updated to be compatible with anaconda 4.0 and python 3.5

pandas/python2-deprecated/utilities/__init__.py

@@ -0,0 +1,3 @@
+from tom import TomTom # (TomTom, FileGenerator)
+from my_finance_tools import (side_by_side, csv_preview)
+from my_finance_plots import montecarloPlot

pandas/python2-deprecated/utilities/generators.py

@@ -0,0 +1,160 @@
+import pandas as pd
+from pandas_datareader.data import DataReader
+import numpy as np
+
+def p01_d2csv(tomtom):
+    d = {'a' : pd.Series(['one','one','two','three','two']),
+     'b' : pd.Series(['x','y','y','x','y']),
+     'c' : pd.Series(np.random.randn(5))}
+    d2 = pd.DataFrame(d)
+    d2.to_csv(tomtom.get_tmp_name('p01_d2.csv'))
+
+def p01_d3csv(tomtom):
+    comuni = pd.read_csv(tomtom.get_example_name('tabella_comuni_italiani.txt'),
+                         sep=';', header=0)
+    # d3 = pd.DataFrame(np.random.randn(1000, 100), columns=comuni['Comune'].ix[0:199])
+    comuni.to_csv(tomtom.get_tmp_name('p01_d3.csv'), index=False)
+
+def p01_d4csv(tomtom):
+    idx = [('Fra', 'one', 'x'),
+           ('Fra', 'two', 'y'),
+           ('Fra', 'two', 'z'),
+           ('Ger', 'one', 'x'),
+           ('Jap', 'one', 'x'),
+           ('Jap', 'two', 'x'),
+           ('USA', 'one', 'y'),
+           ('USA', 'one', 'z')]
+    index = pd.MultiIndex.from_tuples(idx, names=['Country', 'Number', 'Dir'])
+    d4 = pd.DataFrame(np.random.randn(8,3), index=index)
+    d4.to_csv(tomtom.get_tmp_name('p01_d4.csv'))
+
+def p01_prices(tomtom):
+    symbols = ['AAPL', 'JNJ', 'XOM', 'GOOG']
+    data = dict([(sym, DataReader(sym, 'yahoo')['Close']) for sym in symbols])
+    df = pd.DataFrame.from_dict(data)
+    df.ix[-7:-1].to_csv(tomtom.get_tmp_name('p01_prices.txt'))
+
+def p01_volumes(tomtom):
+    symbols = ['AAPL', 'JNJ', 'XOM']
+    data = dict([(sym, DataReader(sym, 'yahoo')['Volume']) for sym in symbols])
+    df = pd.DataFrame.from_dict(data)
+    df.ix[-7:-3].to_csv(tomtom.get_tmp_name('p01_volumes.txt'))
+
+def p03_DAX(tomtom):
+    DAX = DataReader('^GDAXI','yahoo',start = '01/01/2000')
+    DAX.to_csv(tomtom.get_tmp_name('p03_DAX.csv'))
+
+def p03_AAPL(tomtom):
+    DAX = DataReader('AAPL','yahoo',start = '01/01/2000')
+    DAX.to_csv(tomtom.get_tmp_name('p03_AAPL.csv'))
+
+def p06_d3csv(tomtom):
+    d2 = pd.DataFrame({'City' : ['New York', ' frisco', 'houston', ' taft', 'venice'],
+                'State' : [' NY ', 'CA', '  tx ', '   OK', '  IL'],
+                'Name' : ['Roy', 'Johnn', 'Jim', 'Paul', 'Ross'],
+                'Revenues' : ['1250', '840', '349', '1100', '900']})
+    d2.to_csv(tomtom.get_tmp_name('p06_d2.txt'))
+
+def p06_d2csv(tomtom):
+    d3 = pd.DataFrame({'Quantity' : ['1-one', '1-one', '2-two', '3-three'] * 6,
+                'Axis' : ['X', 'Y', 'Z'] * 8,
+                'Type' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
+                'N1' : np.random.randn(24),
+                'N2' : np.random.randn(24)})
+    d3.to_csv(tomtom.get_tmp_name('p06_d3.txt'))
+
+
+def p07_d1csv(tomtom):
+    d1 = pd.DataFrame({'State' : ['NE','KY','CO','CO','KY','KY','CO','NE','CO'],
+                    'City' : ['Page','Stone','Rye','Rye','Dema','Keavy','Rye',
