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Grouping by two values


Grouping by two values

By : user2173117
Date : October 21 2020, 08:10 PM
seems to work fine It's the same thing, just add p.carId to the grouping separated by a comma:
code :
GROUP BY gasStationId, p.carId
SELECT g.name, p.carId, SUM(totalPrice) AS totalCosts
FROM purchase AS p
JOIN gas_station AS g ON g.id = p.id
GROUP BY gasStationId, p.carId


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grouping table elements according to grouping column values

grouping table elements according to grouping column values


By : Bill Anderson
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have this table, with number of photon counts of an event in each energy channel. , Give this a try,
code :
chan = np.array( [0,1,2,3,4,5,6,7,8,9] )
counts = np.array( [0.,0.,5.,2.,0.,0.,1.,1.,1.,0.] )
groups = np.array( [1,-1,-1,-1,-1,1,-1,-1,-1,-1] )

indx = np.where( groups==1 )
# indx is a tuple with one entry for each dimension of the array groups
# in the next statement I just grab the first (and only) element of the tuple 
indx = indx[0]  

# next we split the array based on those indices
counts = np.split( counts, indx )
# counts is now a list of arrays 
# [array([], dtype=float64), array([ 0.,  0.,  5.,  0.,  0.]),array([ 0.,  1.,  1.,  1.,  0.])]
# I use the if statement in the list comprehension to get rid of the first empty array

totals = np.array( [sum(c) for c in counts if len(c)>0] )
tchnls = np.split( chan, indx )[1:]
>>> totals
array([ 7.,  3.])
>>> tchnls
[array([0, 1, 2, 3, 4]), array([5, 6, 7, 8, 9])]
How to find highest values in multiple columns by grouping the row values using python?

How to find highest values in multiple columns by grouping the row values using python?


By : msgoon6
Date : March 29 2020, 07:55 AM
I hope this helps you . I am working on a dataset that contains three columns; roadType (string), cars and buses (integer values). , try this:
code :
In [31]: d = pd.DataFrame(data, columns=['roadType','cars','buses'])

In [32]: d
Out[32]:
  roadType  cars  buses
0        A     5      6
1        B     7      3
2        C     9      6
3        B     2      8
4        A     4      8
5        C     8      1
6        B     1      0

In [33]: d.groupby('roadType').max().reset_index()
Out[33]:
  roadType  cars  buses
0        A     5      8
1        B     7      8
2        C     9      6
Find the average values by grouping and sub-grouping variables, and count of the number of times a value occurs within t

Find the average values by grouping and sub-grouping variables, and count of the number of times a value occurs within t


By : Rameshwary
Date : March 29 2020, 07:55 AM
With these it helps I have a dataset which has four columns of data. , We don't need to reshape it to 'long' format to get the mean values
code :
library(dplyr)
df %>% 
   group_by(Grouping) %>% #first grouping
   #create the mean column and the count by 'Grouping'
   mutate(AvgPrice = mean(Price), n1 = n()) %>% 
   group_by(Sub_grouping, add= TRUE) %>% #second grouping
   #summarise to get the mean within Sub_grouping and count the values with n()
   summarise(AvgPrice = first(AvgPrice), n1 = first(n1), AvgPrice2 = mean(Price), n2 = n())
df
  %>%
   group_by(Grouping, SP) %>%
   ...
   ...
library(data.table)
dcast(setDT(df), Grouping + Sub_grouping ~ SP, value.var = "Price", c(mean, length))
From a Pandas Dataframe, return specific column values based on grouping and largest values of other columns

From a Pandas Dataframe, return specific column values based on grouping and largest values of other columns


By : نهجي علي الكرار
Date : March 29 2020, 07:55 AM
hope this fix your issue IIUC, you can sort_values, then apply head to your groupby object
code :
df_new = df.sort_values(['Class', 'Grade'], ascending=[True, False]).groupby('Class').head(2)
  Class  Grade Student
0      A     96    Eric
1      A     92   Caden
4      B     80   Laura
5      B     22   Leann
7      C     90    Jack
8      C     87    Jill
10     D     48  Andrew
15     E     93   Sarah
16     E     92    Brit
df_new[['Student']]

   Student
0     Eric
1    Caden
4    Laura
5    Leann
7     Jack
8     Jill
10  Andrew
15   Sarah
16    Brit
Replicate values across a grouping set with multiple groups within the grouping set by date

Replicate values across a grouping set with multiple groups within the grouping set by date


By : user3257125
Date : March 29 2020, 07:55 AM
To fix this issue Without ROWS your current query returns the Max over all rows, i.e. a Group Max.
You probably want
code :
LAST_VALUE(GR_MATERIAL IGNORE NULLS) 
over (partition by A_ID, B_ID, C_ID 
      order by LAST_DT DESC, DATE_TIME DESC)
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