Sure, here is a code sample demonstrating how to compute a SVM in python using `pandas` and `sklearn`:

```
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy score

# loading in data from csv file
data = ps.read_csv('data.csv')

# split the data into testing and training sets
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# train the svm classifier
svm = SVC(kernel='linear', C=1)
svm.fit(X_train, y_train)

# predict the target variable for testing data
y_pred = svm.predict(X_test)

# evaluate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
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