import bokeh.plotting as bk
bk.output_notebook()

p = bk.figure(match_aspect=True)
p.circle(0, 0, radius=1, radius_dimension='y', fill_color=None)
bk.show(p)

p = bk.figure(match_aspect=True)
p.ellipse(0, 0, width=2, height=2, fill_color=None)
bk.show(p)

Pandas¶

import pandas as pd

Use pandas read_csv function to read csv files:

NYSE = pd.read_csv('http://www.math.buffalo.edu/~badzioch/MTH337/_downloads/NYSE_2016_03_25.csv', # name or url of the csv file
                  delimiter = ',', #character separating columns
                  skiprows=3, #number of rows to skip
                  header = 0, #number of row with column headers
                  encoding = 'latin1', #file encoding
                  low_memory = False #read the whole file in a single piece
                  )

a = NYSE.values
a

array([['3D Systems', 'DDD', 14.14, ..., 32.88, 6.0, 69.85],
       ['3M', 'MMM', 163.68, ..., 167.7, 134.0, 9.17],
       ['58.com ADR', 'WUBA', 56.25, ..., 83.71, 37.72, -14.37],
       ..., 
       ['Zimmer Biomet Holdings', 'ZBH', 103.58, ..., 119.1, 88.27, 1.12],
       ["Zoe's Kitchen", 'ZOES', 37.65, ..., 46.61, 23.17, 37.53],
       ['Zoetis', 'ZTS', 42.27, ..., 55.38, 37.73, -11.35]], dtype=object)

NYSE

capitals = pd.read_csv('http://www.math.buffalo.edu/~badzioch/MTH337/_downloads/capital_distances.txt', # name or url of the csv file
                  delimiter = ' ', #character separating columns
                  skiprows=11, #number of rows to skip
                  header = None, #no  column headers
                  names = ['1st country code', '2nd country code', 'distance', '1st name code', '2nd name code'], #manually specify column headers
                  encoding = 'latin1', #file encoding
                  low_memory = False #read the whole file in a single piece
                  )

capitals

Accessing columns by name:

NYSE['Symbol']

0        DDD
1        MMM
2       WUBA
3       WBAI
4       ATEN
5        AAC
6        AIR
7        AAN
8        ABB
9        ABT
10      ABBV
11       ANF
12       ABM
13       AKR
14       ACN
15      ACCO
16       ACW
17       ATV
18       ATU
19       AYI
20      AGRO
21      ADPT
22       ADT
23       AAP
24       WMS
25       ASX
26       AAV
27       ATE
28       ACM
29       ANW
        ... 
2112     GWW
2113     WYN
2114    XTLY
2115     XEL
2116     XHR
2117     XRM
2118     XRX
2119     XIN
2120      XL
2121    XOXO
2122     XPO
2123     XUE
2124     XYL
2125    YDKN
2126     AUY
2127     YZC
2128    YELP
2129     YGE
2130     YRD
2131    YOKU
2132     YPF
2133     YUM
2134    YUME
2135     ZFC
2136    ZAYO
2137     ZEN
2138    ZPIN
2139     ZBH
2140    ZOES
2141     ZTS
Name: Symbol, Length: 2142, dtype: object

NYSE[['Name', 'Symbol']]

Accessing rows by slicing:

NYSE[5:10]

Selecting rows by a logical condition:

NYSE[NYSE['Name'] == '3M']

low_vol  = NYSE[NYSE['Volume'] < 2000].copy()
low_vol

Use loc to select rows by indexes and columns by their names:

low_vol.loc[425, 'Symbol']

'XNY'

low_vol.loc[[425, 491], ['Symbol', 'Open']]

Use iloc to select rows and columns by their numbers:

low_vol.iloc[0, 0]

"Alexander's Inc."

low_vol.iloc[:10:2, 0:3]

Sorting rows by column values:

low_vol.sort_values(by='Volume', ascending=False)

low_vol['Extra'] = 0

low_vol

low_vol['Extra'] = low_vol['High'] - low_vol['Low']

low_vol

Numpy functions work on rows and columns:

import numpy as np

low_vol['Extra'] = np.cos(low_vol['Open'])

low_vol

Using custom functions:

def f(x):
    if x < 200:
        return 'LOWEST'
    elif x < 500:
        return 'LOWER'
    else:
        return 'LOW'
    
low_vol['Extra'] = low_vol['Volume'].apply(f)

low_vol

Summarizing columns:

low_vol.mean()

