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ENH/TST: add anonymous reading of s3 for public buckets #7281

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Jun 2, 2014
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1 change: 1 addition & 0 deletions ci/requirements-2.7.txt
Original file line number Diff line number Diff line change
Expand Up @@ -20,3 +20,4 @@ scipy==0.13.3
beautifulsoup4==4.2.1
statsmodels==0.5.0
bigquery==2.0.17
boto==2.26.1
1 change: 1 addition & 0 deletions doc/source/v0.14.1.txt
Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,7 @@ Known Issues

Enhancements
~~~~~~~~~~~~
- Tests for basic reading of public S3 buckets now exist (:issue:`7281`).

.. _whatsnew_0141.performance:

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7 changes: 6 additions & 1 deletion pandas/io/common.py
Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,12 @@ def get_filepath_or_buffer(filepath_or_buffer, encoding=None):
# Assuming AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
# are environment variables
parsed_url = parse_url(filepath_or_buffer)
conn = boto.connect_s3()

try:
conn = boto.connect_s3()
except boto.exception.NoAuthHandlerFound:
conn = boto.connect_s3(anon=True)

b = conn.get_bucket(parsed_url.netloc)
k = boto.s3.key.Key(b)
k.key = parsed_url.path
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245 changes: 245 additions & 0 deletions pandas/io/tests/data/tips.csv
Original file line number Diff line number Diff line change
@@ -0,0 +1,245 @@
total_bill,tip,sex,smoker,day,time,size
16.99,1.01,Female,No,Sun,Dinner,2
10.34,1.66,Male,No,Sun,Dinner,3
21.01,3.5,Male,No,Sun,Dinner,3
23.68,3.31,Male,No,Sun,Dinner,2
24.59,3.61,Female,No,Sun,Dinner,4
25.29,4.71,Male,No,Sun,Dinner,4
8.77,2.0,Male,No,Sun,Dinner,2
26.88,3.12,Male,No,Sun,Dinner,4
15.04,1.96,Male,No,Sun,Dinner,2
14.78,3.23,Male,No,Sun,Dinner,2
10.27,1.71,Male,No,Sun,Dinner,2
35.26,5.0,Female,No,Sun,Dinner,4
15.42,1.57,Male,No,Sun,Dinner,2
18.43,3.0,Male,No,Sun,Dinner,4
14.83,3.02,Female,No,Sun,Dinner,2
21.58,3.92,Male,No,Sun,Dinner,2
10.33,1.67,Female,No,Sun,Dinner,3
16.29,3.71,Male,No,Sun,Dinner,3
16.97,3.5,Female,No,Sun,Dinner,3
20.65,3.35,Male,No,Sat,Dinner,3
17.92,4.08,Male,No,Sat,Dinner,2
20.29,2.75,Female,No,Sat,Dinner,2
15.77,2.23,Female,No,Sat,Dinner,2
39.42,7.58,Male,No,Sat,Dinner,4
19.82,3.18,Male,No,Sat,Dinner,2
17.81,2.34,Male,No,Sat,Dinner,4
13.37,2.0,Male,No,Sat,Dinner,2
12.69,2.0,Male,No,Sat,Dinner,2
21.7,4.3,Male,No,Sat,Dinner,2
19.65,3.0,Female,No,Sat,Dinner,2
9.55,1.45,Male,No,Sat,Dinner,2
18.35,2.5,Male,No,Sat,Dinner,4
15.06,3.0,Female,No,Sat,Dinner,2
20.69,2.45,Female,No,Sat,Dinner,4
