吴裕雄 python深度学习与实践（5）

import numpy as np

data = np.mat([[1,200,105,3,False],
               [2,165,80,2,False],
               [3,184.5,120,2,False],
               [4,116,70.8,1,False],
               [5,270,150,4,True]])
row = 0
for line in data:
    row += 1
print(row)
print(data.size)

import numpy as np

data = np.mat([[1,200,105,3,False],
               [2,165,80,2,False],
               [3,184.5,120,2,False],
               [4,116,70.8,1,False],
               [5,270,150,4,True]])
print(data[0,3])
print(data[0,4])

import numpy as np

data = np.mat([[1,200,105,3,False],
               [2,165,80,2,False],
               [3,184.5,120,2,False],
               [4,116,70.8,1,False],
               [5,270,150,4,True]])
print(data)
col1 = []
for row in data:
    print(row)
    col1.append(row[0,1])

print(col1)
print(np.sum(col1))
print(np.mean(col1))
print(np.std(col1))
print(np.var(col1))

import pylab
import numpy as np
import scipy.stats as stats

data = np.mat([[1,200,105,3,False],
               [2,165,80,2,False],
               [3,184.5,120,2,False],
               [4,116,70.8,1,False],
               [5,270,150,4,True]])

col1 = []
for row in data:
    col1.append(row[0,1])

stats.probplot(col1,plot=pylab)
pylab.show()

import pandas as pd
import matplotlib.pyplot as plot

rocksVMines = pd.DataFrame([[1,200,105,3,False],
                            [2,165,80,2,False],
                            [3,184.5,120,2,False],
                            [4,116,70.8,1,False],
                            [5,270,150,4,True]])
print(rocksVMines)
dataRow1 = rocksVMines.iloc[1,0:3]
dataRow2 = rocksVMines.iloc[2,0:3]
print(type(dataRow1))
print(dataRow1)
print(dataRow2)
plot.scatter(dataRow1, dataRow2)
plot.xlabel("Attribute1")
plot.ylabel("Attribute2")
plot.show()

dataRow3 = rocksVMines.iloc[3,0:3]
plot.scatter(dataRow2, dataRow3)
plot.xlabel("Attribute2")
plot.ylabel("Attribute3")
plot.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plot

filePath = ("G:\MyLearning\TensorFlow_deep_learn\data\dataTest.csv")
dataFile = pd.read_csv(filePath,header=None, prefix="V")
print(np.shape(dataFile))
dataRow1 = dataFile.iloc[100,1:300]
dataRow2 = dataFile.iloc[101,1:300]
plot.scatter(dataRow1, dataRow2)
plot.xlabel("Attribute1")
plot.ylabel("Attribute2")
plot.show()

import pandas as pd
import matplotlib.pyplot as plot

filePath = ("G:\MyLearning\TensorFlow_deep_learn\data\dataTest.csv")
dataFile = pd.read_csv(filePath,header=None, prefix="V")

target = []
for i in range(200):
    if dataFile.iat[i,10] >= 7:
        target.append(1.0)
    else:
        target.append(0.0)

dataRow = dataFile.iloc[0:200,10]
plot.scatter(dataRow, target)
plot.xlabel("Attribute")
plot.ylabel("Target")
plot.show()

import random as rd
import pandas as pd
import matplotlib.pyplot as plot

filePath = ("G:\MyLearning\TensorFlow_deep_learn\data\dataTest.csv")
dataFile = pd.read_csv(filePath,header=None, prefix="V")

target = []
for i in range(200):
    if dataFile.iat[i,10] >= 7:
        target.append(1.0 + rd.uniform(-0.3, 0.3))
    else:
        target.append(0.0 + rd.uniform(-0.3, 0.3))
dataRow = dataFile.iloc[0:200,10]
plot.scatter(dataRow, target, alpha=0.5, s=100)
plot.xlabel("Attribute")
plot.ylabel("Target")
plot.show()

from pylab import *
import pandas as pd
import matplotlib.pyplot as plot

filePath = ("G:\MyLearning\TensorFlow_deep_learn\data\dataTest.csv")
dataFile = pd.read_csv(filePath,header=None, prefix="V")

print(dataFile.head())
print(dataFile.tail())

summary = dataFile.describe()
print(summary)

array = dataFile.iloc[:,10:16].values
boxplot(array)
plot.xlabel("Attribute")
plot.ylabel("Score")
show()