Open          6813.310000
High          6838.652258
Low           6791.326129
Close         6823.932903
Net Chg        -28.886897
% Chg           -1.316667
Volume         749.838710
52 Wk High    7234.611613
52 Wk Low     6055.974839
YTD % Chg       -0.265806
dtype: float64

low_vol.median()

Open           10.28
High           10.28
Low            10.28
Close          10.28
Net Chg        -0.03
% Chg          -0.30
Volume        642.00
52 Wk High     16.12
52 Wk Low       9.05
YTD % Chg       0.55
dtype: float64

low_vol.max()

Name          Xueda Education Group ADR
Symbol                               ZX
Open                             210200
High                             210980
Low                              209525
Close                            210530
Net Chg                            3.73
% Chg                             11.05
Volume                             1830
52 Wk High                       223011
52 Wk Low                        186900
YTD % Chg                         57.85
Extra                            LOWEST
dtype: object

Plotting with pandas:

vols = low_vol['Extra'].value_counts()
vols

LOW       17
LOWER      7
LOWEST     7
Name: Extra, dtype: int64

import matplotlib.pyplot as plt
plt.figure(figsize=(8,8))
plt.title('Trading volumes')
vols.plot(kind='bar', color='red')
plt.show()

Converting a DataFrame into a numpy array:

