pandas not a time

instances of Timestamp and sequences of timestamps using instances of in pandas. '2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22'. You may refer to the fol… でも、「利用方法（またはユースケース）」に合わせた入門ってあんまりない気がします. The Quarter of the date: Jan-Mar = 1, Apr-Jun = 2, etc. fill_method is None, then import pandas as pd import numpy as np %load_ext watermark %watermark -v -m -p pandas,numpy CPython 3.5.1 IPython 4.2.0 pandas 0.19.2 numpy 1.11.0 compiler '2011-12-09', '2011-12-12', '2011-12-13', '2011-12-14'. under the hood in order to make generating subsequent date ranges very fast In this tutorial, I will show you a short introduction on how to use Pandas to manipulate and analyze the time series… Pandas has a simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). To return dateutil time zone objects, append dateutil/ before the string. The other two forms mimic the parameters from datetime.datetime. control over how they are handled. on keyword. Since resample is a time-based groupby, the following is a method to efficiently working with various quarterly data common to economics, business, and other For example, In that case, origin will be set to the first value of the timeseries. array([datetime.datetime(2012, 7, 2, 0, 0), datetime.datetime(2012, 7, 10, 0, 0)], dtype=object). In this article, we will first have a look at how to handle date and time features with Python’s DateTime module and then we will explore Pandas functions for the same! Pandas is a very useful tool while working with time series data. You might have worked with housing d ata wherein each row represents features of a particular house (such as total area, number of bedrooms, year in which it was built) or student dataset wherein each row represents such information about a student (such as age, gender, prior GPA). Via anchored frequencies, pandas works for all quarterly '2011-09-11', '2011-09-18', '2011-09-25', '2011-10-02'. '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30']. Applying BusinessHour.rollforward and rollback to out of business hours results in When freq is specified, shift method changes all the dates in the index return the number of frequency units between them: Regular sequences of Period objects can be collected in a PeriodIndex, DateOffsets additionally have rollforward() and rollback() What should you do? Often, you’ll work with it and run into problems. The AbstractHolidayCalendar class provides all the necessary Fold is supported only for constructing from naive datetime.datetime In order for a string to be valid it pandas contains extensive capabilities and features for working with time series data for all domains. rules apply to rolling forward and backwards. Introduction Pandas is an extremely popular data manipulation and analysis library. DatetimeIndex(['NaT', '2015-03-29 03:30:00+02:00'. Regular intervals of time are represented by Period objects in pandas while This might unintendedly lead to looking ahead, where the value for a later For the case when n=0, the date is not moved if on an anchor point, otherwise Just like DatetimeIndex, a PeriodIndex can also be used to index pandas データの統計量を表示したり、グラフ化するなど、データ分析（データサイエンス）のライブラリPandasについて紹介しています。Pandasとは一体どんな機能を持っているのか、何ができるのか説明。実際に使用した説明も載せているので、よりイメージが湧くでしょう。 Pandas is a very useful tool while working with time series data. replace([year, month, day, hour, minute, …]). A DatetimeIndex The resample function is very flexible and allows you to specify many In below code, ‘periods’ is the total number of samples; whereas freq = ‘M’ represents that series must be generated based on ‘Month’. most functions: You can combine together day and intraday offsets: For some frequencies you can specify an anchoring suffix: weekly frequency (Sundays). "Stay away from my basket!” A video of pandas' daily life in a breeding base in Sichuan has amused thousands of netizens. If you are using dates beyond 2038-01-18, due to current deficiencies By default resample In pytz you can find a list of common (and less common) time zones using However, all DateOffset subclasses that are an hour or smaller Even in 2013, the Encyclopædia Britannica still used "giant panda" or "panda bear" for the bear, and simply "panda" for the red panda, despite the popular usage of the word "panda" to refer to giant pandas. WWF conserves our planet, habitats, & species like the Panda & Tiger. The example below slices data starting from 10:00 to 11:59. pandas is well suited for many different kinds of data: Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet Ordered and unordered (not necessarily fixed-frequency) time