17.78,3.27,Male,No,Sat,Dinner,2
24.06,3.6,Male,No,Sat,Dinner,3
16.31,2.0,Male,No,Sat,Dinner,3
16.93,3.07,Female,No,Sat,Dinner,3
18.69,2.31,Male,No,Sat,Dinner,3
31.27,5.0,Male,No,Sat,Dinner,3
16.04,2.24,Male,No,Sat,Dinner,3
17.46,2.54,Male,No,Sun,Dinner,2
13.94,3.06,Male,No,Sun,Dinner,2
9.68,1.32,Male,No,Sun,Dinner,2
30.4,5.6,Male,No,Sun,Dinner,4
18.29,3.0,Male,No,Sun,Dinner,2
22.23,5.0,Male,No,Sun,Dinner,2
32.4,6.0,Male,No,Sun,Dinner,4
28.55,2.05,Male,No,Sun,Dinner,3
18.04,3.0,Male,No,Sun,Dinner,2
12.54,2.5,Male,No,Sun,Dinner,2
10.29,2.6,Female,No,Sun,Dinner,2
34.81,5.2,Female,No,Sun,Dinner,4
9.94,1.56,Male,No,Sun,Dinner,2
25.56,4.34,Male,No,Sun,Dinner,4
19.49,3.51,Male,No,Sun,Dinner,2
38.01,3.0,Male,Yes,Sat,Dinner,4
26.41,1.5,Female,No,Sat,Dinner,2
11.24,1.76,Male,Yes,Sat,Dinner,2
48.27,6.73,Male,No,Sat,Dinner,4
20.29,3.21,Male,Yes,Sat,Dinner,2
13.81,2.0,Male,Yes,Sat,Dinner,2
11.02,1.98,Male,Yes,Sat,Dinner,2
18.29,3.76,Male,Yes,Sat,Dinner,4
17.59,2.64,Male,No,Sat,Dinner,3
20.08,3.15,Male,No,Sat,Dinner,3
16.45,2.47,Female,No,Sat,Dinner,2
3.07,1.0,Female,Yes,Sat,Dinner,1
20.23,2.01,Male,No,Sat,Dinner,2
15.01,2.09,Male,Yes,Sat,Dinner,2
12.02,1.97,Male,No,Sat,Dinner,2
17.07,3.0,Female,No,Sat,Dinner,3
26.86,3.14,Female,Yes,Sat,Dinner,2
25.28,5.0,Female,Yes,Sat,Dinner,2
14.73,2.2,Female,No,Sat,Dinner,2
10.51,1.25,Male,No,Sat,Dinner,2
17.92,3.08,Male,Yes,Sat,Dinner,2
27.2,4.0,Male,No,Thur,Lunch,4
22.76,3.0,Male,No,Thur,Lunch,2
17.29,2.71,Male,No,Thur,Lunch,2
19.44,3.0,Male,Yes,Thur,Lunch,2
16.66,3.4,Male,No,Thur,Lunch,2
10.07,1.83,Female,No,Thur,Lunch,1
32.68,5.0,Male,Yes,Thur,Lunch,2
15.98,2.03,Male,No,Thur,Lunch,2
34.83,5.17,Female,No,Thur,Lunch,4
13.03,2.0,Male,No,Thur,Lunch,2
18.28,4.0,Male,No,Thur,Lunch,2
24.71,5.85,Male,No,Thur,Lunch,2
21.16,3.0,Male,No,Thur,Lunch,2
28.97,3.0,Male,Yes,Fri,Dinner,2
22.49,3.5,Male,No,Fri,Dinner,2
5.75,1.0,Female,Yes,Fri,Dinner,2
16.32,4.3,Female,Yes,Fri,Dinner,2
22.75,3.25,Female,No,Fri,Dinner,2
40.17,4.73,Male,Yes,Fri,Dinner,4
27.28,4.0,Male,Yes,Fri,Dinner,2
12.03,1.5,Male,Yes,Fri,Dinner,2
21.01,3.0,Male,Yes,Fri,Dinner,2
12.46,1.5,Male,No,Fri,Dinner,2
11.35,2.5,Female,Yes,Fri,Dinner,2
15.38,3.0,Female,Yes,Fri,Dinner,2
44.3,2.5,Female,Yes,Sat,Dinner,3
22.42,3.48,Female,Yes,Sat,Dinner,2
20.92,4.08,Female,No,Sat,Dinner,2
15.36,1.64,Male,Yes,Sat,Dinner,2
20.49,4.06,Male,Yes,Sat,Dinner,2
25.21,4.29,Male,Yes,Sat,Dinner,2
18.24,3.76,Male,No,Sat,Dinner,2
14.31,4.0,Female,Yes,Sat,Dinner,2
14.0,3.0,Male,No,Sat,Dinner,2
7.25,1.0,Female,No,Sat,Dinner,1
38.07,4.0,Male,No,Sun,Dinner,3
23.95,2.55,Male,No,Sun,Dinner,2
25.71,4.0,Female,No,Sun,Dinner,3
17.31,3.5,Female,No,Sun,Dinner,2
