Unhashable type error with sklearn and importing a CSV

I'm trying to execute the below code and I don't understand what I'm doing wrong. The purpose of the code is to use Python's & sklearn's train_test_split function to partition the data into training and testing chunks.

The data (downloadable here) is cost of rent data for various houses/condos, along with each house/condo's properties. Ultimately I'm trying to use predictive modeling to predict rent prices (so rent prices are the target). Here's the code:

import pandas as pd
rentdata = pd.read_csv('6000_clean.csv')

import sklearn as sk
import numpy as np
import matplotlib.pyplot as plt

from sklearn.cross_validation import train_test_split

#trying to make a all rows of the first column and b all rows of columns 2-46, i.e., a will be only target data (rent prices) and b will be the data.

a, b = rentdata[ : ,0], rentdata[ : ,1:46]

What results is the following error:

TypeError                                 Traceback (most recent call last)
<ipython-input-24-789fb8e8c2f6> in <module>()
      8 from sklearn.cross_validation import train_test_split
---> 10 a, b = rentdata[ : ,0], rentdata[ : ,1:46]

C:\Users\Nick\Anaconda\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
   2001             # get column
   2002             if self.columns.is_unique:
-> 2003                 return self._get_item_cache(key)
   2005             # duplicate columns

C:\Users\Nick\Anaconda\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
    665             return cache[item]
    666         except Exception:
--> 667             values = self._data.get(item)
    668             res = self._box_item_values(item, values)
    669             cache[item] = res

C:\Users\Nick\Anaconda\lib\site-packages\pandas\core\internals.pyc in get(self, item)
   1653     def get(self, item):
   1654         if self.items.is_unique:
-> 1655             _, block = self._find_block(item)
   1656             return block.get(item)
   1657         else:

C:\Users\Nick\Anaconda\lib\site-packages\pandas\core\internals.pyc in _find_block(self, item)
   1934     def _find_block(self, item):
-> 1935         self._check_have(item)
   1936         for i, block in enumerate(self.blocks):
   1937             if item in block:

C:\Users\Nick\Anaconda\lib\site-packages\pandas\core\internals.pyc in _check_have(self, item)
   1940     def _check_have(self, item):
-> 1941         if item not in self.items:
   1942             raise KeyError('no item named %s' % com.pprint_thing(item))

C:\Users\Nick\Anaconda\lib\site-packages\pandas\core\index.pyc in __contains__(self, key)
    318     def __contains__(self, key):
--> 319         hash(key)
    320         # work around some kind of odd cython bug
    321         try:

TypeError: unhashable type

You can download the CSV to get a look at the data here: http://wikisend.com/download/776790/6000_clean.csv


I downloaded your data and modified your problem line to this:

a, b = rentdata.iloc[0], rentdata.iloc[1:46]

iloc selects row by position, see the docs: http://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-position

This now selects the first row and rows 2-46 (remember that slicing is open-closed, includes begin of range but not the end of the range)

Note you can always select the first row using head:

a, b = rentdata.head(0), rentdata.iloc[1:46]

would also work

In [5]:



Monthly $ rent                                                    1150
Location                                                       alameda
# of bedrooms                                                        1
# of bathrooms                                                       1
# of square feet                                                   NaN
Latitude                                                      37.77054
Longitude                                                    -122.2509
Street address                                  1500-1598 Lincoln Lane
# more rows so trimmed for brevity here

In [9]: b

# too large to paste here
45 rows × 46 columns

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