%pylab inline

import pandas as pd

!head data/players_dat.csv

df = pd.read_csv('data/players_dat.csv')
df.head(5)

type(df)

df.sort('cash', ascending=0).head(5)

df.describe()

df.groupby(['gender']).score.mean().plot(kind='bar', title="Scores", rot=0)

from sklearn import datasets

# generate some clustered data (X == features, L == labels)
X, L = datasets.make_blobs(centers=4, cluster_std=0.5, random_state=2)
scatter(X[:,0], X[:,1])

scatter(X[:,0], X[:,1], c=L)

from sklearn.cluster import KMeans
km = KMeans(4)
km.fit(X);

scatter(X[:,0], X[:,1], c=km.labels_)

mu = km.cluster_centers_
print mu

scatter(X[:,0], X[:,1], c=km.labels_, alpha=0.5)
scatter(mu[:,0], mu[:,1], s=100, c=np.unique(km.labels_))

from skimage.data import camera

im_orig = camera()

print 'image shape:', im_orig.shape
print 'image (min,max):', im_orig.min(), im_orig.max()

gray()          # set default colormap to gray scale
imshow(im_orig)

from skimage.transform import resize, rotate, rescale

img = (resize(im_orig, (128, 128)) * 255).astype('uint16')
print img.dtype
print img.shape
print (img.min(), img.max())

im_r = rotate(img.astype('float')/255, angle=15, order=2)

plt.figure(figsize=(8,4))

plt.subplot(121)
plt.imshow(img)

plt.subplot(122)
plt.imshow(im_r)
plt.show()

h = hist(img)

# optimal threshold
from skimage.filter import threshold_otsu
thres = threshold_otsu(img)
print "otsu thres:", thres

plt.figure(figsize=(8,4))
plt.subplot(121)
plt.imshow(img > 127)
plt.title("Manual")
plt.axis('off')

plt.subplot(122)
plt.imshow(img > thres)
plt.title("Otsu")
plt.axis('off')

from skimage.filter import sobel

imshow(sobel(img))