+                              'Cairo', 'Dumont'],
+                    'Views' : [10, 9, 3, 7, 4, 2, 1, 8, 12],
+                    'Likes' : [4, 3, 0, 2, 1, 1, 0, 3, 7]})
+    d1.to_csv(tomtom.get_tmp_name('p07_d1.txt'))
+
+def p07_d2csv(tomtom):
+    import random; random.seed(0)
+    import string
+    N = 1000
+    def rands(n):
+        choices = string.ascii_uppercase
+        return ''.join([random.choice(choices) for _ in xrange(n)])
+
+    tickers = np.array([rands(5) for _ in xrange(N)])
+
+    # Create a DataFrame containing 3 columns representing
+    # hypothetical, but random portfolios for a subset of tickers:
+    d2 = pd.DataFrame({'Momentum' : np.random.randn(500) / 200 + 0.03,
+                   'Value' : np.random.randn(500) / 200 + 0.08,
+                   'ShortInterest' : np.random.randn(500) / 200 - 0.02},
+                   index=tickers.take(np.random.permutation(N)[:500]))
+
+    # Next, let's create a random industry classification for the tickers.
+    ind_names = np.array(['FINANCIAL', 'TECH'])
+    sampler = np.random.randint(0, len(ind_names), N)
+    industries = pd.Series(ind_names.take(sampler), index=tickers, name='industry')
+    d2['Industry'] = industries
+
+    d2.to_csv(tomtom.get_tmp_name('p07_d2.csv'))
+
+def p07_portfolioh5(tomtom):
+    import random; random.seed(0)
+    import string
+    N = 1000
+    def rands(n):
+        choices = string.ascii_uppercase
+        return ''.join([random.choice(choices) for _ in xrange(n)])
+
+    tickers = np.array([rands(5) for _ in xrange(N)])
+    fac1, fac2, fac3 = np.random.rand(3, 1000)
+    ticker_subset = tickers.take(np.random.permutation(N)[:1000])
+
+    # portfolio = weighted sum of factors plus noise
+    portfolio = pd.Series(0.7 * fac1 - 1.2 * fac2 + 0.3 * fac3 + np.random.rand(1000),
+                      index=ticker_subset)
+    factors = pd.DataFrame({'f1': fac1, 'f2': fac2, 'f3': fac3},
+                        index=ticker_subset)
+
+    h5file = pd.HDFStore(tomtom.get_tmp_name('p07_portfolio.h5'))
+    h5file['factors'] = factors
+    h5file['portfolio'] = portfolio
+    h5file.close()
+
+def baby_names(tomtom):
+    import zipfile
+    path = tomtom.get_example_name('babynames.zip')
+    opath = tomtom.get_tmp_name("")
+    z = zipfile.ZipFile(path, "r")
+    z.extractall(path=opath)
+
+generators = {
+    'baby_names/': baby_names,
+    'p07_portfolio.h5': p07_portfolioh5,
+    'p07_d2.csv': p07_d2csv,
+    'p07_d1.txt': p07_d1csv,
+    'p06_d3.txt': p06_d3csv,
+    'p06_d2.txt': p06_d2csv,
+    'p03_DAX.csv': p03_DAX,
+    'p03_AAPL.csv': p03_AAPL,
+    'p01_prices.txt': p01_prices,
+    'p01_d2.csv': p01_d2csv,
+    'p01_d3.csv': p01_d3csv,
+    'p01_d4.csv': p01_d4csv,
+    'p01_volumes.txt': p01_volumes,
+    }
+
+def generate_all():
+    from tom import TomTom
+    import os
+
+    tomtom = TomTom()
+    for filename, gen in generators.iteritems():
+        path = tomtom.get_tmp_name(filename)
+        if not os.path.exists(path):
+            print "Generating {}...".format(filename)
+            gen(tomtom)
+        else:
+            print "Skipped {} (already existing)".format(filename)
+
+if __name__ == '__main__':
+    generate_all()            