a = low_vol.values

a

array([["Alexander's Inc.", 'ALX', 383.4, 383.48, 380.42, 380.42, -0.48,
        -0.13, 1408, 468.25, 350.03, -0.96, 'LOW'],
       ['Banco Bradesco Ord ADR', 'BBDO', 7.89, 8.24, 7.89, 8.24, 0.02,
        0.24, 1598, 11.46, 4.15, 57.85, 'LOW'],
       ['Berkshire Hathaway Cl A', 'BRKA', 210200.01, 210980.0, 209525.0,
        210530.0, -840.0, -0.4, 179, 223011.49, 186900.0, 6.44, 'LOWEST'],
       ['Brookfield Canada Office Properties', 'BOXC', 21.91, 22.03, 21.75,
        21.91, 0.28, 1.29, 1503, 24.0, 16.22, 15.26, 'LOW'],
       ['BRT Realty Trust', 'BRT', 6.76, 6.76, 6.7, 6.75, nan, nan, 1830,
        7.3, 5.41, 6.47, 'LOW'],
       ['Capital One Financia Wt', 'COF%', 28.4, 28.74, 28.4, 28.74, -0.62,
        -2.11, 1250, 50.01, 19.23, -8.33, 'LOW'],
       ['China Xiniya Fashion ADR', 'XNY', 1.2, 1.2, 1.2, 1.2, nan, 0.08,
        137, 2.44, 1.03, -32.53, 'LOWEST'],
       ['China Zenix Auto International ADR', 'ZX', 1.08, 1.08, 1.08, 1.08,
        -0.02, -1.82, 158, 1.45, 0.43, 36.71, 'LOWEST'],
       ['Companhia Energetica de Minas Gerais-Cemig ADR', 'CIGC', 2.09,
        2.09, 2.04, 2.06, -0.05, -2.37, 812, 5.5, 1.08, 18.39, 'LOW'],
       ['Constellation Brands Cl B', 'STZB', 149.49, 149.49, 149.42,
        149.42, 0.18, 0.12, 205, 156.0, 115.0, 3.64, 'LOWER'],
       ['Eastman Kodak Co. WT 125%', 'KODK%', 2.54, 2.54, 2.54, 2.54,
        -0.22, -7.97, 400, 6.45, 0.98, -8.3, 'LOWER'],
       ['Eastman Kodak WT', 'KODK%A', 2.0, 2.0, 1.85, 1.86, -0.34, -15.45,
        700, 5.75, 0.27, 1.64, 'LOW'],
       ['Embotelladora Andina S.A. (Series A) ADR', 'AKOA', 16.0, 16.37,
        16.0, 16.37, 0.41, 2.57, 200, 19.2, 12.53, 0.55, 'LOWER'],
       ['Federal Agricultural Mortgage Cl A', 'AGMA', 35.07, 37.66, 35.07,
        37.5, 3.73, 11.05, 550, 37.66, 24.75, 38.68, 'LOW'],
       ['First Marblehead', 'FMD', 3.51, 3.51, 3.51, 3.51, -0.05, -1.4,
        1112, 7.0, 2.81, -16.63, 'LOW'],
       ['GeoPark', 'GPRK', 2.9, 2.9, 2.89, 2.89, 0.11, 3.83, 642, 5.73,
        2.45, 7.04, 'LOW'],
       ['Gray Television Cl A', 'GTNA', 10.28, 10.28, 10.28, 10.28, -0.31,
        -2.93, 155, 16.12, 9.05, -24.8, 'LOWEST'],
       ['Haverty Furniture Cl A', 'HVTA', 20.42, 20.42, 20.42, 20.42, 0.25,
        1.24, 105, 24.4, 17.52, -5.02, 'LOWEST'],
       ['HealthSouth WT', 'HLS%', 1.48, 1.48, 1.48, 1.48, -0.02, -1.33,
        101, 2.3, 0.74, -31.16, 'LOWEST'],
       ['HudBay Minerals WT', 'HBM%', 0.24, 0.25, 0.24, 0.24, -0.03,
        -10.88, 341, 1.73, 0.08, 0.29, 'LOWER'],
       ['Industrias Bachoco ADR', 'IBA', 50.84, 50.97, 50.53, 50.97, -0.1,
        -0.2, 830, 64.3, 40.43, 3.53, 'LOW'],
       ['John Wiley&Sons Cl B', 'JWB', 47.73, 47.73, 47.73, 47.73, 0.85,
        1.82, 253, 62.01, 40.18, 5.12, 'LOWER'],
       ['Lincoln National WT', 'LNC%', 31.77, 31.77, 30.4, 30.4, -1.73,
        -5.37, 1800, 53.99, 24.59, -28.47, 'LOW'],
       ['McCormick Com. Vtg.', 'MKCV', 94.96, 96.14, 94.96, 96.14, 2.47,
        2.64, 1051, 96.14, 75.2, 11.31, 'LOW'],
       ['M&T Bank WT', 'MTB%', 38.95, 38.95, 38.0, 38.0, -1.58, -3.99, 300,
        59.0, 30.26, -22.0, 'LOWER'],
       ['Steel Partners Holdings', 'SPLP', 15.09, 15.13, 14.75, 14.8, -0.1,
        -0.67, 1753, 19.37, 12.86, -9.65, 'LOW'],
       ['SunTrust Banks Cl A WT', 'STI/%A', 7.15, 7.15, 7.15, 7.15, -0.75,
        -9.49, 100, 12.76, 5.76, -35.06, 'LOWEST'],
       ['TCF Financial WT', 'TCB%', 1.72, 1.72, 1.72, 1.72, 0.02, 1.39,
        200, 3.1, 1.35, -18.1, 'LOWER'],
       ['Urstadt Biddle Properties', 'UBP', 17.54, 17.95, 17.54, 17.95,
        0.45, 2.57, 1250, 19.95, 15.89, 0.84, 'LOW'],
       ['Westmoreland Resource Partners', 'WMLP', 4.81, 4.81, 4.79, 4.79,
        -0.08, -1.64, 1260, 12.65, 2.38, 19.75, 'LOW'],
       ['Xueda Education Group ADR', 'XUE', 5.38, 5.38, 5.36, 5.36, -0.01,
        -0.19, 1062, 5.45, 2.56, -0.74, 'LOW']], dtype=object)

a[1, :]

array(['Banco Bradesco Ord ADR', 'BBDO', 7.89, 8.24, 7.89, 8.24, 0.02,
       0.24, 1598, 11.46, 4.15, 57.85, 'LOW'], dtype=object)

b = a[:, 3]
b

array([383.48, 8.24, 210980.0, 22.03, 6.76, 28.74, 1.2, 1.08, 2.09, 149.49,
       2.54, 2.0, 16.37, 37.66, 3.51, 2.9, 10.28, 20.42, 1.48, 0.25, 50.97,
       47.73, 31.77, 96.14, 38.95, 15.13, 7.15, 1.72, 17.95, 4.81, 5.38], dtype=object)

vols

LOW       17
LOWER      7
LOWEST     7
Name: Extra, dtype: int64

a = vols.values

a

array([17,  7,  7])

plt.bar(range(3), a, tick_label=vols.index.values)
plt.show()

