Expand docs for export functionality

Now the docs include an example code output.

README.md

@@ -181,7 +181,27 @@ print(tpot.score(X_train, y_train, X_test, y_test))
 tpot.export('tpot_mnist_pipeline.py')
 ```
 
-Running this code should discover a pipeline that achieves ~97% testing accuracy, and the corresponding Python code should be exported to the `tpot_mnist_pipeline.py` file.
+Running this code should discover a pipeline that achieves ~97% testing accuracy, and the corresponding Python code should be exported to the `tpot_mnist_pipeline.py` file and look similar to the following:
+
+```python
+import numpy as np
+import pandas as pd
+
+from sklearn.cross_validation import StratifiedShuffleSplit
+from sklearn.linear_model import LogisticRegression
+
+# NOTE: Make sure that the class is labeled 'class' in the data file
+tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')
+training_indeces, testing_indeces = next(iter(StratifiedShuffleSplit(tpot_data['class'].values, n_iter=1, train_size=0.75)))
+
+
+result1 = tpot_data.copy()
+
+# Perform classification with a logistic regression classifier
+lrc1 = LogisticRegression(C=2.8214285714285716)
+lrc1.fit(result1.loc[training_indeces].drop('class', axis=1).values, result1.loc[training_indeces, 'class'].values)
+result1['lrc1-classification'] = lrc1.predict(result1.drop('class', axis=1).values)
+```
 
 ## Want to get involved with TPOT?
 

docs/sources/examples/MNIST_Example.md

@@ -17,4 +17,24 @@ print(tpot.score(X_train, y_train, X_test, y_test))
 tpot.export('tpot_mnist_pipeline.py')
 ```
 
-Running this code should discover a pipeline that achieves ~97% testing accuracy, and the corresponding Python code should be exported to the `tpot_mnist_pipeline.py` file.
+Running this code should discover a pipeline that achieves ~97% testing accuracy, and the corresponding Python code should be exported to the `tpot_mnist_pipeline.py` file and look similar to the following:
+
+```python
+import numpy as np
+import pandas as pd
+
+from sklearn.cross_validation import StratifiedShuffleSplit
+from sklearn.linear_model import LogisticRegression
+
+# NOTE: Make sure that the class is labeled 'class' in the data file
+tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')
+training_indeces, testing_indeces = next(iter(StratifiedShuffleSplit(tpot_data['class'].values, n_iter=1, train_size=0.75)))
+
+
+result1 = tpot_data.copy()
+
+# Perform classification with a logistic regression classifier
+lrc1 = LogisticRegression(C=2.8214285714285716)
+lrc1.fit(result1.loc[training_indeces].drop('class', axis=1).values, result1.loc[training_indeces, 'class'].values)
+result1['lrc1-classification'] = lrc1.predict(result1.drop('class', axis=1).values)
+```