Unable to resample between irregular 15 minutes time-stamp 00:14:59 - 00:29:59 in pandas

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I have a dataframe 'df' with datetime index set at 15 minute intervals. The intervals are like this

Index                Data
2015-03-15 00:14:59  36.0
2015-03-15 00:29:59  54.9
2015-03-15 00:44:59  28.7

I want to upsample the above data to minute by minute intervals like below by interpolating the data.

Index                Data
2015-03-15 00:14:59  36.0
2015-03-15 00:15:59  36.5
2015-03-15 00:16:59  43.3
...... so on

However, I have tried to do following:

df = df.resample('T').interpolate(method='spline', order=3)

Also tried :

df = df.resample('60s').interpolate(method='spline', order=3)

However both of the above produce following result. They set the microseconds to 00:00:00 whereas I want it to be set according to the starting timestamp that ends at 59. 00:14:59.

Index                Data
2015-03-15 00:14:00  NaN
2015-03-15 00:15:00  NaN
2015-03-15 00:16:00  NaN
...... so on

How do I upsample according to uniform intervals of 59 microseconds at the end?

Usingreindex with interpolate

df.reindex(pd.date_range(df.index.min(),df.index.max(),freq='T')).interpolate()

Time Series / Date functionality — pandas 0.25.0.dev0+752 , Series(np.random.randn(3), dates) In [15]: type(ts.index) Out[15]: pandas allows you to capture both representations and convert between them Better support for irregular intervals with arbitrary start and end points are forth-coming in future releases A timestamp string less accurate than a minute gives a Series object. from pandas import Series, DataFrame import pandas as pd from datetime import datetime, timedelta import numpy as np def rolling_mean(data, window, min_periods=1, center=False): ''' Function that computes a rolling mean Parameters ----- data : DataFrame or Series If a DataFrame is passed, the rolling_mean is computed for all columns.

Solution from WeNYoBen will generate valeus at needed intervals id the Index column has been indexed. If not, the below formula will generate the values. pd.date_range(df['Index'].iloc[0],df['Index'].iloc[-1],freq='T') this will provide the values as follows['2015-03-15 00:14:59', '2015-03-15 00:15:59', '2015-03-15 00:16:59', '2015-03-15 00:17:59', '2015-03-15 00:18:59', '2015-03-15 00:19:59', '2015-03-15 00:20:59', '2015-03-15 00:21:59', '2015-03-15 00:22:59', '2015-03-15 00:23:59', '2015-03-15 00:24:59', '2015-03-15 00:25:59', '2015-03-15 00:26:59', '2015-03-15 00:27:59', '2015-03-15 00:28:59', '2015-03-15 00:29:59', '2015-03-15 00:30:59', '2015-03-15 00:31:59', '2015-03-15 00:32:59', '2015-03-15 00:33:59', '2015-03-15 00:34:59', '2015-03-15 00:35:59', '2015-03-15 00:36:59', '2015-03-15 00:37:59', '2015-03-15 00:38:59', '2015-03-15 00:39:59', '2015-03-15 00:40:59', '2015-03-15 00:41:59', '2015-03-15 00:42:59', '2015-03-15 00:43:59', '2015-03-15 00:44:59'] But re-indexing is not working for me! May be you can figure it out.

Time Series / Date functionality — pandas 0.23.1 documentation, to 45 minute frequency and forward fill In [5]: converted = ts.asfreq('45Min', 02: 15:00 -1.509059 2011-01-01 03:00:00 -1.135632 Freq: 45T, dtype: float64 Time-stamped data is the most basic type of timeseries data that associates values pandas allows you to capture both representations and convert between them. I have a timeseries data where I am using resample technique to downsample my data from 15 minute to 1 hour. The data is quite large ( values every 15 minutes for 1 year) so there are more than 30k rows in my original csv file. I am using: df[‘dt’] = pd.to_datetime(df[‘Date’] + ‘ ‘ + df[‘Time’])

Try:

df = df.asfreq('60s').interpolate()

Time Series / Date functionality — pandas 0.16.2 documentation, kind can be set to 'timestamp' or 'period' to convert the resulting index to/from time -stamp and time-span representations. By default resample retains the input� Specify a 30 minute time step. Since 30 minutes is not a predefined time step, you must specify it as a duration value, using the 'TimeStep' name-value pair argument. Resample the data from TT1 using linear interpolation.

Time Series / Date functionality — pandas 0.18.1 documentation, kind can be set to 'timestamp' or 'period' to convert the resulting index to/from time -stamp and time-span representations. By default resample retains the input� Specifically, trades executed between midnight and 8:00 a.m. must be reported by 8:15 a.m. Eastern Time on trade date. Trades executed between the close of the Facility (6:30 p.m. for the ADF and 8:00 p.m. for the TRFs and the ORF) and midnight must be reported on an "as/of" basis by 8:15 a.m. Eastern Time the following business day.

Time Series / Date functionality — pandas 0.17.0 documentation, mobiw.ru 2009-2020. Сайт Позитива и Хорошего Настроения! Афоризмы, цитаты, высказывания великих людей

If the woman is given a 500 ml whole blood transfusion over a 15 minute period, her ventricular preload will increase. One minute after the transfusion is complete, what will the new stroke volume be assuming her heart is normal? A. 70 ml B. 80 ml C. 90 ml D. 100 ml E. 120 ml

Comments
  • No it doesn't work. Still producing NaN. There is data available only at microsecond date 00:14:59 NOT 00:14:00. so it will produce Nan. Please read the question properly. I want it to be at intervals of 00:14:59, 00:15:59 @WeNYoBen