29.93,5.07,Male,No,Sun,Dinner,4
10.65,1.5,Female,No,Thur,Lunch,2
12.43,1.8,Female,No,Thur,Lunch,2
24.08,2.92,Female,No,Thur,Lunch,4
11.69,2.31,Male,No,Thur,Lunch,2
13.42,1.68,Female,No,Thur,Lunch,2
14.26,2.5,Male,No,Thur,Lunch,2
15.95,2.0,Male,No,Thur,Lunch,2
12.48,2.52,Female,No,Thur,Lunch,2
29.8,4.2,Female,No,Thur,Lunch,6
8.52,1.48,Male,No,Thur,Lunch,2
14.52,2.0,Female,No,Thur,Lunch,2
11.38,2.0,Female,No,Thur,Lunch,2
22.82,2.18,Male,No,Thur,Lunch,3
19.08,1.5,Male,No,Thur,Lunch,2
20.27,2.83,Female,No,Thur,Lunch,2
11.17,1.5,Female,No,Thur,Lunch,2
12.26,2.0,Female,No,Thur,Lunch,2
18.26,3.25,Female,No,Thur,Lunch,2
8.51,1.25,Female,No,Thur,Lunch,2
10.33,2.0,Female,No,Thur,Lunch,2
14.15,2.0,Female,No,Thur,Lunch,2
16.0,2.0,Male,Yes,Thur,Lunch,2
13.16,2.75,Female,No,Thur,Lunch,2
17.47,3.5,Female,No,Thur,Lunch,2
34.3,6.7,Male,No,Thur,Lunch,6
41.19,5.0,Male,No,Thur,Lunch,5
27.05,5.0,Female,No,Thur,Lunch,6
16.43,2.3,Female,No,Thur,Lunch,2
8.35,1.5,Female,No,Thur,Lunch,2
18.64,1.36,Female,No,Thur,Lunch,3
11.87,1.63,Female,No,Thur,Lunch,2
9.78,1.73,Male,No,Thur,Lunch,2
7.51,2.0,Male,No,Thur,Lunch,2
14.07,2.5,Male,No,Sun,Dinner,2
13.13,2.0,Male,No,Sun,Dinner,2
17.26,2.74,Male,No,Sun,Dinner,3
24.55,2.0,Male,No,Sun,Dinner,4
19.77,2.0,Male,No,Sun,Dinner,4
29.85,5.14,Female,No,Sun,Dinner,5
48.17,5.0,Male,No,Sun,Dinner,6
25.0,3.75,Female,No,Sun,Dinner,4
13.39,2.61,Female,No,Sun,Dinner,2
16.49,2.0,Male,No,Sun,Dinner,4
21.5,3.5,Male,No,Sun,Dinner,4
12.66,2.5,Male,No,Sun,Dinner,2
16.21,2.0,Female,No,Sun,Dinner,3
13.81,2.0,Male,No,Sun,Dinner,2
17.51,3.0,Female,Yes,Sun,Dinner,2
24.52,3.48,Male,No,Sun,Dinner,3
20.76,2.24,Male,No,Sun,Dinner,2
31.71,4.5,Male,No,Sun,Dinner,4
10.59,1.61,Female,Yes,Sat,Dinner,2
10.63,2.0,Female,Yes,Sat,Dinner,2
50.81,10.0,Male,Yes,Sat,Dinner,3
15.81,3.16,Male,Yes,Sat,Dinner,2
7.25,5.15,Male,Yes,Sun,Dinner,2
31.85,3.18,Male,Yes,Sun,Dinner,2
16.82,4.0,Male,Yes,Sun,Dinner,2
32.9,3.11,Male,Yes,Sun,Dinner,2
17.89,2.0,Male,Yes,Sun,Dinner,2
14.48,2.0,Male,Yes,Sun,Dinner,2
9.6,4.0,Female,Yes,Sun,Dinner,2
34.63,3.55,Male,Yes,Sun,Dinner,2
34.65,3.68,Male,Yes,Sun,Dinner,4
23.33,5.65,Male,Yes,Sun,Dinner,2
45.35,3.5,Male,Yes,Sun,Dinner,3
23.17,6.5,Male,Yes,Sun,Dinner,4
40.55,3.0,Male,Yes,Sun,Dinner,2
20.69,5.0,Male,No,Sun,Dinner,5
20.9,3.5,Female,Yes,Sun,Dinner,3
30.46,2.0,Male,Yes,Sun,Dinner,5
18.15,3.5,Female,Yes,Sun,Dinner,3
23.1,4.0,Male,Yes,Sun,Dinner,3
15.69,1.5,Male,Yes,Sun,Dinner,2
19.81,4.19,Female,Yes,Thur,Lunch,2
28.44,2.56,Male,Yes,Thur,Lunch,2
15.48,2.02,Male,Yes,Thur,Lunch,2
16.58,4.0,Male,Yes,Thur,Lunch,2
7.56,1.44,Male,No,Thur,Lunch,2
10.34,2.0,Male,Yes,Thur,Lunch,2
43.11,5.0,Female,Yes,Thur,Lunch,4