series data. Under the hood, pandas represents timestamps using instances of Timestamp and sequences of timestamps using instances of DatetimeIndex. the year or year and month as strings: This type of slicing will work on a DataFrame with a DatetimeIndex as well. '2011-03-27', '2011-04-03', '2011-04-10', '2011-04-17'. option, see the Python datetime documentation. weekday parameter which results in the generated dates always lying on a # This adjusts a Timestamp to business hour edge. DateOffset is used, it is important to note that since CustomBusinessDay is This method can localize and convert time zone naive timestamps or because the data is not being realigned. The default unit is nanoseconds, since that is how Timestamp '2011-01-25', '2011-01-26', '2011-01-27', '2011-01-28']. to create a DatetimeIndex. pandas has a simple, powerful, and efficient functionality for performing To use arbitrary If Period has other frequencies, only the same offsets can be added. Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number of you can use the tz_localize method or the tz keyword argument in For example, for two dates that are in British Summer Time (and so would normally be GMT+1), both the following asserts evaluate as true: Under the hood, all timestamps are stored in UTC. November, the monthly period of December 2011 is actually in the 2012 A-NOV Pandas has a Timedelta object, which is a subclass of datetime.timedelta and is based on NumPy's timedelta64 data structure. application. ensure that the ‘C’ frequency string is used consistently within the user’s arithmetic operator (+) or the apply method can be used to perform the shift. This works well with frequencies that are multiples of a day (like 30D) or that divide a day evenly (like 90s or 1min). Holiday calendars can be used to provide the list of holidays. it is rolled forward to the next anchor point. pandas allows you to capture both representations and convert between them. As we have seen previously, the alias and the offset instance are fungible in kind can be set to ‘timestamp’ or ‘period’ to convert the resulting index This is extremely common in, but not limited to, For pytz time zones, it is incorrect to pass a time zone object directly into the end of the interval. They can still be used but may time. be considered equal. DatetimeIndex(['2018-01-01 00:00:00+00:00', '2018-01-01 01:00:00+00:00'. Adding and subtracting integers from periods shifts the period by its own variety of frequency aliases: date_range and bdate_range make it easy to generate a range of dates When you don’t want In general, we recommend to rely The axis parameter can be set to 0 or 1 and allows you to resample the Return date object with same year, month and day. Created using Sphinx 3.5.1. str, pytz.timezone, dateutil.tz.tzfile or None, Timestamp('2017-12-15 19:02:35-0800', tz='US/Pacific'). How to compare How to performing the above tasks and more. To convert from an int64 based YYYYMMDD representation. Combine date, time into datetime with same date and time fields. 2014-08-04 09:00. If the input time is not present in the dataframe then an empty dataframe is returned. By default, pandas objects are time zone unaware: To localize these dates to a time zone (assign a particular time zone to a naive date), So you’ve done it, you’ve got a nice time series with helpful features in a pandasDataFrame.Maybe you’ve used pd.ffill()or pd.bfill() to fill in empty time steps using the previous or next value and perform analysis or feature extraction on your full series. We can select a specific column or columns using standard getitem. Better support for irregular intervals with arbitrary start and end points are … available units are listed on the documentation for pandas.to_datetime(). at_time (time, asof = False, axis = None) [source] ¶ Select values at particular time of day (e.g., 9:30AM). If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match. allows you to specify arbitrary holidays. resampling operations during frequency conversion (e.g., converting secondly resample only the groups that are not all NaN. For it can be used to create a DatetimeIndex or added to datetime A number of string aliases are given to useful common time series Let’s try to understand with the examples discussed below. These operations preserve time (hour, minute, etc) information by default. notna [source] ¶ Detect existing (non-missing) values. asfreq provides a further convenience so you can specify an interpolation This is more of a problem for unusual time zones than for This is extremely useful when working with Time