Significant performance degradation in 0.9.1 for SparseDataFrame methods like to_dense() and save() and for arithmetic operations
This is what I have in version 0.9.0:

```
import pandas as pd
pd.__version__
```

> '0.9.0'

```
barra_industry_exposures
```

> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 253738 entries, (20061229, '00036110') to (20120928, 'Y8564W10')
> Data columns:
> MINING_METALS                  253738  non-null values
> GOLD                           253738  non-null values
> FORESTRY_PAPER                 253738  non-null values
> CHEMICAL                       253738  non-null values
> ENERGY_RESERVES                253738  non-null values
> OIL_REFINING                   253738  non-null values
> OIL_SERVICES                   253738  non-null values
> FOOD_BEVERAGES                 253738  non-null values
> ALCOHOL                        253738  non-null values
> TOBACCO                        253738  non-null values
> HOME_PRODUCTS                  253738  non-null values
> GROCERY_STORES                 253738  non-null values
> CONSUMER_DURABLES              253738  non-null values
> MOTOR_VEHICLES                 253738  non-null values
> APPAREL_TEXTILES               253738  non-null values
> CLOTHING_STORES                253738  non-null values
> SPECIALTY_RETAIL               253738  non-null values
> DEPARTMENT_STORES              253738  non-null values
> CONSTRUCTION                   253738  non-null values
> PUBLISHING                     253738  non-null values
> MEDIA                          253738  non-null values
> HOTELS                         253738  non-null values
> RESTAURANTS                    253738  non-null values
> ENTERTAINMENT                  253738  non-null values
> LEISURE                        253738  non-null values
> ENVIRONMENTAL_SERVICES         253738  non-null values
> HEAVY_ELECTRICAL_EQUIPMENT     253738  non-null values
> HEAVY_MACHINERY                253738  non-null values
> INDUSTRIAL_PARTS               253738  non-null values
> ELECTRICAL_UTILITY             253738  non-null values
> GAS_WATER_UTILITY              253738  non-null values
> RAILROADS                      253738  non-null values
> AIRLINES                       253738  non-null values
> FREIGHT                        253738  non-null values
> MEDICAL_SERVICES               253738  non-null values
> MEDICAL_PRODUCTS               253738  non-null values
> DRUGS                          253738  non-null values
> ELECTRONIC_EQUIPMENT           253738  non-null values
> SEMICONDUCTORS                 253738  non-null values
> COMPUTER_HARDWARE              253738  non-null values
> COMPUTER_SOFTWARE              253738  non-null values
> DEFENCE_AEROSPACE              253738  non-null values
> TELEPHONE                      253738  non-null values
> WIRELESS                       253738  non-null values
> INFORMATION_SERVICES           253738  non-null values
> INDUSTRIAL_SERVICES            253738  non-null values
> LIFE_HEALTH_INSURANCE          253738  non-null values
> PROPERTY_CASUALTY_INSURANCE    253738  non-null values
> BANKS                          253738  non-null values
> THRIFTS                        253738  non-null values
> ASSET_MANAGEMENT               253738  non-null values
> FINANCIAL_SERVICES             253738  non-null values
> INTERNET                       253738  non-null values
> REITS                          253738  non-null values
> BIOTECH                        253738  non-null values
> dtypes: int64(55)

```
sparse = barra_industry_exposures.to_sparse(fill_value=0)
sparse
```

> <class 'pandas.sparse.frame.SparseDataFrame'>
> MultiIndex: 253738 entries, (20061229, '00036110') to (20120928, 'Y8564W10')
> Columns: 55 entries, AIRLINES to WIRELESS
> dtypes: float64(55)

```
%timeit sparse / 100.
```

> 100 loops, best of 3: 6.64 ms per loop

```
%timeit sparse.to_dense()
```

> 10 loops, best of 3: 127 ms per loop

```
%timeit sparse.save('test.pkl')
```

> 1 loops, best of 3: 16.9 ms per loop

Now this is what I get in 0.9.1:

```
import pandas as pd
pd.__version__
```

> '0.9.1'

```
%timeit sparse / 100.
```

> 1 loops, best of 3: 92.2 s per loop

```
%timeit sparse.to_dense()
```

> 1 loops, best of 3: 99.8 s per loop

```
%timeit sparse.save('test.pkl')
```

> 1 loops, best of 3: 100 s per loop

So, in the new version SparseDataFrame methods that used to run in less than 7-130 ms now run in more than 90 s. Ouch! What happened?
