How to do a linear regression with variables from a dataframe [Python]

Question

I have two variables A and B in a dataframe. When i try this:

x=df.['A']
y=df.['B']

M = LinearRegression()
M.fit(A,B)

I get the following error

Expected 2D array, got 1D array instead:
array=[86.  0. 86. ...  0.  0.  0.].
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.

Although when i try to apply the reshape as suggested, i got a "'Series' object has no attribute 'reshape'"

How can I get around this?

Ynjxsjmh · Accepted Answer · 2022-08-13 14:08:48Z

2

You can try to change x to DataFrame rather than Series.

x = df[['A']]
y = df['B']

answered Aug 13, 2022 at 14:08

Ynjxsjmh

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3 Comments

ViB Over a year ago

I think that worked, whats the difference between dataframe and series?

Ynjxsjmh Over a year ago

@ViB row/column in DataFrame is a Series, DataFrame can be 2D array while Series is 1D.

Ynjxsjmh Over a year ago

@ViB Locating with list of label is to subset the DataFrame, here you can take df[['A']] as x = df[['a', 'b', 'c']] which mean you want to use feature a, b, c which is columns in DataFrame to predict target B.

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