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Use general float format when writing to CSV buffer to prevent numerical overload #193

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Merged
merged 6 commits into from
Jul 26, 2018
Merged

Use general float format when writing to CSV buffer to prevent numerical overload #193

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10ee0da
Write to CSV stream with general float format.
anthonydelage Jul 22, 2018
7a5b410
Specify number of significant digits for float format.
anthonydelage Jul 23, 2018
8d84d84
Change format to '%.15g' and add tests.
anthonydelage Jul 24, 2018
ef0553f
Fixing style errors.
stickler-ci Jul 24, 2018
3d10d62
Define string as unicode in to_gbq's float test.
anthonydelage Jul 24, 2018
d13c370
Update Changelog.
anthonydelage Jul 26, 2018
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2 changes: 1 addition & 1 deletion pandas_gbq/load.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ def encode_chunk(dataframe):
csv_buffer = six.StringIO()
dataframe.to_csv(
csv_buffer, index=False, header=False, encoding='utf-8',
date_format='%Y-%m-%d %H:%M:%S.%f')
float_format='%.15g', date_format='%Y-%m-%d %H:%M:%S.%f')

# Convert to a BytesIO buffer so that unicode text is properly handled.
# See: https://github.com/pydata/pandas-gbq/issues/106
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15 changes: 15 additions & 0 deletions tests/unit/test_load.py
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Expand Up @@ -4,6 +4,7 @@
import pandas

from pandas_gbq import load
from io import StringIO


def test_encode_chunk_with_unicode():
Expand All @@ -20,6 +21,20 @@ def test_encode_chunk_with_unicode():
assert u'信用卡' in csv_string


def test_encode_chunk_with_floats():
"""Test that floats in a dataframe are encoded with at most 15 significant
figures.

See: https://github.com/pydata/pandas-gbq/issues/192
"""
input_csv = StringIO('01/01/17 23:00,1.05148,1.05153,1.05148,1.05153,4')
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For Py2 to pass, you need to put StringIO(u'...') to pass unicode into StringIO (you can see the test failure in Travis)

df = pandas.read_csv(input_csv, header=None)
csv_buffer = load.encode_chunk(df)
csv_bytes = csv_buffer.read()
csv_string = csv_bytes.decode('utf-8')
assert '1.05153' in csv_string


def test_encode_chunks_splits_dataframe():
df = pandas.DataFrame(numpy.random.randn(6, 4), index=range(6))
chunks = list(load.encode_chunks(df, chunksize=2))
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