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Improved docs for infer_datetime_format #12606

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d3ce9a1
Improved docs infer_datetime_format
robintw Mar 13, 2016
19fd9d4
Fix linter error
robintw Mar 13, 2016
50311ce
Updated docs
robintw Mar 13, 2016
4dbb8ec
Added example to docs
robintw Mar 13, 2016
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6 changes: 4 additions & 2 deletions pandas/io/parsers.py
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Expand Up @@ -132,8 +132,10 @@ class ParserWarning(Warning):

Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : boolean, default False
If True and parse_dates is enabled for a column, attempt to infer
the datetime format to speed up the processing
If True and parse_dates is enabled, pandas will attempt to infer the format
of the datetime strings in the columns, and if it can be inferred, switch
to a faster method of parsing them. In some cases this can increase the
parsing speed by ~5-10x.
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put in a link to pd.to_datetime here

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hah, I forgot my own comment. If you want to add this in another PR great.

keep_date_col : boolean, default False
If True and parse_dates specifies combining multiple columns then
keep the original columns.
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19 changes: 17 additions & 2 deletions pandas/tseries/tools.py
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Expand Up @@ -231,8 +231,10 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
(e.g. a unix timestamp), which is an integer/float number.
infer_datetime_format : boolean, default False
If no `format` is given, try to infer the format based on the first
datetime string. Provides a large speed-up in many cases.
If True and no `format` is given, attempt to infer the format of the
datetime strings, and if it can be inferred, switch to a faster
method of parsing them. In some cases this can increase the parsing
speed by ~5-10x.

Returns
-------
Expand Down Expand Up @@ -273,6 +275,19 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
99 2000-04-09
Length: 100, dtype: datetime64[ns]

Infer the format from the first entry

>>> pd.to_datetime(df.month + '/' + df.day + '/' + df.year,
infer_datetime_format=True)
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this is not valid code (as df.month are integers), do something like:

s = df.month.astype(str) + '/' + df.day.astype(str) + '/' + df.year.astype(str)

then infer on that

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I assumed that the df = df.astype(str) from the example above would still have applied.

Should each example be entirely separate?

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ahh I see that. ok no, that is fine then (maybe rename df -> dfs or something (on the .astype(str) line)

0 2000-01-01
1 2000-01-02
...
98 2000-04-08
99 2000-04-09

This gives the same results as omitting the `infer_datetime_format=True`,
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this is confusing, pls reword

but is much faster.

Date that does not meet timestamp limitations:

>>> pd.to_datetime('13000101', format='%Y%m%d')
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