(11)] will create a list of numbers for your columns
# that line will make a dataframe called data, which will contain your data.
l = [i for i in range(10)]
X_train = data[l]
y_train = data[10]

This is the most easy way to have ready your data for any machine learning algorithm in scikit-learn.

Just slice the data, for instance for a classification problem:

>>> import numpy as np
>>> from sklearn.ensemble import ExtraTreesClassifier

>>> data_train = np.loadtxt('data_train.csv', delimiter=',')
>>> X = data_train[:, 1:]
>>> y = data_train[:, 0].astype(np.int)
>>> clf = ExtraTreesClassifier(n_estimators=100).fit(X, y)

Then make prediction on the test data that does not have the target label in the first column:

>>> data_test = np.loadtxt('data_test.csv', delimiter=',')
>>> print(clf.predict(data_test))

You can use csv2libsvm.py to convert csv to libsvm data

python csv2libsvm.py iris.csv libsvm.data 4 True

where 4 means target index, and True means csv has a header.

Finally, you can get libsvm.data as

0 1:5.1 2:3.5 3:1.4 4:0.2
0 1:4.9 2:3.0 3:1.4 4:0.2
0 1:4.7 2:3.2 3:1.3 4:0.2
0 1:4.6 2:3.1 3:1.5 4:0.2
...

from iris.csv

150,4,setosa,versicolor,virginica
5.1,3.5,1.4,0.2,0
4.9,3.0,1.4,0.2,0
4.7,3.2,1.3,0.2,0
4.6,3.1,1.5,0.2,0
...