We use Boolean indexing to filter or select parts of the data. The operators are as follows:

These operators must be grouped using parentheses when used together. Using the earlier DataFrame from the previous section, here we display the trading dates for which NASDAQ closed above 4,300:

  In [311]: sharesIndexDataDF.ix[(sharesIndexDataDF['PriceType']=='close') & 
                         (sharesIndexDataDF['Nasdaq']>4300) ]
  Out[311]:        PriceType  Nasdaq   S&P 500   Russell 2000
      TradingDate
      2014/02/27   close  4318.93   1854.29   1187.94
      2014/02/28   close  4308.12   1859.45   1183.03
      2 rows × 4 columns
  

You can also create Boolean conditions in which you use arrays to filter out parts of the data, as shown in the following code:

highSelection=sharesIndexDataDF['PriceType']=='high'  NasdaqHigh=sharesIndexDataDF['Nasdaq']<4300  sharesIndexDataDF.ix[highSelection & NasdaqHigh]
    Out[316]: TradingDate  PriceType Nasdaq  S&P 500  Russell 2000
        2014/02/21        high 4284.85  1846.13  1168.43

Thus, the preceding code snippet displays the only date in the dataset for which the NASDAQ Composite index stayed below the 4,300 level for the entire trading session.