These commands assume you are using the standard scikit-learn,pandas, statsmodels, and matplotlib. If you do not use this ML pipeline, move on, stop reading now.

### Cross-Validation

Cross-validation is a method used for splitting the training setup into test and train sets, and then training the algorithm on the training set and evaluating the algorithm on the test set. You do not need to divide these up manually, it can be done very easily with scikit-learn

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,
y,
test_size=0.4,
random_state=0)

It’s important to understand that train_test_split does NOT preserve you percentage of samples for each class. If you want to perserve this (and if you have unbalanced classes, you most certainly do), use StratifiedKFold:

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,
y,
test_size=0.4,
random_state=0)

### Scaling/Normalizing Features

If different columns values have different magnitudes, you are going to need to normalize your features before you do any training. To do that, you can use scikit-learn:

scaler = preprocessing.StandardScaler()
X = scaler.fit_transform(X)

From the documentation, you can read that StandardScaler subtracts the mean and scales to unit variance.

### Converting A Pandas Data Frame To Numpy Matrix For Scikit-Learn

Scikit-learn does not currently accept Panda’s Dataframes, but that is OK, because you can convert a dataframe into a numpy matrix easily enough with the following command:

 X = df.as_matrix().astype(np.float) 

Note this will only work if all of your data is boolean + numerical (no text)

### Dropping All Rows That Contain NAN in a Given Column

If a certain feature is required, you will need to drop any rows in your dataframe that contain NaN values for that feature. To do this, you can execute something simple like the following

 df = df[np.isfinite(df['FeatureColumn'])] 

### Dropping Useless Columns

Sometimes you have columns in your dataframe that you know are not useful for training a model, so drop them, in place:

 df.drop(['UselessColumn1', 'UselessColumn2'], axis=1, inplace=True) 

If you look at the columns in your dataframe, UselessColumn1 and UselessColumn2 should be gone.

### Discretizing Columns In A Dataframe

Supervised learning algorithms usually like looking at numerical features, so if you need to convert a column that contains finite text classes (states, countries, etc.) to numbers, use scikit-learn:

Before:

In [72]:

df.State
Out[72]:
0     KS
1     OH
2     NJ
3     OH
4     OK
5     AL
6     MA
7     MO
8     LA
9     WV
10    IN
11    RI
12    IA
13    MT
14    IA
...

And After:

from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
df['State'] = label_encoder.fit_transform(df['State'])

In [74]:

df.State
Out[74]:
0     16
1     35
2     31
3     35
4     36
5      1
6     19
7     24
8     18
9     49
10    15
11    39
12    12
13    26
14    12
...
3318    36
3319    50
3320    10
3321    46
3322    20
3323    15
3324    49

### Boolean ANDs or ORs with two columns

Sometimes you want to filter a given set of rows in a dataframe by ANDing or ORing together two columns and take the result. This can be done like this

valid_columns = (df.Column1) | (df.Column2)
df_filtered = df[valid_columns,]

The parentheses are very important, otherwise pandas will complain.

### plotting multiple columns in pandas via subplots

df1[["column1","column1","column1"]].plot(kind='hist',subplots=True, figsize=(12, 6),bins=100);