# 61、pandas.to_numeric函数

# 61-1、转换Series

# 0 1.0

# 1 2.0

# 2 3.0

# 3 NaN

# 4 5.0

# dtype: float64

# 61-2、转换DataFrame

# A B

# 0 1.0 10.5

# 1 2.0 20.1

# 2 3.0 30.2

# 3 NaN 40.0

# 4 5.0 50.5

62、pandas.to_datetime函数

62-1、语法

# 62、pandas.to_datetime函数

pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=False, format=None, exact=_NoDefault.no_default, unit=None, infer_datetime_format=_NoDefault.no_default, origin='unix', cache=True)code>

Convert argument to datetime.

This function converts a scalar, array-like, Series or DataFrame/dict-like to a pandas datetime object.

Parameters:

argint, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like

The object to convert to a datetime. If a DataFrame is provided, the method expects minimally the following columns: "year", "month", "day". The column “year” must be specified in 4-digit format.

errors{‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’

If 'raise', then invalid parsing will raise an exception.

If 'coerce', then invalid parsing will be set as NaT.

If 'ignore', then invalid parsing will return the input.

dayfirstbool, default False

Specify a date parse order if arg is str or is list-like. If True, parses dates with the day first, e.g. "10/11/12" is parsed as 2012-11-10.

Warning

dayfirst=True is not strict, but will prefer to parse with day first.

yearfirstbool, default False

Specify a date parse order if arg is str or is list-like.

If True parses dates with the year first, e.g. "10/11/12" is parsed as 2010-11-12.

If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil).

Warning

yearfirst=True is not strict, but will prefer to parse with year first.

utcbool, default False

Control timezone-related parsing, localization and conversion.

If True, the function always returns a timezone-aware UTC-localized Timestamp, Series or DatetimeIndex. To do this, timezone-naive inputs are localized as UTC, while timezone-aware inputs are converted to UTC.

If False (default), inputs will not be coerced to UTC. Timezone-naive inputs will remain naive, while timezone-aware ones will keep their time offsets. Limitations exist for mixed offsets (typically, daylight savings), see Examples section for details.

Warning

In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless utc=True. Please specify utc=True to opt in to the new behaviour and silence this warning. To create a Series with mixed offsets and object dtype, please use apply and datetime.datetime.strptime.

See also: pandas general documentation about timezone conversion and localization.

formatstr, default None

The strftime to parse time, e.g. "%d/%m/%Y". See strftime documentation for more information on choices, though note that "%f" will parse all the way up to nanoseconds. You can also pass:

“ISO8601”, to parse any ISO8601 time string (not necessarily in exactly the same format);

“mixed”, to infer the format for each element individually. This is risky, and you should probably use it along with dayfirst.

Note

If a DataFrame is passed, then format has no effect.

exactbool, default True

Control how format is used:

If True, require an exact format match.

If False, allow the format to match anywhere in the target string.

Cannot be used alongside format='ISO8601' or format='mixed'.code>

unitstr, default ‘ns’

The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the origin. Example, with unit='ms' and origin='unix', this would calculate the number of milliseconds to the unix epoch start.code>

infer_datetime_formatbool, default False

If True and no format is given, attempt to infer the format of the datetime strings based on the first non-NaN element, 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.

Deprecated since version 2.0.0: A strict version of this argument is now the default, passing it has no effect.

originscalar, default ‘unix’

Define the reference date. The numeric values would be parsed as number of units (defined by unit) since this reference date.

If 'unix' (or POSIX) time; origin is set to 1970-01-01.

If 'julian', unit must be 'D', and origin is set to beginning of Julian Calendar. Julian day number 0 is assigned to the day starting at noon on January 1, 4713 BC.

If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date string), origin is set to Timestamp identified by origin.

If a float or integer, origin is the difference (in units determined by the unit argument) relative to 1970-01-01.

cachebool, default True

If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. The cache is only used when there are at least 50 values. The presence of out-of-bounds values will render the cache unusable and may slow down parsing.

Returns:

datetime

If parsing succeeded. Return type depends on input (types in parenthesis correspond to fallback in case of unsuccessful timezone or out-of-range timestamp parsing):

scalar: Timestamp (or datetime.datetime)

array-like: DatetimeIndex (or Series with object dtype containing datetime.datetime)

Series: Series of datetime64 dtype (or Series of object dtype containing datetime.datetime)

DataFrame: Series of datetime64 dtype (or Series of object dtype containing datetime.datetime)

Raises:

ParserError

When parsing a date from string fails.

ValueError

When another datetime conversion error happens. For example when one of ‘year’, ‘month’, day’ columns is missing in a DataFrame, or when a Timezone-aware datetime.datetime is found in an array-like of mixed time offsets, and utc=False.

62-2、参数