Date / Time discrete note (DS)

1from datetime import datetime, timedelta
2from isodate import duration_isoformat # used only for datetime.timedelta
3
4def get_isoformat(time):
5    """
6    Convert pd.Timedelta, pd.Timestamp,
7    datetimme.datetime, datetime.time, datetime.date, datetime.timedelta
8    to isoformat
9    """
10    if not isinstance(time, timedelta):
11        return time.isoformat()
12    else:
13        return duration_isoformat(time)

1# numpy.timedelta64(208206000000000,'ns') → Timedelta('2 days 09:50:06')
2pd.Timedelta(time, unit='ns')

1# DateOffsets ('14T') → Timedelta('0 days 00:14:00')
2pd.to_timedelta('14T')

1# Can't use 'T' as '1T'?
2from pandas.tseries.frequencies import to_offset
3pd.to_timedelta(to_offset('T'))

1# Timedelta('0 days 00:01:20') -> 80 (s)
2# (SINGLE VALUE)
3td.total_seconds() # float

1# Timedelta('0 days 00:01:20') -> 80 (s) (FLOAT)
2# (ONLY WORK with a series, not a single value)
3series.astype('timedelta64[s]') # or 'ms'

1# '1 minutes' -> '1T'
2def timedelta_to_string(timedelta):
3    units = ['D', 'H', 'T', 'S', 'L', 'U', 'N']
4    time_format = ''
5    for i, c in enumerate(timedelta.components):
6        if c != 0: time_format += str(c) + units[i]
7    return time_format

1## EXAMPLE
2import pandas as pd
3test = pd.Timedelta('1 minutes')
4timedelta_to_string(test)

1np.subtract(df[1:], df[:-1])
2
3# convert to hour
4np.subtract(df[1:], df[:-1]) / pd.Timedelta('1 hour')

1import pandas as pd
2pd.to_timedelta('150S') > pd.to_timedelta('1T')
3pd.to_timedelta('120S') == pd.to_timedelta('1T')
4pd.to_timedelta('120S') == pd.to_timedelta('2T')

1from datetime import datetime

1# to same timezone (UTC, +0)
2df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True, infer_datetime_format=True, cache=True)

1# UTC+0 to UNIX timestamp (POSIX timestamp)
2df['timestamp'] = df['timestamp'].apply(lambda x: int(datetime.timestamp(x)*1000)) # miliseconds
3
4# unix timestamp to Timestamps
5datetime.fromtimestamp(unix_ts//1000)

1# UNIX float (ms) -> datetime64
2df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
3# change `ms` with others, e.g. `ns` for nanosecond

1# remove timezone
2df['time'].dt.tz_localize(None)

1def set_index(data, col_time):
2    """
3    Make a copy of a time-series dataframe `df` and set the column-time be the
4    index of the dataframe.
5    In the case index has no name, we set it as `'index'`.
6    """
7    df0 = data.copy()
8    if col_time != 'index': # col_time is not the index
9        df0 = df0.set_index(col_time)
10    else:
11        if df0.index.name is None:
12            df0.index.name = 'index'
13    return df0

1def get_info_timestamps(df, col_date='index'):
2    # make sure timestamps are on index
3    df = set_index(df, col_date)
4    index_name = df.index.name
5    df = df.reset_index()
6    print('Time range: ', df[index_name].max() - df[index_name].min())
7    print('Number of different time steps: ', df[index_name].diff().value_counts().count())
8    print('Max time step: ', df[index_name].diff().max())
9    print('Min time step: ', df[index_name].diff().min())
10    print('The most popular time step: ', df[index_name].diff().value_counts().index[0])
11    print('timestamps are monotonic increasing? ', df[index_name].is_monotonic)
12    print('Are there duplicate timestamps? ', df[index_name].duplicated().any())
13    print('How many unique duplicates? ', df[index_name].duplicated().sum(), ' (in total ',df.shape[0], ')')
14    print('How many repeated duplicates? ', df[index_name].duplicated(keep=False).sum(), ' (in total ',df.shape[0], ')')

1# CHECK
2df.date.is_monotonic # monotonic increasing?
3df.date.is_monotonic_decreasing # decreasing?
4
5# if using groupby
6def check_monotonic(group):
7    return group.is_monotonic
8df.groupby('label').agg({'timestamp': [check_monotonic] })

1# ARRANGE THEM
2df.sort_values(by='date', inplace=True)

1# count the number of elements for each time steps
2df.date.diff().value_counts()
3
4# count number of different time steps
5df.date.diff().value_counts().count()
6
7# take the index of the largest
8df.date.diff().value_counts().index[0]
9
10# take the index of the smallest
11df.date.diff().value_counts().index[-1]