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

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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.

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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.