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This call produces 3 timestamps when it has to produce only two:
pd.date_range('2005-01-12 10:00', '2005-01-12 16:00', freq='345min')
this gives:
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-01-12 10:00:00, ..., 2005-01-12 21:30:00]
Length: 3, Freq: 345T, Timezone: None
strangely, for another date function works as expected:
pd.date_range('2005-01-13 10:00', '2005-01-13 16:00', freq='345min')
this gives:
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-01-13 10:00:00, 2005-01-13 15:45:00]
Length: 2, Freq: 345T, Timezone: None
Could you please take a look? Thanks!
Pandas versions:
INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
Hi Pandas devs!
This call produces 3 timestamps when it has to produce only two:
pd.date_range('2005-01-12 10:00', '2005-01-12 16:00', freq='345min')
this gives:
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-01-12 10:00:00, ..., 2005-01-12 21:30:00]
Length: 3, Freq: 345T, Timezone: None
strangely, for another date function works as expected:
pd.date_range('2005-01-13 10:00', '2005-01-13 16:00', freq='345min')
this gives:
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-01-13 10:00:00, 2005-01-13 15:45:00]
Length: 2, Freq: 345T, Timezone: None
Could you please take a look? Thanks!
Pandas versions:
INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
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