pandas tutorial

Author: sb2nov

Examples/Basic/pandas-tutorial.py

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+'''
+(c) 2011, 2012 Georgia Tech Research Corporation
+This source code is released under the New BSD license.  Please see
+http://wiki.quantsoftware.org/index.php?title=QSTK_License
+for license details.
+
+Created on October, 4, 2013
+
+@author: Sourabh Bajaj
+@contact: sourabhbajaj@gatech.edu
+@summary: Example tutorial code.
+'''
+
+import pandas as pd
+import datetime as dt
+import numpy as np
+
+## Tutorial on using Pandas in QSTK
+ldt_timestamps = []
+for i in range(1, 6):
+    ldt_timestamps.append(dt.datetime(2011, 1, i, 16))
+
+print "The index we created has the following dates : "
+print ldt_timestamps
+print
+
+## TimeSeries
+ts_single_value = pd.TimeSeries(0.0, index=ldt_timestamps)
+print "A timeseries initialized to one single value : "
+
+na_vals = np.arange(len(ldt_timestamps))
+print "Dummy initialized array : "
+print na_vals
+print
+
+ts_array = pd.TimeSeries(na_vals, index=ldt_timestamps)
+print "A timeseries initialized using a numpy array : "
+print ts_array
+print 
+
+print "Reading the timeseries for a particular date"
+print "Date :  ", ldt_timestamps[1]
+print "Value : ", ts_array[ldt_timestamps[1]]
+print
+
+print "Initializing a list of symbols : "
+ls_symbols = ['AAPL', 'GOOG', 'MSFT', 'IBM']
+print ls_symbols
+print
+
+print "Initializing a dataframe with one value : "
+df_single = pd.DataFrame(index=ldt_timestamps, columns=ls_symbols)
+df_single = df_single.fillna(0.0)
+print df_single
+print
+
+print "Initializing a dataframe with a numpy array : "
+na_vals_2 = np.random.randn(len(ldt_timestamps), len(ls_symbols))
+df_vals = pd.DataFrame(na_vals_2, index=ldt_timestamps, columns=ls_symbols)
+print df_vals
+print 
+
+print "Access the timeseries of a particular symbol : "
+print df_vals[ls_symbols[1]]
+print
+
+print "Access the timeseries of a particular date : "
+print df_vals.ix[ldt_timestamps[1]]
+print
+
+print "Access the value for a specific symbol on a specific date: "
+print df_vals[ls_symbols[1]].ix[ldt_timestamps[1]]
+print
+
+print "Reindexing the dataframe"
+ldt_new_dates = [dt.datetime(2011, 1, 3, 16), 
+                 dt.datetime(2011, 1, 5, 16),
+                 dt.datetime(2011, 1, 7, 16)]
+ls_new_symbols = ['AAPL', 'IBM', 'XOM']
+df_new = df_vals.reindex(index=ldt_new_dates, columns=ls_new_symbols)
+print df_new
+print "Observe that reindex carried over whatever values it could find and set the rest to NAN"
+print
+
+print "For pandas rolling statistics please refer : http://pandas.pydata.org/pandas-docs/dev/computation.html#moving-rolling-statistics-moments"
+