Pandas Cookbook

How to do it...

1. Read in the Schlumberger stock data, put the Date column into the index, and convert it to a DatetimeIndex:

>>> slb = pd.read_csv('data/slb_stock.csv', index_col='Date',                      parse_dates=['Date'])>>> slb.head()

2. Select the closing price as a Series and use the describe method to return summary statistics as a Series:

>>> slb_close = slb['Close']>>> slb_summary = slb_close.describe(percentiles=[.1, .9])>>> slb_summarycount    1895.000000
mean       79.121905
std        11.767802
min        51.750000
10%        64.892000
50%        78.000000
90%        93.248000
max       117.950000
Name: Close, dtype: float64

3. Using boolean selection, select all closing prices in the upper or ...