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繪制圖表，進行數據可視化

常用的操作

axis越大，深入的就越大，反之深入的越小

numpy.array中只可以有一種數據類型

List每次處理對象會判斷數據類型，可存放多種類數據，但維護成本較高

數據分析工具

數據分析工具

import?numpy?as?np
print('FFT:')
print(np.fft.fft(np.array([1,1,1,1,1,1,1,1,1])))
print('Coef:')
print(np.corrcoef([1,0,1],[0,2,1]))
print('Poly:')
print(np.poly1d([2,1,3]))

#?Set
s1=pd.Series(list(range(10,18)),index=pd.date_range("20170301",periods=8))
df["F"]=s1
print(df)
df.at[dates[0],"A"]=0
print(df)
df.iat[1,1]=1
df.loc[:,"D"]=np.array([4]*len(df))
print(df)
df2=df.copy()
df2[df2>0]=-df2
print(df2)

#?Select切片
print(df["A"])
print(type(df["A"]))
print(df[:3])
print(df["20170301":"20170304"])
print(df.loc[dates[0]])
print(df.loc["20170301":"20170304",["B","D"]])
print(df.at[dates[0],"C"])
print(df.at["20170301","C"])

print(df.iloc[1:3,2:4])
print(df.iloc[1,4])
print(df.iat[1,4])

print(df[df.B>0][df.A<0])
print(df[df>0.1])
print(df[df["E"].isin([1,2])])

def?main():
????#?Data?Structure
????s=pd.Series([i*2?for?i?in?range(1,11)])
????print(type(s))
????dates=pd.date_range("20170301",periods=8)
????df=pd.DataFrame(np.random.rand(8,5),index=dates,columns=list("ABCDE"))
????print(df)
????df?=?pd.DataFrame({"A":?1,?"B":?pd.Timestamp("20170301"),?"C":?pd.Series(1,?index=list(range(4)),?dtype="float32"),
???????????????????????"D":?np.array([3]*4,?dtype="float32"),?"E":?pd.Categorical(["police",?"student",?"teacher",?"doctor"])})
????print(df)

#導包
import?numpy?as?np
import?matplotlib.pyplot?as?plt

#橫軸設置
x?=?np.linspace(-np.pi,np.pi,512,endpoint=True)

#構建sin，cos函數
c,?s?=?np.cos(x),?np.sin(x)

#繪圖
plt.figure()
plt.plot(x,s)
plt.plot(x,c)

plt.show()

#2-Optimizer
from?scipy.optimize?import?minimize
def?rosen(x):????#hansu
????return?sum(100.0*(x[1:]-x[:-1]**2.0)**2.0?+?(1-x[:-1]**2.0))
x0=np.array([1.3,0.7,0.8,1.9,1.2])
res?=?minimize(rosen,x0,method="nelder-mead",options={"xtol":1e-8,"disp":True})
print("ROSE?MINI:",res.x)

def?func(x):
????return?-(2*x[0]*x[1]+2*x[0]-x[0]**2-2*x[1]**2)
def??func_deriv(x):
????dfdx0?=?-(-2*x[0]?+?2*x[1]?+?2)
????dfdx1?=?-(2*x[0]?-?4*x[1])
????return?np.array([dfdx0,dfdx1])
cons?=?({"type":"eq","fun":lambda?x:np.array([x[0]**3.0-x[1]]),"jac":lambda?x:np.array([3.0*(x[0]**2.0),-1.0])},
{"type":"ineq","fun":lambda?x:np.array([x[1]-1]),"jac":lambda?x:np.array([0.0,1.0])})
res=minimize(func,[-1.0,1.0],jac?=?func_deriv,constraints=cons,method='SLSQP',options?=?{'disp':True})
print("RESTRICT:",res)
from??scipy.optimize?import?root
def?fun(x):
????return??x+2*np.cos(x)
sol=root(fun,0.1)
print?("ROOT:",sol.x,sol.fun)

有結果不對的可以自己比對一下，視頻可能有點模糊和拖動

給你們代碼，寫的啥咱也不知道，咱也不敢問
#?
__author__?=?'aaron'
__date__?=?'7/20/2019?11:19?AM'

import?numpy
from?keras.models?import?Sequential
from?keras.layers?import?Dense,?Activation
from?keras.optimizers?import?SGD

def?main():
????from?sklearn.datasets?import?load_iris
????iris?=?load_iris()
????print(iris["target"])
????from?sklearn.preprocessing?import??LabelBinarizer
????print(LabelBinarizer().fit_transform(iris["target"]))

????from?sklearn.model_selection?import?train_test_split

????train_data,?test_data,?train_target,?test_target?=?train_test_split(iris.data,?iris.target,?test_size=0.2,?random_state=1)

????labels_train?=?LabelBinarizer().fit_transform(train_target)
????labels_test?=?LabelBinarizer().fit_transform(test_target)


????model?=?Sequential(
????????[

????????????Dense(5,?input_dim=4),
????????????Activation("relu"),
????????????Dense(3),
????????????Activation("sigmoid"),

????????]
????)

????sgd?=?SGD(lr=0.01,?decay=1e-6,?momentum=0.9,?nesterov=True)
????model.compile(optimizer=sgd,?loss="categorical_crossentropy")
????model.fit(train_data,?labels_train,?nb_epoch=200,?batch_size=40)
????print(model.predict_classes(test_data))


if?__name__?==?"__main__":
????main()

sklearn(scikit-learn)主要用于數據挖掘與機器學習

機器學習：由數據經過一個過程獲得結果，本質是一個函數