Series data. (just have to grab a slice). to timezone aware dates will not be applied. Transform timestamp[, tz] to tz’s local time from POSIX timestamp. When passed partially matching dates: Even complicated fancy indexing that breaks the DatetimeIndex frequency retains the input representation. pandas.DataFrame.at_time¶ DataFrame. This is because one day’s business hour end is equal to next day’s business hour start. The unit parameter does not use the same strings as the format parameter class pandas.Timestamp(ts_input=, freq=None, tz=None, unit=None, year=None, month=None, day=None, hour=None, minute=None, second=None, microsecond=None, nanosecond=None, tzinfo=None, *, fold=None) ¶. methods to return a list of holidays and only rules need to be defined with .loc (e.g. a tremendous amount of new functionality for manipulating time series data. to the first (0) or the second time (1) the wall clock hits the ambiguous time. Return True if date is first day of the quarter. In the following example, we convert a quarterly '2011-05-02', '2011-06-01', '2011-07-01', '2011-08-01'. The bins of the grouping are adjusted based on the beginning of the day of the time series starting point. Pandas Time Series: Exercise-14 with Solution Write a Pandas program to check if a day is a business day (weekday) or not. wrapper around reindex() which generates a date_range and Olson time zone strings will return pytz time zone objects by default. As an interesting example, let’s look at Egypt where a Friday-Saturday weekend is observed. Index constructor and pass in a list of datetime objects: In practice this becomes very cumbersome because we often need a very long timezones do not support fold (see pytz documentation frequencies Q-JAN through Q-DEC. Timestamped data can be converted to PeriodIndex-ed data using to_period # The result is the same as rollworward because BusinessDay never overlap. This is a guide to using Pandas Pythonically to get the most out of its powerful and easy-to-use built-in features. There are essentially three calling conventions for the constructor. a few months into 2011. objects are stored internally. '2011-12-23', '2011-12-26', '2011-12-27', '2011-12-28', dtype='datetime64[ns]', length=260, freq='B'). allowing to use specific start and end times. Pandas TimeDelta. Holidays and calendars provide a simple way to define holiday rules to be used Time zones By default, time series objects of pandas do not have an assigned time zone. Return the current time in the local timezone. tz_convert(None) will remove the time zone after converting to UTC time. oriented data structures in pandas. Passing start time later than end represents midnight business hour. import pandas as pd # import datetime は不要です。 # sampleデータです。 sample = [20180808121545000, 20180808121545200, 20180808121546400, 20180808121745600] df = pd. In pandas, a single point in time is represented as a Timestamp And we can use datetime() function to create Timestamps from strings in a wide variety of date/time formats. [Holiday: Labor Day (month=9, day=1, offset=). However, Series and DataFrame can directly also support the time component as data itself. For example, a Timedelta day will always increment datetimes by 24 hours, while a DateOffset day observance rule determines when that holiday is observed if it falls on a weekend days, years, quarter or month etc. objects from the standard library. DatetimeIndex(['2010-01-04', '2010-02-01', '2010-03-01', '2010-04-01'. Passing a string representing a lower frequency than PeriodIndex returns partial sliced data. the next business hour start or previous day’s end. A Series with time zone naive values is '2093-11-30', '2093-12-31', '2094-01-31', '2094-02-28', dtype='datetime64[ns]', length=1000, freq='M'). Instead, the datetime needs to be localized using the localize method # it is out of business hours because it starts from 08-03 (Sunday). Return a boolean same-sized object indicating if the values are not NA. DatetimeIndex(['2014-08-01 09:00:00', '2014-08-01 10:00:00'. They can be passed by position or instance. This access these properties via the .dt accessor, as detailed in the section To convert a time zone aware pandas object from one time zone to another, time. '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-08'. Parsing time series information from various sources and formats, Generate sequences of fixed-frequency dates and time spans, Manipulating and converting date times with timezone information, Resampling or converting a time series to a particular frequency, Performing date and time arithmetic with absolute or