- from pylab import *
- import pandas as pd
- import matplotlib.pyplot as plot
- import numpy as np
- filePath = ("G:\\MyLearning\\TensorFlow_deep_learn\\data\\dataTest.csv")
- dataFile = pd.read_csv(filePath,header=None, prefix="V")
- summary = dataFile.describe()
- dataFileNormalized = dataFile.iloc[:,1:6]
- for i in range(1,6):
- mean = summary.iloc[1, i]
- sd = summary.iloc[2, i]
- dataFileNormalized.iloc[:,(i-1)] = (dataFileNormalized.iloc[:,(i-1)] - mean) / sd
- array = dataFileNormalized.values
- print(np.shape(array))
- boxplot(array)
- plot.xlabel("Attribute")
- plot.ylabel("Score")
- show()
- from pylab import *
- import pandas as pd
- import matplotlib.pyplot as plot
- filePath = ("c://dataTest.csv")
- dataFile = pd.read_csv(filePath,header=None, prefix="V")
- summary = dataFile.describe()
- minRings = -1
- maxRings = 99
- nrows = 10
- for i in range(nrows):
- dataRow = dataFile.iloc[i,1:10]
- labelColor = (dataFile.iloc[i,10] - minRings) / (maxRings - minRings)
- dataRow.plot(color=plot.cm.RdYlBu(labelColor), alpha=0.5)
- plot.xlabel("Attribute")
- plot.ylabel("Score")
- show()
- import numpy as np
- 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")
- corMat = pd.DataFrame(dataFile.iloc[1:20,1:20].corr())
- plot.pcolor(corMat)
- plot.show()
- print(np.shape(corMat))
- print(corMat)
- from pylab import *
- import pandas as pd
- import matplotlib.pyplot as plot
- filePath = ("G:\\MyLearning\\TensorFlow_deep_learn\\data\\rain.csv")
- dataFile = pd.read_csv(filePath)
- summary = dataFile.describe()
- print(summary)
- array = dataFile.iloc[:,1:13].values
- boxplot(array)
- plot.xlabel("month")
- plot.ylabel("rain")
- show()
- from pylab import *
- import pandas as pd
- import matplotlib.pyplot as plot
- filePath = ("G:\\MyLearning\\TensorFlow_deep_learn\\data\\rain.csv")
- dataFile = pd.read_csv(filePath)
- minRings = -1
- maxRings = 99
- nrows = 12
- for i in range(nrows):
- dataRow = dataFile.iloc[i,1:13]
- labelColor = (dataFile.iloc[i,12] - minRings) / (maxRings - minRings)
- dataRow.plot(color=plot.cm.RdYlBu(labelColor), alpha=0.5)
- plot.xlabel("Attribute")
- plot.ylabel("Score")
- show()
- from pylab import *
- import pandas as pd
- import matplotlib.pyplot as plot
- filePath = ("G:\\MyLearning\\TensorFlow_deep_learn\\data\\rain.csv")
- dataFile = pd.read_csv(filePath)
- corMat = pd.DataFrame(dataFile.iloc[1:20,1:20].corr())
- plot.pcolor(corMat)
- plot.show()
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