## How to calculate the median for a specific hour from a time series

aggregate time series data pandas

time average function r

subset data by time in r

pandas time series

how to summarize time series data

aggregate hourly data to daily in r

plot time series python

I have a df with information how many transfers have been made in the next 10 minutes. I would like to show which banks are the most popular at specific hours (I conclude that I will be able to show this thanks to the median). My pivot is like that:

bank_name bank1 bank2 bank3 bank4 date 2019-11-03 102 105 78 81 00:00 2019-11-03 108 100 103 77 00:10 2019-11-03 00:20 108 134 55 27 ... ... ... ... ... 2019-12-22 15:30 461 312 312 253 2019-12-22 15:40 396 361 376 229

Or regular df

date bank_name transfers 2019-11-03 00:00 bank1 102 2019-11-03 00:00 bank2 105 2019-11-03 00:00 bank3 78 2019-11-03 00:00 bank4 81 2019-11-03 00:10 bank1 108 2019-11-03 00:10 bank2 100 ... ... ...

My expected out (I entered the median values at random)

hour bank_name median 00 bank2 641 01 bank2 711 02 bank1 668 ... ... ... 23 bank3 757

At the beginning I I would like to sum the values from `2019-11-03 00:00, 00:10, 00:20, 00:30, 00:40, 00:50`

and take it as a value `03 00`

. I did it like this:

`df['date_'] = pd.to_datetime(df['date'].dt.strftime('%d %H'))`

`df = df.set_index('bank_name').groupby([ 'bank_name', 'date_']).agg({'transfers':np.sum})`

... but I don't know what to do next. I will be grateful for your help.

Here's how I would do it.

import pandas as pd df.groupby([pd.to_datetime(df['date']).dt.hour, 'bank_name'])['transfers'].median()

**Function to calculate time averages for data frames — timeAverage ,** timeAverage( mydata, avg.time = "day", data.thresh = 0, statistic = "mean", type This is useful, for example, for calculating a 15-minute time series from an hourly the time interval of the original series is an exact multiple of avg.time e.g. hour to 10 A value of zero means that all available data will be used in a particular� The median is the exact middle number in a sequence or set of numbers. When you're looking for the median in a sequence that has an odd amount of total numbers, the process is really easy. Finding the median in a sequence that has an even amount of total numbers is a bit harder. To find the median easily and successfully, read on.

Try this:

# median hourly transfer hourly_transfers = df.groupby([pd.Grouper(key='date', freq='H'), 'bank_name']).median() # which bank has the highest median in each hour idx = hourly_transfers.groupby('date')['transfers'].idxmax() # the result hourly_transfers.loc[idx]

**Working with Time Series,** Time intervals and periods reference a length of time between a particular While the time series tools provided by Pandas tend to be the most useful for data science For example, for a frequency of 2 hours 30 minutes, we can combine the hour ( H ) For example, we use shifted values to compute the one-year return on� tsmedian = median(ts,Name,Value) specifies additional options when computing the median using one or more name-value pair arguments.For example, tsmedian = median(ts,'Quality',-99,'MissingData','remove') defines -99 as the missing sample quality code, and removes the missing samples before computing the median.

**Time Series 04: Subset and Manipulate Time Series Data with dplyr ,** This subset was created in the Subsetting Time Series Data tutorial. Calculate the mean precipitation value for each group (ie for each year). filter() & slice() : filter rows based on values in specified columns; group-by() : group all data by a column; arrange() 24*4 # 24 hours/day * 4 15-min data points/hour ## [1] 96. The whole of GCSE 9-1 Maths in only 2 hours!! Higher and Foundation Revision for Edexcel, AQA or OCR - Duration: 2:06:55. Science and Maths by Primrose Kitten 1,154,550 views

**Resample or Summarize Time Series Data in Python With Pandas ,** Resample time series data from hourly to daily, monthly, or yearly using pandas. Note that if there is no precipitation recorded in a particular hour, then df. resample('D').sum() calculate a mean, minimum or maximum value,� Time series calculations assume that you have Dynamic Time Series members defined in the outline. Calculating time series data is helpful in tracking inventory by calculating the first and last values for a time period, and in calculating period-to-date values.

**Manipulating Time Series Data in R with xts & zoo,** 1. Introduction to eXtensible Time Series, using xts and zoo for time series It is best to think of xts objects as normal R matrices, but with special powers. In this exercise, you will extract recurring morning hours from the time series irreg, which holds irregular Using period.apply(), calculate the weekly mean of the Temp. Example: find the Median of 12, 3 and 5. Put them in order: 3, 5, 12. The middle is 5, so the median is 5.

**Averages: Mean, Median and Mode,** Introduction to averages and an average calculator. sense, it usually refers to the mean, especially when no other information is given. minutes – time does not work on the decimal system as there are 60 minutes in an hour and not 100. I'm having trouble when trying to calculate the average temperature by hour. I have a data frame with date, time (hh:mm:ss p.m./a.m.)and temperature. What I need is to extract the mean temperature by hour in order to plot daily variation of temperature.

##### Comments

- I don't see how your expected results relate to the median. The median across what? The median number of transfers across each 10 minutes interval in each hour?
- I want to normalize df to hours (add transfer value from minutes to full hour, keeping days), and then calculate the median for each hour for each bank (I have 50 days so I have 50 observations about the amount of transfers from 15:00). The results that I published in the OP show which bank has the largest median at 01:00, 02:00 etc.
- I guess it will be better to convert "date" type before. As alternative you could use
`df["date"].astype("M8[us]").dt.hour`

.