Reputation: 771
How convert the dataframe bellow:
m1,d1 m2,d2 m3,d3 m4,d4 ... 20-40 40-60 60-80 80-100
0 [0.986, 1.0] [1.0, 0.707] [0.986, 0.0] [0.972, 0.5] ... 0 0 0.655 1.0
1 [1.0, 0.0] [0.958, 0.0] [1.0, 0.516] [0.972, 0.5] ... 0 0 0.724 1.0
2 [0.958, 0.447] [0.972, 1.0] [1.0, 0.516] [0.901, 1.0] ... 0 0 0.724 1.0
3 [0.972, 0.894] [0.972, 1.0] [0.928, 1.0] [1.0, 0.354] ... 0 0 1.000 0.0
In array 1d bellow:
m1,d1 m2,d2 m3,d3 m4,d4 ... 20-40 40-60 60-80 80-100
0 0.986, 1.0 1.0, 0.707 0.986, 0.0 0.972, 0.5 ... 0 0 0.655 1.0
Output:
[4 rows x 10 columns]
[list([0.986, 1.0]) list([1.0, 0.0]) list([0.958, 0.447])
list([0.972, 0.894]) list([1.0, 0.707]) list([0.958, 0.0])
list([0.972, 1.0]) list([0.972, 1.0]) list([0.986, 0.0])
list([1.0, 0.516]) list([1.0, 0.516]) list([0.928, 1.0])
list([0.972, 0.5]) list([0.972, 0.5]) list([0.901, 1.0])
list([1.0, 0.354]) list([0.862, 0.408]) list([0.812, 1.0])
list([0.9, 0.204]) list([1.0, 0.816]) 0 0 0 0 0 0 0 0 0 0 0 0 0.655 0.724
0.724 1.0 1.0 1.0 1.0 0.0]
Script:
import pandas as pd
Ninput = [[[0.986, 1.0], [1.0, 0.707], [0.986, 0.0], [0.972, 0.5], [0.862, 0.408]], [[1.0, 0.0], [0.958, 0.0], [1.0, 0.516], [0.972, 0.5], [0.812, 1.0]], [[0.958, 0.447], [0.972, 1.0], [1.0, 0.516], [0.901, 1.0], [0.9, 0.204]], [[0.972, 0.894], [0.972, 1.0], [0.928, 1.0], [1.0, 0.354], [1.0, 0.816]]]
Noutput = [[0, 0, 0, 0.655, 1.0], [0, 0, 0, 0.724, 1.0], [0, 0, 0, 0.724, 1.0], [0, 0, 0, 1.0, 0.0]]
def conversionDataframe(dataNeuronInput,dataNeuronOutput):
""" Converts data to a dataframe pandas """
ni = pd.DataFrame(data= dataNeuronInput)
ni.columns = ['m1,d1', 'm2,d2', 'm3,d3', 'm4,d4', 'm5,d5']
no = pd.DataFrame(data= dataNeuronOutput)
no.columns = ['0-20', '20-40', '40-60', '60-80', '80-100']
return pd.concat([ni, no], axis=1)
dataFrameNoBinary = conversionDataframe(Ninput, Noutput)
print(dataFrameNoBinary.values.ravel('F'))
Note: Goals is to insert at neuron layer for training using classification binary with keras (Neural Network)
Upvotes: 1
Views: 200