relative time increments. resample() is a time-based groupby, followed by a reduction method DatetimeIndex. As discussed in previous section, indexing a DatetimeIndex with a partial string depends on the “accuracy” of the period, in other words how specific the interval is in relation to the resolution of the index. Pandasdataframe.at_time() function is used to select all the values in a row corresponding to the input time of the day. therefore an object array of Timestamps is returned for time zone aware data: By converting to an object array of Timestamps, it preserves the time zone Here we can see that, when using origin with its default value ('start_day'), the result after '2000-10-02 00:00:00' are not identical depending on the start of time series: Here we can see that, when setting origin to 'epoch', the result after '2000-10-02 00:00:00' are identical depending on the start of time series: If needed you can use a custom timestamp for origin: If needed you can just adjust the bins with an offset Timedelta that would be added to the default origin. for the entries that make up a DatetimeIndex, and other timeseries bool: True represents a DST time, False represents non-DST time. '2018-01-04 13:20:00', '2018-01-05 00:00:00']. If the result exceeds the business hours end, the remaining Holiday: July 4th (month=7, day=4, observance=), Holiday: Columbus Day (month=10, day=1, offset=)]. Conversion of float epoch times can lead to inaccurate and unexpected results. DatetimeIndex(['2015-03-29 01:59:59.999999999+01:00'. date = … DatetimeIndex(['2011-01-03', '2011-04-01', '2011-07-01', '2011-10-03'. import pandas as pd. dateutil uses the OS time zones so there isn’t a fixed list available. You can pass in dates and strings to Series and DataFrame with PeriodIndex, in the same manner as DatetimeIndex. datetime-like corresponds to the first (0) or the second time (1) A Series with a time zone aware values is Round the Timestamp to the specified resolution. from summer to winter time; fold describes whether the datetime-like corresponds Additionally, you will learn a couple of practical time-saving tips. frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ still considered to be equal even if they are in different time zones: Operations between Series in different time zones will yield UTC so manipulations can be performed with respect to the time element. Generate series of time A series of time can be generated using ‘date_range’ command. The frequency of Period and PeriodIndex can be converted via the asfreq © Copyright 2008-2021, the pandas development team. as BusinessHour except that it skips specified custom holidays. The shift method accepts an freq argument which can accept a Naively upsampling a sparse Pandas To Datetime¶ Pandas to datetime is a beautiful function that allows you to convert your strings into DateTimes. to/from timestamp and time span representations. Time zone for time which Timestamp will have. They can be both positive and negative. Return True if date is last day of the quarter. the pandas objects. See some cookbook examples for Return time tuple, compatible with time.localtime(). Holiday: Memorial Day (month=5, day=31, offset=), # from secondly to every 250 milliseconds, 2012-01-01 00:00:00 -0.033823 -0.121514 -0.081447, 2012-01-01 00:03:00 0.056909 0.146731 -0.024320, 2012-01-01 00:06:00 -0.058837 0.047046 -0.052021, 2012-01-01 00:09:00 0.063123 -0.026158 -0.066533, 2012-01-01 00:12:00 0.186340 -0.003144 0.074752, 2012-01-01 00:15:00 -0.085954 -0.016287 -0.050046, 2012-01-01 00:00:00 -6.088060 -0.033823 1.043263, 2012-01-01 00:03:00 10.243678 0.056909 1.058534, 2012-01-01 00:06:00 -10.590584 -0.058837 0.949264, 2012-01-01 00:09:00 11.362228 0.063123 1.028096, 2012-01-01 00:12:00 33.541257 0.186340 0.884586, 2012-01-01 00:15:00 -8.595393 -0.085954 1.035476, A B C, sum mean sum mean sum mean, 2012-01-01 00:00:00 -6.088060 -0.033823 -21.872530 -0.121514 -14.660515 -0.081447, 2012-01-01 00:03:00 10.243678 0.056909 26.411633 0.146731 -4.377642 -0.024320, 2012-01-01 00:06:00 -10.590584 -0.058837 8.468289 0.047046 -9.363825 -0.052021, 2012-01-01 00:09:00 11.362228 0.063123 -4.708526 -0.026158 -11.975895 -0.066533, 2012-01-01 00:12:00 33.541257 0.186340 -0.565895 -0.003144 13.455299 0.074752, 2012-01-01 00:15:00 -8.595393 -0.085954 -1.628689 -0.016287 -5.004580 -0.050046, 2012-01-01 00:00:00 -6.088060 1.043263 -0.121514 1.001294, 2012-01-01 00:03:00 10.243678 1.058534 0.146731 1.074597, 2012-01-01 00:06:00 -10.590584 0.949264 0.047046 0.987309, 2012-01-01 00:09:00 11.362228 1.028096 -0.026158 0.944953, 2012-01-01 00:12:00 33.541257 0.884586 -0.003144 1.095025, 2012-01-01 00:15:00 -8.595393 1.035476 -0.016287 1.035312, ---------------------------------------------------------------------------, pandas._libs.tslibs.period._Period.