I have a Pandas dataframe with 10 columns, 9 of which are features to be used to predict the 10th column.
How is it ossible to convert this Pandas dataframe into X and y vectors to use in a linear regression problem?
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2 Answers
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If you have your dataframe loaded as the variable df, you can simply use this
X = df[['A','B','C']]
y = df['Z']
where A, B and C are your independent variables and Z is your dependent variable.
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$\begingroup$ is that possible to use X and y for train_test_split further? $\endgroup$MJay– MJay2019-09-02 07:39:20 +00:00Commented Sep 2, 2019 at 7:39
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$\begingroup$ Yes, it is possible. Please see sklearn's documentation for more details . Have a look here scikit-learn.org/stable/modules/generated/… $\endgroup$Gyan Ranjan– Gyan Ranjan2019-09-02 07:41:18 +00:00Commented Sep 2, 2019 at 7:41
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Are you looking for this?
#format the data as a numpy array to feed into the algorithm
X = np.asarray([np.asarray(df['Ind1']),np.asarray(df['Ind2']),np.asarray(df['Ind3'])])
y = np.asarray([np.asarray(df['Dep'])])
Or, simplified.
# array(['a', 'b', 'c'], dtype=object)
arr = df.index.to_numpy()
# array([1, 2, 3])
arr = df['A'].to_numpy()
