Pythonフレーズ2

(plt.scatter())

plt.scatter()

(plt.imshow(img,''))

plt.imshow(img,'')

(re.sub())

re.sub()

([^a])

[^a]

(a+)

a+

($$a_1$$)

$$a_1$$

($$x^2$$)

$$x^2$$

($$\sqrt x$$)

$$\sqrt x$$

($$\sin x$$)

$$\sin x$$

($$\frac{a}{b}$$)

$$\frac{a}{b}$$

($$\sum_{k=1}^n a_k$$)

$$\sum_{k=1}^n a_k$$

($$\prod_{k=1}^n a_k$$)

$$\prod_{k=1}^n a_k$$

(plt.hist())

plt.hist()

(.strip())

.strip()

(.keys())

.keys()

(.values())

.values()

(.items())

.items()

(print('{0},{1},{0}'.format('Hello','world')))

print('{0},{1},{0}'.format('Hello','world'))

(input(''))

input('')

(*args)

*args

(**kwargs)

**kwargs

(___doc___)

___doc___

(lambda)

lambda

(yield)

yield

(if __name__=='__main__')

if __name__=='__main__'

(__del__():)

__del__():

(self)

self

(readlines())

readlines()

(os.rename())

os.rename()

(os.mkdir)

os.mkdir

(os.rmdir)

os.rmdir

(os.remove)

os.remove

(X.T)

X.T

(np.linalg.inv())

np.linalg.inv()

(from sklearn.linear_model import LinearRegression)

from sklearn.linear_model import LinearRegression

(model=LinearRegression)

model=LinearRegression

(model.fit())

model.fit()

(model.coef_)

model.coef_

(model.score())

model.score()

(model.predict())

model.predict()

(import seaborn as sns)

import seaborn as sns

(sns.distplot())

sns.distplot()

(sns.pairplot())

sns.pairplot()

(from sklearn.model_selection import train_test_split)

from sklearn.model_selection import train_test_split

(from sklearn.externals import joblib)

from sklearn.externals import joblib

(joblib.dump())

joblib.dump()

(joblib.load())

joblib.load()

(np.set_printoptions(precision=3,suppress=True))

np.set_printoptions(precision=3,suppress=True)

(high=mean[col]-3*sigma[col])

high=mean[col]-3*sigma[col]

(df[(df[col]>low)&(df[col]<high)])

df[(df[col]>low)&(df[col]<high)]

(import chainer.links as L)

import chainer.links as L

(fc=L.Linear())

fc=L.Linear()

(import chainer.functions as F)

import chainer.functions as F

(F.relu())

F.relu()

(F.mean_squared_erro())

F.mean_squared_erro()

(x.astype('f'))

x.astype('f')

(super().__init__())

super().__init__()

(with self.init_scope():)

with self.init_scope():

(list(zip()))

list(zip())

(model=L.Classifier())

model=L.Classifier()

(chainer.datasets.split_dataset_random())

chainer.datasets.split_dataset_random()

(chainer.optimizers.SGD())

chainer.optimizers.SGD()

(optimizer.setup(model))

optimizer.setup(model)

(L.BatchNormalization())

L.BatchNormalization()

(chainer.iterators.SerialIterator())

chainer.iterators.SerialIterator()

(from chainer.training import extensions)

from chainer.training import extensions