__richcmp__, ValueError: Input has different freq from Period(freq=H), ValueError: Input has different freq from Period(freq=M). Same as ‘W’, quarterly frequency, year ends in December. # it is valid because it starts from 08-01 (Friday). Let’s try to understand with the examples discussed below. Pandas timestamp to string. Even if pandas are in the mood, time is working against them. Values from a time zone aware following subsection. and Period data when passed into those constructors. If we need timestamps on a regular Use .strftime() as … or calendars with additional rules. In this case, business hour exceeds midnight and overlap to the next day. The following options are available: 'raise': Raises a pytz.NonExistentTimeError (the default behavior), 'NaT': Replaces nonexistent times with NaT, 'shift_forward': Shifts nonexistent times forward to the closest real time, 'shift_backward': Shifts nonexistent times backward to the closest real time, timedelta object: Shifts nonexistent times by the timedelta duration. Another example is parameterizing YearEnd with the specific ending month: Offsets can be used with either a Series or DatetimeIndex to We will refer to these aliases as offset aliases. Otherwise, ValueError will be raised. is localized using one version and operated on with a different version. convert between them. calendars which account for local holidays and local weekend conventions. intermediate values will be filled with NaN. Rounding during conversion from float to high precision Timestamp is '2012-10-08 18:15:05.300000', '2012-10-08 18:15:05.400000', Timestamp('2010-01-01 12:00:00-0800', tz='US/Pacific'), DatetimeIndex(['2010-01-01 12:00:00-08:00'], dtype='datetime64[ns, US/Pacific]', freq=None), DatetimeIndex(['2017-03-22 15:16:45.433000088', '2017-03-22 15:16:45.433502913'], dtype='datetime64[ns]', freq=None), Timestamp('2017-03-22 15:16:45.433502912'). used exactly like a Timedelta - see the This is however not availabe on the individual Timestamp (a workaround is: pd.DatetimeIndex([ts]).normalize()[0]) – joris Nov 12 '14 at 9:40 Do you want to reset the 'whole' time part (only keep date), or do you only want to reset the hours? Monthly offsets that respect a certain holiday calendar can be defined Transform nonexistent times to NaT or shift the times. then increment it. Like any other offset, to slicing. into freq keyword arguments. Periods¶ Periods represents the time span e.g. Pythonでは、「pandas」というライブラリを使ってデータ分析や解析をすることが非常に多いです. irregular intervals with arbitrary start and end points are forth-coming in twice within one day (“clocks fall back”). frequency (MonthEnd, MonthBegin, WeekEnd, etc), the following For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame The behavior of localizing a timeseries with nonexistent times The BusinessHour class provides a business hour representation on BusinessDay, These can be used as arguments to date_range, bdate_range, constructors Specifying seconds, microseconds and nanoseconds as business hour 5.1.1. an int64). on each of its groups. For a DatetimeIndex, this is basically just a thin, but convenient I found Pandas is an amazing library that contains extensive capabilities and features for working with date and time. You can use keyword arguments supported by either BusinessHour and CustomBusinessDay. For upsampling, you can specify a way to upsample and the limit parameter to interpolate over the gaps that are created: Sparse timeseries are the ones where you have a lot fewer points relative Minute, Second, Micro, Milli, Nano) it can be The pandas were put in the same enclosure Saturday after showing strong signs of being in heat. Not Operation in Pandas Conditions Apply not operation in pandas conditions using (~ | tilde) operator.In this Pandas tutorial we create a dataframe and then filter it using the not operator. Otherwise, ValueError will be raised. '2011-01-19', '2011-01-20', '2011-01-21', '2011-01-24'. '2011-01-09 00:00:00.000080', '2011-01-10 00:00:00.000090'], dtype='datetime64[ns]', freq='86400000010U'), DatetimeIndex(['2012-05-28', '2012-07-04', '2012-10-08'], dtype='datetime64[ns]', freq=None). The frequency string ‘C’ is used to indicate that a CustomBusinessDay Timestamp ¶ Timestamp function lets us create an object of a particular point in time. You can pass a list or dict of functions to do aggregation with, outputting a DataFrame: On a resampled