13.0,2.0,Female,Yes,Thur,Lunch,2
13.51,2.0,Male,Yes,Thur,Lunch,2
18.71,4.0,Male,Yes,Thur,Lunch,3
12.74,2.01,Female,Yes,Thur,Lunch,2
13.0,2.0,Female,Yes,Thur,Lunch,2
16.4,2.5,Female,Yes,Thur,Lunch,2
20.53,4.0,Male,Yes,Thur,Lunch,4
16.47,3.23,Female,Yes,Thur,Lunch,3
26.59,3.41,Male,Yes,Sat,Dinner,3
38.73,3.0,Male,Yes,Sat,Dinner,4
24.27,2.03,Male,Yes,Sat,Dinner,2
12.76,2.23,Female,Yes,Sat,Dinner,2
30.06,2.0,Male,Yes,Sat,Dinner,3
25.89,5.16,Male,Yes,Sat,Dinner,4
48.33,9.0,Male,No,Sat,Dinner,4
13.27,2.5,Female,Yes,Sat,Dinner,2
28.17,6.5,Female,Yes,Sat,Dinner,3
12.9,1.1,Female,Yes,Sat,Dinner,2
28.15,3.0,Male,Yes,Sat,Dinner,5
11.59,1.5,Male,Yes,Sat,Dinner,2
7.74,1.44,Male,Yes,Sat,Dinner,2
30.14,3.09,Female,Yes,Sat,Dinner,4
12.16,2.2,Male,Yes,Fri,Lunch,2
13.42,3.48,Female,Yes,Fri,Lunch,2
8.58,1.92,Male,Yes,Fri,Lunch,1
15.98,3.0,Female,No,Fri,Lunch,3
13.42,1.58,Male,Yes,Fri,Lunch,2
16.27,2.5,Female,Yes,Fri,Lunch,2
10.09,2.0,Female,Yes,Fri,Lunch,2
20.45,3.0,Male,No,Sat,Dinner,4
13.28,2.72,Male,No,Sat,Dinner,2
22.12,2.88,Female,Yes,Sat,Dinner,2
24.01,2.0,Male,Yes,Sat,Dinner,4
15.69,3.0,Male,Yes,Sat,Dinner,3
11.61,3.39,Male,No,Sat,Dinner,2
10.77,1.47,Male,No,Sat,Dinner,2
15.53,3.0,Male,Yes,Sat,Dinner,2
10.07,1.25,Male,No,Sat,Dinner,2
12.6,1.0,Male,Yes,Sat,Dinner,2
32.83,1.17,Male,Yes,Sat,Dinner,2
35.83,4.67,Female,No,Sat,Dinner,3
29.03,5.92,Male,No,Sat,Dinner,3
27.18,2.0,Female,Yes,Sat,Dinner,2
22.67,2.0,Male,Yes,Sat,Dinner,2
17.82,1.75,Male,No,Sat,Dinner,2
18.78,3.0,Female,No,Thur,Dinner,2
30 changes: 30 additions & 0 deletions pandas/io/tests/test_parsers.py
Original file line number Diff line number Diff line change
Expand Up @@ -3414,6 +3414,36 @@ def test_convert_sql_column_decimals(self):
assert_same_values_and_dtype(result, expected)


class TestS3(tm.TestCase):
def setUp(self):
try:
import boto
except ImportError:
raise nose.SkipTest("boto not installed")

if compat.PY3:
raise nose.SkipTest("boto incompatible with Python 3")

@tm.network
def test_parse_public_s3_bucket(self):
import nose.tools as nt
df = pd.read_csv('s3://nyqpug/tips.csv')
nt.assert_true(isinstance(df, pd.DataFrame))
nt.assert_false(df.empty)
tm.assert_frame_equal(pd.read_csv(tm.get_data_path('tips.csv')), df)

@tm.network
def test_s3_fails(self):
import boto
with tm.assertRaisesRegexp(boto.exception.S3ResponseError,
'S3ResponseError: 404 Not Found'):
pd.read_csv('s3://nyqpug/asdf.csv')

with tm.assertRaisesRegexp(boto.exception.S3ResponseError,
'S3ResponseError: 403 Forbidden'):
pd.read_csv('s3://cant_get_it/tips.csv')


def assert_same_values_and_dtype(res, exp):
tm.assert_equal(res.dtype, exp.dtype)
tm.assert_almost_equal(res, exp)
Expand Down