	Name	Symbol	Open	High	Low	Close	Net Chg	% Chg	Volume	52 Wk High	52 Wk Low	YTD % Chg
0	3D Systems	DDD	14.14	14.81	13.60	14.76	0.32	2.22	3240444	32.88	6.00	69.85
1	3M	MMM	163.68	164.54	163.05	164.46	0.19	0.12	1730167	167.70	134.00	9.17
2	58.com ADR	WUBA	56.25	56.76	55.28	56.48	-0.04	-0.07	518280	83.71	37.72	-14.37
3	500.com ADR	WBAI	16.46	17.18	16.04	16.88	0.26	1.56	378620	30.67	9.89	-16.02
4	A10 Networks	ATEN	5.85	5.91	5.77	5.87	-0.03	-0.51	126272	8.30	4.12	-10.52
5	AAC Holdings	AAC	18.18	18.48	17.99	18.38	0.17	0.93	123794	46.60	14.36	-3.57
6	AAR Corp.	AIR	22.82	22.83	21.45	22.80	-0.23	-1.00	463662	34.24	18.36	-13.28
7	Aaron's Inc.	AAN	23.76	23.88	23.37	23.87	-0.14	-0.58	240738	40.80	20.24	6.61
8	ABB ADR	ABB	18.80	18.94	18.78	18.93	-0.16	-0.84	1308716	23.66	15.74	6.77
9	Abbott Laboratories	ABT	40.64	40.90	40.36	40.75	-0.03	-0.07	4038030	51.74	36.00	-9.26
10	AbbVie	ABBV	56.14	56.25	55.67	56.12	-0.24	-0.43	4708673	71.60	45.45	-5.27
11	Abercrombie&Fitch	ANF	30.74	31.54	30.58	31.41	0.58	1.88	1496086	32.83	15.42	16.33
12	ABM Industries	ABM	31.42	31.61	31.20	31.48	-0.04	-0.13	148112	34.00	26.50	10.57
13	Acadia Realty Trust	AKR	33.58	33.80	33.50	33.70	NaN	NaN	219740	35.36	28.34	1.66
14	Accenture Cl A	ACN	108.79	114.44	108.68	114.30	6.64	6.17	6279911	114.44	88.43	9.38
15	ACCO Brands	ACCO	8.47	8.63	8.42	8.56	0.06	0.71	326854	8.90	5.47	20.06
16	Accuride	ACW	1.61	1.67	1.53	1.64	0.03	1.86	422355	5.00	0.74	-1.20
17	Acorn International ADR	ATV	4.68	4.68	4.47	4.56	-0.10	-2.15	20026	20.85	1.77	-16.18
18	Actuant Cl A	ATU	23.92	24.34	23.67	24.34	0.26	1.08	325150	25.50	17.02	1.59
19	Acuity Brands	AYI	217.43	220.40	212.93	214.91	-2.89	-1.33	448249	241.90	160.02	-8.08
20	Adecoagro	AGRO	11.55	11.70	11.53	11.61	0.01	0.09	945336	13.41	6.71	-5.53
21	Adeptus Health	ADPT	54.63	56.24	53.00	54.61	-0.30	-0.55	232924	123.97	37.13	0.17
22	ADT Corp.	ADT	40.97	41.15	40.95	41.10	0.15	0.37	1249587	42.88	24.22	24.62
23	Advance Auto Parts	AAP	160.99	162.04	159.95	161.82	0.73	0.45	823452	201.24	131.59	7.51
24	Advanced Drainage Systems	WMS	20.50	20.94	20.45	20.93	0.31	1.50	107246	33.28	17.72	-12.90
25	Advanced Semiconductor Engineering ADR	ASX	5.66	5.77	5.66	5.76	0.03	0.52	1329756	7.65	4.54	1.59
26	Advantage Oil&Gas	AAV	5.05	5.19	5.05	5.13	-0.02	-0.39	47516	6.59	4.17	0.98
27	Advantest ADR	ATE	9.55	9.55	9.52	9.52	-0.33	-3.35	3743	13.31	7.01	14.70
28	AECOM	ACM	30.31	30.65	30.20	30.59	0.03	0.10	412599	35.40	22.80	1.86
29	Aegean Marine Petroleum Network	ANW	7.10	7.64	7.10	7.50	0.27	3.73	161016	15.71	5.52	-10.29