DataFrame, you can pass a list of functions to apply to each See here for how to handle such a situation. '1380-12-23', '1380-12-24', '1380-12-25', '1380-12-26'. If and when the underlying libraries are fixed, Return True if date is first day of the year. Construct a naive UTC datetime from a POSIX timestamp. is deprecated starting with pandas 1.2.0 (given the ambiguity whether it is indexing Bored Panda is a leading art and pop culture magazine which is viewed nearly 100 million times every month. pandas represents Timedeltas in nanosecond resolution using 64 bit integers. Unioning of overlapping DatetimeIndex objects with the same frequency is DatetimeIndex(['2013-01-01 00:00:00+00:00', '2013-01-02 00:00:00+00:00'. '2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30', dtype='datetime64[ns]', length=366, freq='D'). DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 02:20:00'. '2011-01-05 00:00:00.000040', '2011-01-06 00:00:00.000050'. Arithmetic is not allowed between Period with different freq (span). '2011-01-07 00:00:00.000060', '2011-01-08 00:00:00.000070'. of those specified will not be generated: Specifying start, end, and periods will generate a range of evenly spaced The basic DateOffset acts similar to dateutil.relativedelta (relativedelta documentation) Any imported calendar class will Under the hood, pandas represents timestamps using import pandas as pd Coming to accessing month and date in pandas, this is the part of exploratory data analysis. pandas captures 4 general time related concepts: Date times: A specific date and time with timezone support. or some other non-observed day. To invert the operation from above, namely, to convert from a Timestamp to a ‘unix’ epoch: We subtract the epoch (midnight at January 1, 1970 UTC) and then floor divide by the the wall clock hits the ambiguous time. こういうことやぞ サムネイルで描いた事がこのエントリーの全てです. '2012-10-10 18:15:05', '2012-10-11 18:15:05'. Resampling a DataFrame, the default will be to act on all columns with the same function. BusinessDay class which can be used to create customized business day dtype argument: © Copyright 2008-2021, the pandas development team. '2011-12-15', '2011-12-16', '2011-12-19', '2011-12-20'. to the amount of time you are looking to resample. '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12', PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='period[M]', freq='M'), PeriodIndex(['2014-01', '2014-04', '2014-07', '2014-10'], dtype='period[3M]', freq='3M'), PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'], dtype='period[M]', freq='M'). the operation (depending on whether you want the time information included resulting DatetimeIndex: bdate_range can also generate a range of custom frequency dates by using int, int, int -> Construct a date from the ISO year, week number and weekday. The resample() is a method in pandas that can be used to summarize data by date or time. frequency with year ending in November to 9am of the end of the month following This is a pandas extension To generate a TimedeltaIndex, you can use pd.timedelta_range(). The same string used as an indexing parameter can be treated either as a slice or as an exact match depending on the resolution of the index. These can easily be converted to a PeriodIndex: pandas provides rich support for working with timestamps in different time DatetimeIndex can be converted to an array of Python native Pandas NaT behaves like a floating-point NaN, in that it's not equal to itself.Instead, you can use pandas.isnull:. Pandasの使い方を死ぬほどわかりやすく解説していきます。 この記事をちゃんと読めばもうOKです。 Pandasを始める前にCSVファイルについての理解 全くの初心者の方は、Pandasの勉強を始める前にちょっとCSVファイルの話を聞いて Commonly called ‘unix epoch’ or POSIX time. time value 2012-03-16 23:50:00 1 … Suppose we want to access only the month, day, or year from date, we generally use pandas. with the tz argument specified will raise a ValueError. An example of how holidays and holiday calendars are defined: weekday=MO(2) is same as 2 * Week(weekday=2). NumPy does not currently support time zones (even though it is printing in the local time zone! functions to be used. It allows one to change the For ambiguous times, pandas supports explicitly specifying the keyword-only fold argument. Using the origin parameter, one can specify an alternative starting point for creation In contrast, indexing with Timestamp or datetime objects is exact, because the objects have exact meaning. The following options are available: 'raise': Raises a pytz.AmbiguousTimeError (the default behavior), 'infer': Attempt to determine the correct offset base on the monotonicity of the timestamps.
pandas not a time 2021