title: column is not a column of the pandas dataframe
body: <p>I am trying to determine the evaluation and the final conformal predictions for my model with my data. But it gives me following error:</p>
  
  <h1>Error</h1>
  
  <pre><code>Traceback (most recent call last):
    File "/home/maria/CP/scripts/Conformity_PredictionsV4.py", line 89, in &lt;module&gt;
      icp.fit(X_train, y_train)
    File "/home/maria/.local/lib/python3.8/site-packages/sklearn/utils/__init__.py", line 454, in _get_column_indices
      raise ValueError(
  ValueError: A given column is not a column of the dataframe
  </code></pre>
  
  <h1>Code Sample</h1>
  
  <pre><code>from sklearn.tree import DecisionTreeRegressor
  from nonconformist.cp import IcpRegressor
  from nonconformist.base import RegressorAdapter
  from nonconformist.nc import RegressorNc, AbsErrorErrFunc, RegressorNormalizer, NcFactory
  from sklearn.model_selection import train_test_split
  import numpy as np
  import pandas as pd
  
  # -----------------------------------------------------------------------------
  # Load Environment and Models
  # -----------------------------------------------------------------------------
  
  # -----------------------------------------------------------------------------
  # Setup training, calibration and test data
  # -----------------------------------------------------------------------------
  df = pd.read_csv ("prepared_data.csv")
  
  
  # Initial split into train/test data
  train = df.loc[df['split']== 'train']
  valid = df.loc[df['split']== 'valid']
  
  # Proper Validation Set (Split the Validation set into features and target)
  X_valid = valid.drop(['expression'], axis = 1)
  y_valid = valid.drop(columns = ['new_host', 'split', 'sequence'])
  
  # Create Training Set (Split the Training set into features and target)
  X_train = valid.drop(['expression'], axis = 1)
  y_train = valid.drop(columns = ['new_host', 'split', 'sequence'])
  
  # Split Training set into further training set and calibration set
  X_train, X_cal, y_train, y_cal = train_test_split(X_train, y_train, test_size =0.2)
  
  # -----------------------------------------------------------------------------
  # Train and calibrate underlying model
  # -----------------------------------------------------------------------------
  underlying_model = RegressorAdapter(DecisionTreeRegressor(min_samples_leaf=5))
  print("Underlying model loaded")
  model = RegressorAdapter(underlying_model)
  nc = RegressorNc(model, AbsErrorErrFunc())
  
  print("Nonconformity Function Applied")
  icp = IcpRegressor(nc)  # Create an inductive conformal Regressor
  print("ICP Regressor Created")
  
  #Dataset Review
  print('{} instances, {} features, {} classes'.format(y_train.size,
                                                     X_train.shape[1],
                                                     np.unique(y_train).size))
  
  icp.fit(X_train, y_train)
  </code></pre>
  
  <h1>Example Dataframe</h1>
  
  <pre><code>new_host  split     sequence    expression
  FALSE     train     AQVPYGVS    0.039267878
  FALSE     train     ASVPYGVSI   0.039267878
  FALSE     train     STNLYGSGR   0.261456561
  FALSE     valid     NLYGSGLVR   0.265188519
  FALSE     valid     SLGPSNLYG   0.419680588
  FALSE     valid     ATSLGTTNG   0.145710993
  </code></pre>
  
  <p>I've tried splitting the dataset in various ways but I am continuing to have trouble with this. In this case I want to split the data into train and test sets according to an observation's Data Split value. After which, I will split the train set into train and calibration in a second step. Where myfeatures, X_train and my target, y_train</p>