...	...	...	...	...	...	...	...	...	...	...	...	...
2112	W.W. Grainger	GWW	228.05	231.00	228.05	229.15	0.04	0.02	497110	252.87	176.85	13.11
2113	Wyndham Worldwide	WYN	74.00	75.20	73.42	74.96	0.23	0.31	965832	92.13	60.59	3.18
2114	Xactly	XTLY	6.00	6.14	5.76	6.12	0.12	2.00	54491	10.67	4.68	-28.25
2115	Xcel Energy	XEL	41.12	41.39	40.94	41.23	0.11	0.27	2173378	41.61	31.76	14.81
2116	Xenia Hotels&Resorts	XHR	14.89	15.12	14.79	14.99	0.03	0.20	273200	24.39	12.10	-2.22
2117	Xerium Technologies	XRM	5.01	5.32	4.82	5.24	0.25	5.01	99738	18.93	4.82	-55.78
2118	Xerox	XRX	10.51	10.64	10.35	10.63	0.07	0.66	4049462	13.33	8.48	NaN
2119	Xinyuan Real Estate ADR	XIN	4.07	4.17	4.00	4.10	0.01	0.24	273591	4.51	2.20	11.11
2120	XL Group	XL	36.70	36.81	36.42	36.61	-0.44	-1.19	1921788	40.48	5.40	-6.56
2121	XO Group	XOXO	15.61	15.81	15.61	15.80	0.10	0.64	61323	17.91	13.55	-1.62
2122	XPO Logistics	XPO	30.62	32.03	30.58	32.01	0.98	3.16	859040	50.96	18.04	17.47
2123	Xueda Education Group ADR	XUE	5.38	5.38	5.36	5.36	-0.01	-0.19	1062	5.45	2.56	-0.74
2124	Xylem Inc.	XYL	39.04	39.82	39.01	39.82	0.45	1.14	1784549	39.82	29.90	9.10
2125	Yadkin Financial	YDKN	22.72	23.01	22.49	22.91	0.03	0.13	90991	27.23	18.80	-8.98
2126	Yamana Gold	AUY	2.92	2.95	2.87	2.92	0.03	1.04	7475494	4.17	1.38	56.99
2127	Yanzhou Coal Mining ADR	YZC	5.08	5.08	5.01	5.07	-0.02	-0.39	60605	12.24	3.66	10.70
2128	Yelp	YELP	19.09	19.63	18.65	19.54	0.24	1.24	1099046	52.51	14.53	-32.15
2129	Yingli Green Energy Holding ADR	YGE	4.60	4.70	4.34	4.52	-0.15	-3.21	72346	21.30	2.93	-0.22
2130	Yirendai ADR	YRD	8.78	9.80	8.62	9.59	0.43	4.69	415445	10.39	3.35	1.48
2131	Youku Tudou ADR	YOKU	27.48	27.48	27.47	27.48	0.01	0.04	667327	31.50	11.85	1.29
2132	YPF ADR	YPF	17.42	18.10	17.20	18.01	0.22	1.24	554409	32.00	12.67	14.57
2133	Yum! Brands	YUM	80.11	80.11	78.91	79.35	-1.20	-1.49	2990963	95.90	64.58	8.62
2134	YuMe	YUME	3.65	3.69	3.60	3.67	-0.01	-0.27	34939	5.76	2.35	4.56
2135	ZAIS Financial	ZFC	15.20	15.36	15.01	15.27	0.17	1.13	78959	19.00	12.63	1.26
2136	Zayo Group Holdings	ZAYO	23.74	23.86	23.38	23.57	-0.34	-1.42	931299	29.62	19.59	-11.36
2137	Zendesk	ZEN	19.50	19.60	18.98	19.52	-0.12	-0.61	595860	27.54	14.38	-26.17
2138	Zhaopin ADR	ZPIN	15.85	16.00	15.59	15.78	-0.09	-0.57	93009	17.66	11.65	3.00
2139	Zimmer Biomet Holdings	ZBH	103.58	104.31	103.07	103.74	-0.69	-0.66	850675	119.10	88.27	1.12
2140	Zoe's Kitchen	ZOES	37.65	39.00	37.09	38.48	0.71	1.88	154304	46.61	23.17	37.53
2141	Zoetis	ZTS	42.27	43.06	42.13	42.48	-0.16	-0.38	4784003	55.38	37.73	-11.35

