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I have a function in which I want to represent my data like this:

Input is a column:[ 123 125 11 122 ...]
Output:  123  125
         125  11
         11   122

The function in python is like that:

def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
    a = dataset[i:(i+look_back), 0]
    dataX.append(a)
    dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)

My dataset is represented as 1-grams of integers in a csv file, so I was obliged to use the transpose () of the dataframe to use this function. The problem is that I find the dataframe size null, and the train and test data (after spliting) also empty.

the code is:

dataframe = pd.read_csv("train2.csv")

print(dataframe.shape)  # (0,150)
print("--------\n")
#dataframe= np.asarray(dataframe)
dataframe = dataframe.transpose()
print(dataframe.shape)   #(150,0)
print(dataframe.size)  # 0

dataset = dataframe.values
dataset = dataset.astype('float32')

# split into train and test sets
train_size = int(len(dataset) * 0.67)
print(len(dataset))
#print (train_size)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))

print(dataset[0:train_size,:])  #[]
print(train)                    # []
print(test)                     # []

# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

Any solution please?

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1 Answer 1

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Your first dataframe.shape is showing you that you have no rows. Pandas is probably treating your data as just the column names, so try pd.read_csv('train2.csv', header=None).

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