pd.to_datetime much slower with supplied format than when format is inferred · Issue #10178 · pandas-dev/pandas (original) (raw)

It is much slower when converting a date string to supply a date format for a column than for it to be inferred. I would've though there should be less work to do when the format is known (and supplied)

To test

df = DataFrame({'date_text':["2015-05-18" for i in range(106)]}) %timeit pd.to_datetime(df['date_text'],infer_datetime_format=True, box=False).values.view('i8')/109 10 loops, best of 3: 115 ms per loop #Top line from %prun of same command: ncalls tottime percall cumtime percall filename:lineno(function) 1 0.095 0.095 0.095 0.095 {pandas.tslib.array_to_datetime}

%timeit pd.to_datetime(df['date_text'],format="%Y-%m-%d", box=False).values.view('i8')/10**9 1 loops, best of 3: 2.27 s per loop #Top line from %prun of same command: ncalls tottime percall cumtime percall filename:lineno(function) 1 2.282 2.282 2.282 2.282 {pandas.tslib.array_strptime}

This plot is taken from this S/O post which shows the difference over a larger range of sizes (and compared to other methods).

INSTALLED VERSIONS

commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8

pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.2.0-b1
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: 1.4.0
rpy2: None
sqlalchemy: 1.0.0
pymysql: None
psycopg2: None