	1st country code	2nd country code	distance	1st name code	2nd name code
0	1	2	6405.558	USA	Germany
1	1	3	10910.268	USA	Japan
2	1	4	5904.034	USA	United.Kingdom
3	1	5	6168.601	USA	France
4	1	6	7222.464	USA	Italy
5	1	7	733.894	USA	Canada
6	1	8	11154.311	USA	China
7	1	9	6197.566	USA	Netherlands
8	1	10	13128.552	USA	Hong.Kong
9	1	11	6221.288	USA	Belgium-Lux
10	1	12	15561.059	USA	Singapore
11	1	13	6096.311	USA	Spain
12	1	14	11174.358	USA	Korea.RP.(S)
13	1	16	3038.551	USA	Mexico
14	1	17	12659.382	USA	Taiwan
15	1	18	6606.992	USA	Switzerland
16	1	20	7130.317	USA	Austria
17	1	21	6641.324	USA	Sweden
18	1	22	14168.513	USA	Thailand
19	1	23	15958.048	USA	Australia
20	1	24	15349.792	USA	Malaysia
21	1	25	7827.286	USA	USSR
22	1	26	10856.876	USA	Saudi.Arabia
23	1	27	8733.409	USA	Turkey
24	1	28	16370.820	USA	Indonesia
25	1	29	6798.516	USA	Brazil
26	1	30	6518.058	USA	Denmark
27	1	31	6238.107	USA	Norway
28	1	32	5742.315	USA	Portugal
29	1	33	5448.428	USA	Ireland
...	...	...	...	...	...
9286	150	159	19279.293	Sierra.Leone	Solomon.Islands
9287	150	167	10838.858	Sierra.Leone	Bhutan
9288	152	153	1760.350	Rwanda	Somalia
9289	152	154	1815.308	Rwanda	Comoros
9290	152	155	177.167	Rwanda	Burundi
9291	152	157	2282.678	Rwanda	Chad
9292	152	158	1458.707	Rwanda	Central.African.Repu
9293	152	159	14335.211	Rwanda	Solomon.Islands
9294	152	167	7167.761	Rwanda	Bhutan
9295	153	154	1547.911	Somalia	Comoros
9296	153	155	1880.527	Somalia	Burundi
9297	153	157	3525.780	Somalia	Chad
9298	153	158	2987.717	Somalia	Central.African.Repu
9299	153	159	12752.959	Somalia	Solomon.Islands
9300	153	167	5501.405	Somalia	Bhutan
9301	154	155	1789.183	Comoros	Burundi
9302	154	157	4091.340	Comoros	Chad
9303	154	158	3265.182	Comoros	Central.African.Repu
9304	154	159	12645.417	Comoros	Solomon.Islands
9305	154	167	6642.979	Comoros	Bhutan
9306	155	157	2342.191	Burundi	Chad
9307	155	158	1477.820	Burundi	Central.African.Repu
9308	155	159	14371.021	Burundi	Solomon.Islands
9309	155	167	7318.312	Burundi	Bhutan
9310	157	158	946.032	Chad	Central.African.Repu
9311	157	159	16187.149	Chad	Solomon.Islands
9312	157	167	7897.615	Chad	Bhutan
9313	158	159	15731.287	Central.African.Repu	Solomon.Islands
9314	158	167	7932.815	Central.African.Repu	Bhutan
9315	159	167	8601.426	Solomon.Islands	Bhutan

