Error "'numpy.ndarray' object has no attribute 'values'"

Question

I want to shift my time series data, but I am getting the following error:

AttributeError: 'numpy.ndarray' object has no attribute 'values'

This is my code:

def create_dataset(datasets):
    #series = dataset
    temps = DataFrame(datasets.values)
    dataframes = concat(
        [temps, temps.shift(-1), temps.shift(-2), temps.shift(-3)], axis=1)
    lala = numpy.array(dataframes)
    return lala

    # Load
    dataframe = pandas.read_csv('zahlenreihe.csv', index_col=False,
    engine='python', header=None)
    dataset = dataframe.values
    dataset = dataset.astype('float32')

    # Split
    train_size = int(len(dataset) * 0.70)
    test_size = len(dataset) - train_size
    train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

    # Create
    trainX = create_dataset(train)

I think the following line is wrong:

temps = DataFrame(datasets.values)

My zahlenreihe.csv file (number sequence) just has integers ordered like:

How should I handle it?

A pandas Dataframe or Dataset (column of a frame) has a .values attribute (probably a property). The result in is numpy array. But don't try to apply that twice. An array does not have a values attribute. Check the type of datasets or whatever object your error is complaining about. — hpaulj
– hpaulj, Commented Jan 10, 2017 at 2:24
Hmm, cant follow you. so i cant use a array to shift like the way i want to? datasets is a array yes — T.Setso
– T.Setso, Commented Jan 10, 2017 at 2:27

Peter Mortensen · Accepted Answer · 2022-09-23 12:19:24Z

27

The solution:

The given dataset was already an array, so I didn’t need to call .value.

edited Sep 23, 2022 at 12:19

Peter Mortensen

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answered Jan 10, 2017 at 3:26

T.Setso

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Peter Mortensen · Accepted Answer · 2022-09-23 12:22:47Z

3

The problem lies in the following line:

df = StandardScaler().fit_transform(df)

It returns a NumPy array (see the documentation), which does not have a drop function. You would have to convert it into a pd.DataFrame first!

new_df = pd.DataFrame(StandardScaler().fit_transform(df), columns=df.columns, index=df.index)

edited Sep 23, 2022 at 12:22

Peter Mortensen

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answered Mar 20, 2021 at 18:44

Michael

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