How to find outliers in Python?

This recipe helps you find outliers in Python
In [2]:
## How to find outliers in Python 
def Kickstarter_Example_30():
    print()
    print(format('How to find outliers in Python', '*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # Load libraries
    from sklearn.covariance import EllipticEnvelope
    from sklearn.datasets import make_blobs
    import matplotlib.pyplot as plt

    # Create simulated data
    X, _ = make_blobs(n_samples = 100,
                      n_features = 20,
                      centers = 7,
                      cluster_std = 1.1,
                      shuffle = True,
                      random_state = 42)

    # Detect Outliers
    # Create detector
    outlier_detector = EllipticEnvelope(contamination=.1)

    # Fit detector
    outlier_detector.fit(X)

    # Predict outliers
    print(); print(X)
    print(); print(outlier_detector.predict(X))
    plt.scatter(X[:,0], X[:,1])

    # Show the scatterplot
    plt.show()

Kickstarter_Example_30()
**************************How to find outliers in Python**************************

[[ 4.93252797  7.68541287 -3.97876821 ...  4.52684633 -3.24863123
   9.41974416]
 [-9.3234536   4.59276437 -4.39779468 ... -7.09597087  8.20227193
   2.26134033]
 [-8.7338198   3.08658417 -3.49905765 ... -6.82385124  8.775862
   1.38825176]
 ...
 [-2.83969517 -6.07980264  6.47763993 ... -9.36607752 -2.57352093
  -9.39410402]
 [-2.1671993  10.63717797  5.58330442 ...  0.50898027 -1.25365592
  -5.02572796]
 [ 7.21074034  9.28156979 -3.54240715 ...  3.89782083 -3.2259812
  11.03335594]]

[-1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1 -1
  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1  1  1  1  1  1
  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1  1  1  1  1
  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1 -1  1  1  1  1  1
  1  1  1 -1]