Python date series generation

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In python, given a start date:Nov 2 ,2018 (Friday), I need to generate a dataframe with a series of weekly dates and tag them as below: Basically each date pair goes from one friday to next thursday and spans 1 week

START_DT    END_DT        WEEK_IND  MONTH_IND(same month as END_DT)
2018-11-01  2018-11-08     1         201811
2018-11-09  2018-11-15     2         201811
2018-11-16  2018-11-22     3         201811
2018-11-23  2018-11-29     4         201811
2018-11-30  2018-12-06     1         201812( WEEK_IND resets as it is a new month)
...
...

Can someone point me in the right direction?


You could do something like this:

import pandas as pd

start = pd.date_range(start='20181102', end='20181231', freq='1w-FRI')
end = start + pd.DateOffset(6)

df = pd.DataFrame({ 'start': start, 'end': end, 'month': end.strftime('%Y%m')}, columns=['start', 'end', 'month'])
df['week'] = df.groupby('month').cumcount() + 1

print(df)

Output

       start        end   month  week
0 2018-11-02 2018-11-08  201811     1
1 2018-11-09 2018-11-15  201811     2
2 2018-11-16 2018-11-22  201811     3
3 2018-11-23 2018-11-29  201811     4
4 2018-11-30 2018-12-06  201812     1
5 2018-12-07 2018-12-13  201812     2
6 2018-12-14 2018-12-20  201812     3
7 2018-12-21 2018-12-27  201812     4
8 2018-12-28 2019-01-03  201901     1

Generating all dates within a given range in python, Pandas is great for time series in general, and has direct support both for date ranges and date parsing (it's automagic). import pandas as pd  type(date_rng) pandas.core.indexes.datetimes.DatetimeIndex. Create a dataframe and add random values for the corresponding date. df = pd.DataFrame(date_rng, columns=['date']) df['data'] = np.random.randint(0,100,size=(len(date_rng))) You have your self-generated time-series data. Hope this one helps.


df = pd.DataFrame()
df['START_DT'] = pd.date_range(start='2018-11-02', freq = 'W-FRI', periods = 10)
df['END_DT'] = df['START_DT']+ pd.DateOffset(6)
df['MONTH_IND'] = df['END_DT'].dt.strftime('%Y%m')
df['WEEK_IND'] = df.groupby(by = 'MONTH_IND').cumcount()+1

Output

    START_DT    END_DT  MONTH_IND   WEEK_IND
0   2018-11-02  2018-11-08  201811  1
1   2018-11-09  2018-11-15  201811  2
2   2018-11-16  2018-11-22  201811  3
3   2018-11-23  2018-11-29  201811  4
4   2018-11-30  2018-12-06  201812  1
5   2018-12-07  2018-12-13  201812  2
6   2018-12-14  2018-12-20  201812  3
7   2018-12-21  2018-12-27  201812  4
8   2018-12-28  2019-01-03  201901  1
9   2019-01-04  2019-01-10  201901  2

pandas.date_range, startstr or datetime-like, optional Right bound for generating dates. periodsint, optional Normalize start/end dates to midnight before generating date range. 6 Ways to Plot Your Time Series Data with Python. Time series lends itself naturally to visualization. Line plots of observations over time are popular, but there is a suite of other plots that you can use to learn more about your problem. The more you learn about your data, the more likely you are to develop a better forecasting model.


Use pd.tseries

It has the Week offset for generating weekly data that accepts a weekday parameter and results in the generated dates always lying on a particular day of the week -

from pandas.tseries.offsets import *
pd.to_datetime(df['START_DT']) + Week(weekday=3)

Output

0   2018-11-08
1   2018-11-15
2   2018-11-22
3   2018-11-29
4   2018-12-06

Time series / date functionality, In addition to the required datetime string, a format argument can be passed to date_range and bdate_range make it easy to generate a range of dates using  A time series must be transformed into samples with input and output components. The transform both informs what the model will learn and how you intend to use the model in the future when making predictions, e.g. what is required to make a prediction (X) and what prediction is made (y).


Python Exercise: Get a list of dates between two dates, Python Exercises, Practice and Solution: Write a Python program to get a list of dates between two dates. from datetime import timedelta, date def daterange(​date1, date2): for n in range(int 4, for n in range(int ((date2 - date1).days)+1): Next: Write a Python program to generate RFC 3339 timestamp. start str or datetime-like, optional. Left bound for generating dates. end str or datetime-like, optional. Right bound for generating dates. periods int, optional. Number of periods to generate. freq str or DateOffset, default ‘D’ Frequency strings can have multiples, e.g. ‘5H’. See here for a list of frequency aliases. tz str or tzinfo, optional


Python generator to construct range of dates · GitHub, date-range.py. from datetime import datetime, timedelta. def date_range(start, end, step=7, date_format="%m-%d-%Y"):. """ Creates generator with a range of  I have a Pandas DataFrame as below . ReviewID ID Type TimeReviewed 205 76032930 51936827 ReportID 2015-01-15 00:05:27.513000 232 76032930 51936854 ReportID 2015-01-15 00:06:46.703000 233 76032930 51936855 ReportID 2015-01-15 00:06:56.707000 413 76032930 51937035 ReportID 2015-01-15 00:14:24.957000 565 76032930 51937188 ReportID 2015-01-15 00:23:07.220000 >>> type(df) <class 'pandas.core.frame


Working with Time Series, While the time series tools provided by Pandas tend to be the most useful for data For example, you can manually build a date using the datetime type: To make the creation of regular date sequences more convenient, Pandas offers a few  The datetime module supplies classes for manipulating dates and times in both simple and complex ways. While date and time arithmetic is supported, the focus of the implementation is on efficient attribute extraction for output formatting and manipulation. For related functionality, see also the time and calendar modules.