resampling

问题 I have a question regarding the resampling method of pandas Dataframes. I have a DataFrame with one observation per day: import pandas as pd import numpy as np df = pd.DataFrame(np.random.randint(0,100,size=(366, 1)), columns=list('A')) df.index = pd.date_range(datetime.date(2016,1,1),datetime.date(2016,12,31)) if I want to compute the sum (or other) for every month, I can directly do: EOM_sum = df.resample(rule="M").sum() however I have a specific calendar (irregular frequency): import

问题 Please excuse obvious errors - still in the learning process. I am trying to do a simple timeseries plot on my data with a frequency of 15 minutes. The idea is to plot monthly means, starting with resampling data every hour - including only those hourly means that have atleast 1 observation in the interval. There are subsequent conditions for daily and monthly means. This is relatively simpler only if this error does not crop up- "None of [DatetimeIndex(['2016-01-01 05:00:00', '2016-01-01 05

问题 searched for it but found no solution - if there is already one sry for asking but i would be thankful for a link I have a dataframe (df) like this: timestamp value 2016-03-11 07:37:40 24.6018 2016-03-11 07:37:45 24.6075 2016-03-11 07:37:50 24.599 2016-03-11 07:37:55 24.6047 2016-03-11 07:38:00 24.5905 2016-03-11 07:38:05 24.551 ... important start not at a even minute like 07:40:00 but 07:37:40 (could be any time) and i want to resample it - calculate mean values over e.g. 5 minutes labeled

问题 searched for it but found no solution - if there is already one sry for asking but i would be thankful for a link I have a dataframe (df) like this: timestamp value 2016-03-11 07:37:40 24.6018 2016-03-11 07:37:45 24.6075 2016-03-11 07:37:50 24.599 2016-03-11 07:37:55 24.6047 2016-03-11 07:38:00 24.5905 2016-03-11 07:38:05 24.551 ... important start not at a even minute like 07:40:00 but 07:37:40 (could be any time) and i want to resample it - calculate mean values over e.g. 5 minutes labeled

问题 I always thought set.seed() only makes random variable generators (e.g., rnorm ) to generate a unique sequence for any specific set of input values. However, I'm wondering, why when we set the set.seed() , then the function sample() doesn't do its job correctly? Question Specifically, given the below example, is there a way I can use set.seed before the rnorm but sample would still produce new random samples from this rnorm if sample is run multiple times? Here is an R code: set.seed(123458)

问题 I always thought set.seed() only makes random variable generators (e.g., rnorm ) to generate a unique sequence for any specific set of input values. However, I'm wondering, why when we set the set.seed() , then the function sample() doesn't do its job correctly? Question Specifically, given the below example, is there a way I can use set.seed before the rnorm but sample would still produce new random samples from this rnorm if sample is run multiple times? Here is an R code: set.seed(123458)

问题 I have a dataframe of the classic "open high low close volume" data type, so common in finance. With each row being 1 minute. 720 rows. I gather it with this code from Kraken: import urllib.request, json with urllib.request.urlopen("https://api.kraken.com/0/public/OHLC?pair=XXBTZEUR&interval=1") as url: data = json.loads(url.read().decode()) columns=['time', 'open', 'high', 'low', 'close', 'vwap', 'volume', 'ount'] data_DF=pd.DataFrame(data['result']['XXBTZEUR'],columns=columns) data_DF['open

问题 In this SO Question bootstrapping by several groups and subgroups seemed to be easy using the broom::bootstrap function specifying the by_group argument with TRUE . My desired output is a nested tibble with n rows where the data column contains the bootstrapped data generated by each bootstrap call (and each group and subgroup has the same amount of cases as in the original data). In broom I did the following: # packages library(dplyr) library(purrr) library(tidyr) library(tibble) library

问题 I am developing an application where I am using a wave file from a location at one end of a pipeline and udpsink at the other end of it. gst-launch-1.0 filesrc location=/path/to/wave/file/Tornado.wav ! wavparse ! audioconvert ! audio/x-raw,channels=1,depth=16,width=16,rate=44100 ! rtpL16pay ! udpsink host=xxx.xxx.xxx.xxx port=5000 The Above wave file is having sampling rate = 44100 Hz and single-channel(mono) On the same PC I am using a c++ program application to catch these packets and

问题 I am using griddata to resample a numpy 2 dimensional array on a grid. z.shape = (1000, 1000) x, y = np.arange(-5, 5, 0.01), np.arange(-5, 5, 0.01) newx, newy = np.arange(-2, 2, 0.1), np.arange(-2, 2, 0.1) griddata((x, y), z, (newx[None, :], newy[:, None])) The code should: resample z (which represents an image) to a new coarser or finer grid the new grid does not necessarily cover all of the original one. However griddata cannot manage a regular input grid. Does anyone know an easy