pandas/python2-deprecated/utilities/my_finance_tools.py

@@ -0,0 +1,44 @@
+from datetime import datetime
+import matplotlib.finance as fin
+import pandas as pd
+from pandas import Index, DataFrame
+from pandas.core.datetools import BMonthEnd
+
+# MY FINANCE TOOLS -------------------------------------------------------------
+''' Created by: Addfor s.r.l.
+    This module provides few example finance and utility functions
+    '''
+
+def csv_preview(filename, lines_to_print=5):
+    '''
+    TODO - Add a control to define how many columns to print:
+              start_column = 0
+              end_column = 79
+    '''
+    with open(filename) as fid:
+        for _ in range(lines_to_print):
+            line = fid.readline()
+            print line,
+
+def side_by_side(*objs, **kwds):
+    space = kwds.get('space', 4)
+    reprs = [repr(obj).split('\n') for obj in objs]
+    print '-'*40
+    print pd.core.common.adjoin(space, *reprs)
+    print '-'*40
+
+def getQuotes(symbol, start, end):
+    '''getQuotes documentation'''
+    quotes = fin.quotes_historical_yahoo(symbol, start, end)
+    dates, opn, close, high, low, volume = zip(*quotes)
+    data = {'open': opn, 'close': close, 'high' : high,
+            'low' : low, 'volume': volume}
+
+    dates = Index([datetime.fromordinal(int(d)) for d in dates])
+    return DataFrame(data, index=dates)
+
+
+def toMonthly(frame, how):
+    '''toMonthly documentation'''
+    offset = BMonthEnd()
+    return frame.groupby(offset.rollforward).aggregate(how)

pandas/utilities/__init__.py

@@ -1,3 +1,3 @@
-from tom import TomTom # (TomTom, FileGenerator)
-from my_finance_tools import (side_by_side, csv_preview)
-from my_finance_plots import montecarloPlot
+from .tom import TomTom # (TomTom, FileGenerator)
+from .my_finance_tools import (side_by_side, csv_preview)
+from .my_finance_plots import montecarloPlot

pandas/utilities/generators.py

@@ -1,6 +1,7 @@
 import pandas as pd
 from pandas_datareader.data import DataReader
 import numpy as np
+from .tom import TomTom
 
 def p01_d2csv(tomtom):
     d = {'a' : pd.Series(['one','one','two','three','two']),
@@ -78,9 +79,9 @@ def p07_d2csv(tomtom):
     N = 1000
     def rands(n):
         choices = string.ascii_uppercase
-        return ''.join([random.choice(choices) for _ in xrange(n)])
+        return ''.join([random.choice(choices) for _ in range(n)])
 
-    tickers = np.array([rands(5) for _ in xrange(N)])
+    tickers = np.array([rands(5) for _ in range(N)])
 
     # Create a DataFrame containing 3 columns representing
     # hypothetical, but random portfolios for a subset of tickers:
@@ -103,9 +104,9 @@ def p07_portfolioh5(tomtom):
     N = 1000
     def rands(n):
         choices = string.ascii_uppercase
-        return ''.join([random.choice(choices) for _ in xrange(n)])
+        return ''.join([random.choice(choices) for _ in range(n)])
 
-    tickers = np.array([rands(5) for _ in xrange(N)])
+    tickers = np.array([rands(5) for _ in range(N)])
     fac1, fac2, fac3 = np.random.rand(3, 1000)
     ticker_subset = tickers.take(np.random.permutation(N)[:1000])
 
@@ -144,17 +145,16 @@ def baby_names(tomtom):
     }
 
 def generate_all():
-    from tom import TomTom
     import os
 
     tomtom = TomTom()
-    for filename, gen in generators.iteritems():
+    for filename, gen in generators.items():
         path = tomtom.get_tmp_name(filename)
         if not os.path.exists(path):
-            print "Generating {}...".format(filename)
+            print("Generating {}...".format(filename))
             gen(tomtom)
         else:
-            print "Skipped {} (already existing)".format(filename)
+            print("Skipped {} (already existing)".format(filename))
 
 if __name__ == '__main__':
     generate_all()            

pandas/utilities/my_finance_tools.py

@@ -18,14 +18,14 @@ def csv_preview(filename, lines_to_print=5):
     with open(filename) as fid:
         for _ in range(lines_to_print):
             line = fid.readline()
-            print line,
+            print(line,)
 
 def side_by_side(*objs, **kwds):
     space = kwds.get('space', 4)
     reprs = [repr(obj).split('\n') for obj in objs]
-    print '-'*40
-    print pd.core.common.adjoin(space, *reprs)
-    print '-'*40
+    print('-'*40)
+    print(pd.core.common.adjoin(space, *reprs))
+    print('-'*40)
 
 def getQuotes(symbol, start, end):
     '''getQuotes documentation'''
@@ -41,4 +41,4 @@ def getQuotes(symbol, start, end):
 def toMonthly(frame, how):
     '''toMonthly documentation'''
     offset = BMonthEnd()
-    return frame.groupby(offset.rollforward).aggregate(how)
+    return frame.groupby(offset.rollforward).aggregate(how)

pandas/utilities/tom.py

@@ -3,7 +3,7 @@
 TEMP_DIR="temp"
 EXAMPLE_DIR="example_data"
 
-from generators import generators
+#from generators import generators
 
 class TomTom(object):
     """