'DataFrame' object has no attribute 'ravel' when transforming target variable?

Question

I was fitting a logistic regression with a subset dataset. After splitting the dataset and fitting the model, I got a error message of the following:

/Users/Eddie/anaconda/lib/python3.4/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True)

So I use target_newrdn = target_newrdn.ravel() to modify my target variable but it gave me this:

AttributeError: 'DataFrame' object has no attribute 'ravel'

I am wondering what the problem was and how can I fix? Can anyone help, please?

My code:

    from sklearn.datasets import fetch_covtype
    import numpy as np
    import pandas as pd

    from sklearn.utils import shuffle
    from sklearn.model_selection import train_test_split

    cov = fetch_covtype()
    cov_data = pd.DataFrame(cov.data)
    cov_target = pd.DataFrame(cov.target)

    data_newrdn = cov_data.head(n=10000)
    target_newrdn = cov_target.head(n=10000)


    target_newrdn = target_newrdn.ravel() ## I thought this could fix it??


    X_train2, X_test2, y_train2, y_test2 = train_test_split(data_newrdn, 
    target_newrdn, random_state=42)

    scaler.fit(X_train2)
    X_train_scaled2 = scaler.transform(X_train2)

    # Logistic Regression
    param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
    print(param_grid)
    grid = GridSearchCV(LogisticRegression(), param_grid, cv=kfold) 
    grid.fit(X_train_scaled2, y_train2)
    print("Best cross-validation score w/ kfold: 
    {:.2f}".format(grid.best_score_))
    print("Best parameters: ", grid.best_params_)

Answer 1

Clearly, dataframe does not have ravel function. Try:

target_newrdn.values.ravel()

target_newrdn.values returns a numpy ndarray and you perform ravel on that. Note this returns a flattened numpy array. You may need to convert back to a dataframe.

But I think you need flatten() instead, because it returns a copy and so if you modify the array returned by ravel, it does not modify the entries in the original array.