	Name	Symbol	Open	High	Low	Close	Net Chg	% Chg	Volume	52 Wk High	52 Wk Low	YTD % Chg
5	AAC Holdings	AAC	18.18	18.48	17.99	18.38	0.17	0.93	123794	46.60	14.36	-3.57
6	AAR Corp.	AIR	22.82	22.83	21.45	22.80	-0.23	-1.00	463662	34.24	18.36	-13.28
7	Aaron's Inc.	AAN	23.76	23.88	23.37	23.87	-0.14	-0.58	240738	40.80	20.24	6.61
8	ABB ADR	ABB	18.80	18.94	18.78	18.93	-0.16	-0.84	1308716	23.66	15.74	6.77
9	Abbott Laboratories	ABT	40.64	40.90	40.36	40.75	-0.03	-0.07	4038030	51.74	36.00	-9.26

	Name	Symbol	Open	High	Low	Close	Net Chg	% Chg	Volume	52 Wk High	52 Wk Low	YTD % Chg
61	Alexander's Inc.	ALX	383.40	383.48	380.42	380.42	-0.48	-0.13	1408	468.25	350.03	-0.96
206	Banco Bradesco Ord ADR	BBDO	7.89	8.24	7.89	8.24	0.02	0.24	1598	11.46	4.15	57.85
246	Berkshire Hathaway Cl A	BRKA	210200.01	210980.00	209525.00	210530.00	-840.00	-0.40	179	223011.49	186900.00	6.44
299	Brookfield Canada Office Properties	BOXC	21.91	22.03	21.75	21.91	0.28	1.29	1503	24.00	16.22	15.26
306	BRT Realty Trust	BRT	6.76	6.76	6.70	6.75	NaN	NaN	1830	7.30	5.41	6.47
345	Capital One Financia Wt	COF%	28.40	28.74	28.40	28.74	-0.62	-2.11	1250	50.01	19.23	-8.33
425	China Xiniya Fashion ADR	XNY	1.20	1.20	1.20	1.20	NaN	0.08	137	2.44	1.03	-32.53
427	China Zenix Auto International ADR	ZX	1.08	1.08	1.08	1.08	-0.02	-1.82	158	1.45	0.43	36.71
491	Companhia Energetica de Minas Gerais-Cemig ADR	CIGC	2.09	2.09	2.04	2.06	-0.05	-2.37	812	5.50	1.08	18.39
508	Constellation Brands Cl B	STZB	149.49	149.49	149.42	149.42	0.18	0.12	205	156.00	115.00	3.64
648	Eastman Kodak Co. WT 125%	KODK%	2.54	2.54	2.54	2.54	-0.22	-7.97	400	6.45	0.98	-8.30
649	Eastman Kodak WT	KODK%A	2.00	2.00	1.85	1.86	-0.34	-15.45	700	5.75	0.27	1.64
670	Embotelladora Andina S.A. (Series A) ADR	AKOA	16.00	16.37	16.00	16.37	0.41	2.57	200	19.20	12.53	0.55
768	Federal Agricultural Mortgage Cl A	AGMA	35.07	37.66	35.07	37.50	3.73	11.05	550	37.66	24.75	38.68
793	First Marblehead	FMD	3.51	3.51	3.51	3.51	-0.05	-1.40	1112	7.00	2.81	-16.63
868	GeoPark	GPRK	2.90	2.90	2.89	2.89	0.11	3.83	642	5.73	2.45	7.04
901	Gray Television Cl A	GTNA	10.28	10.28	10.28	10.28	-0.31	-2.93	155	16.12	9.05	-24.80
942	Haverty Furniture Cl A	HVTA	20.42	20.42	20.42	20.42	0.25	1.24	105	24.40	17.52	-5.02
953	HealthSouth WT	HLS%	1.48	1.48	1.48	1.48	-0.02	-1.33	101	2.30	0.74	-31.16
1002	HudBay Minerals WT	HBM%	0.24	0.25	0.24	0.24	-0.03	-10.88	341	1.73	0.08	0.29
1024	Industrias Bachoco ADR	IBA	50.84	50.97	50.53	50.97	-0.10	-0.20	830	64.30	40.43	3.53
1084	John Wiley&Sons Cl B	JWB	47.73	47.73	47.73	47.73	0.85	1.82	253	62.01	40.18	5.12
1176	Lincoln National WT	LNC%	31.77	31.77	30.40	30.40	-1.73	-5.37	1800	53.99	24.59	-28.47
1238	McCormick Com. Vtg.	MKCV	94.96	96.14	94.96	96.14	2.47	2.64	1051	96.14	75.20	11.31
1307	M&T Bank WT	MTB%	38.95	38.95	38.00	38.00	-1.58	-3.99	300	59.00	30.26	-22.00
1767	Steel Partners Holdings	SPLP	15.09	15.13	14.75	14.80	-0.10	-0.67	1753	19.37	12.86	-9.65
1800	SunTrust Banks Cl A WT	STI/%A	7.15	7.15	7.15	7.15	-0.75	-9.49	100	12.76	5.76	-35.06
1829	TCF Financial WT	TCB%	1.72	1.72	1.72	1.72	0.02	1.39	200	3.10	1.35	-18.10
1982	Urstadt Biddle Properties	UBP	17.54	17.95	17.54	17.95	0.45	2.57	1250	19.95	15.89	0.84
2076	Westmoreland Resource Partners	WMLP	4.81	4.81	4.79	4.79	-0.08	-1.64	1260	12.65	2.38	19.75
2123	Xueda Education Group ADR	XUE	5.38	5.38	5.36	5.36	-0.01	-0.19	1062	5.45	2.56	-0.74