re-arranging a numpy array to 1d

Question

here is a numpy array i call this_col = [18 18 18 ... 24 24 24]

I have tried to reshape my data in many ways

        print("yo")
        print(this_col.shape)
        try:
            min_max_scaler = preprocessing.MinMaxScaler()
            print(this_col)
            this_col = pd.Series(min_max_scaler.fit_transform(this_col))
        except Exception as e:
            print("the exception ")
            print(e)
            try:
                print("no og ")
                this_col = this_col.reshape(-1, 1)
                print(this_col)
                min_max_scaler = preprocessing.MinMaxScaler()
                this_col = pd.Series(min_max_scaler.fit_transform(this_col))
            except Exception as e:
                print("the exception ")
                print(e)
                try:
                    print("no .reshape(-1, 1) ")
                    this_col = this_col.reshape(1, -1)
                    print(this_col)
                    min_max_scaler = preprocessing.MinMaxScaler()
                    this_col = pd.Series(min_max_scaler.fit_transform(this_col))
                except Exception as e:
                    print("the exception ")
                    print(e)
                    print("no .reshape(1, -1) ")
                    print(9/0)

below is the output i recieve from this code

  yo
(34144,)
[18 18 18 ... 24 24 24]
the exception 
Expected 2D array, got 1D array instead:
array=[18. 18. 18. ... 24. 24. 24.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
no og 
[[18]
 [18]
 [18]
 ...
 [24]
 [24]
 [24]]
the exception 
Data must be 1-dimensional
no .reshape(-1, 1) 
[[18 18 18 ... 24 24 24]]
the exception 
Data must be 1-dimensional
no .reshape(1, -1) 
ZeroDivisionError: division by zero

surely 1 of these 3 arrangments must have worked!!!! >:(

UPDATE: i have redone the example above to include shape and exception messages

UPDATE AGAIN: im starting to suspect the issue may be with the min_max function. the first error states "Expected 2D array, got 1D array instead" and then the second and third error states "Data must be 1-dimensional". what does it want?

Why do you do print(9/0)??? Why not use raise ZeroDivisionError? — Eli Harold
– Eli Harold, Commented Feb 17, 2022 at 14:16
Looks like this_col is already initially 1D, before you start trying to reshape it. — jjramsey
– jjramsey, Commented Feb 17, 2022 at 14:18

Mikel Irazola · Accepted Answer · 2022-02-17 14:27:28Z

1

You can use the flatten() method:

a = np.array([[1,2,3,1],[8,9,4,1],[7,6,5,1],[7,6,5,1]])
print(a.shape) # gives (4,4)
print(a.flatten().shape) # gives(16,)

Edit: You can read further info on the documentation

answered Feb 17, 2022 at 14:27

Mikel Irazola

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2 Comments

tgmjack Over a year ago

i have tried many combinations of flatten and reshape

Mikel Irazola Over a year ago

as @jjramsey has stated, it looks that "this_col" is already 1D. Try looking at this_col.shape

jjramsey · Accepted Answer · 2022-02-17 16:23:23Z

Your error messages are coming from two different places.

On the one hand, the error message "Expected 2D array, got 1D array instead" comes from passing a 1D array to min_max_scaler.fit_transform() when it needs a 2D array.

On the other hand, the error message "Data must be 1-dimensional" comes from passing a 2D array to the pd.Series() constructor when it needs a 1D array.

The expression pd.Series(min_max_scaler.fit_transform(this_col)) is always going to fail because if this_col is 1D, then min_max_scaler.fit_transform() will fail, and if this_col is 2D, then the output of min_max_scaler.fit_transform() will also be 2D, and pd.Series() cannot accept that output.

You probably want to do something like this:

this_col_transformed = min_max_scaler.fit_transform(this_col.reshape(-1, 1))
this_col_series = pd.Series(this_col_transformed.ravel())

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