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How to efficiently disaggregate data from?


How to efficiently disaggregate data from?

By : Sajjad Aslam
Date : October 18 2020, 06:10 PM
this will help IIUC using repeat create you dfs , then we adjust the two column by cumcount with np.where
code :
df=df.reindex(df.index.repeat(df.users))
df=df.assign(users=1)
df.goal_completions=np.where(df.groupby(level=0).cumcount()<df.goal_completions,1,0)
df
Out[609]: 
       date  users  goal_completions
0  20150101      1                 1
0  20150101      1                 0
1  20150102      1                 1
1  20150102      1                 1
1  20150102      1                 0


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How can I efficiently disaggregate data in a Dataframe (given a set of weights, mapping, etc.)?

How can I efficiently disaggregate data in a Dataframe (given a set of weights, mapping, etc.)?


By : lian flea
Date : March 29 2020, 07:55 AM
This might help you Assuming two dataframes, df_states and df_regional, with the following structure:
code :
In [36]: df_states
Out[36]: 
          Weight    Region
Alabama     0.25  region_1
Arizona     0.75  region_1
Arkansas    0.33  region_2

In [37]: df_regional
Out[37]: 
          Value
region_1    100
region_2     80
In [39]: df = pandas.merge(df_states, df_regional, left_on='Region', right_index=True)

In [40]: df
Out[40]: 
          Weight    Region  Value
Alabama     0.25  region_1    100
Arizona     0.75  region_1    100
Arkansas    0.33  region_2     80

In [41]: df.Weight * df.Value
Out[41]: 
Alabama     25.0
Arizona     75.0
Arkansas    26.4
Using 'disaggregate' with GCM data

Using 'disaggregate' with GCM data


By : Riddler
Date : March 29 2020, 07:55 AM
wish help you to fix your issue The code below should help- it uses aggregate to the closest integer scaling possible then resample to match the other raster's spatial characteristics exactly:
code :
r = raster(ncols=720, nrows=360) #fine resolution grid
r[] = runif(1:100)

s = raster(ncols=192, nrows=145) #dimensions of one of the GCM
s[] = runif(1:10)

d=disaggregate(s, fact=c(round(dim(r)[1]/dim(s)[1]),round(dim(r)[2]/dim(s)[2])), method='') #fact equals r/s for cols and rows
e=resample(d, r,  method="ngb")
Disaggregate one row of data to multiple rows

Disaggregate one row of data to multiple rows


By : user3803277
Date : March 29 2020, 07:55 AM
it should still fix some issue Goodafternoon! , Try
code :
library(data.table)
setDT(df1)[, list(Clicked=rep(c(1,0), c(Clicks, Impressions-Clicks)),
 Converted=rep(c(1,0), c(Conversions, Impressions-Conversions))) , Keyword]
#       Keyword Clicked Converted
# 1: SampleName       1         1
# 2: SampleName       1         1
# 3: SampleName       1         0
# 4: SampleName       1         0
# 5: SampleName       1         0
# 6: SampleName       0         0
# 7: SampleName       0         0
# 8: SampleName       0         0
# 9: SampleName       0         0
#10: SampleName       0         0
setDT(df1)[, list(Clicked=rep(c(1,0), c(Clicks, Impressions-Clicks)), 
 Converted=rep(c(1,0), c(Conversions, Impressions-Conversions)), 
 CPC=rep(c(CostPerClick, 0), c(Clicks,Impressions-Clicks))), Keyword]
#    Keyword Clicked Converted  CPC
# 1: Sample1       1         1 0.26
# 2: Sample1       1         1 0.26
# 3: Sample1       1         0 0.26
# 4: Sample1       1         0 0.26
# 5: Sample1       1         0 0.26
# 6: Sample1       0         0 0.00
# 7: Sample1       0         0 0.00
# 8: Sample1       0         0 0.00
# 9: Sample1       0         0 0.00
#10: Sample1       0         0 0.00
#11: Sample2       1         1 0.15
#12: Sample2       1         0 0.15
#13: Sample2       1         0 0.15
#14: Sample2       0         0 0.00
#15: Sample2       0         0 0.00
#16: Sample2       0         0 0.00
 df1 <- structure(list(Keyword = "SampleName", Impressions = 10L, 
 Clicks = 5L, 
 Conversions = 2L), .Names = c("Keyword", "Impressions", "Clicks", 
 "Conversions"), class = "data.frame", row.names = c(NA, -1L))
Disaggregate quarterly data to daily data in R keeping values?

Disaggregate quarterly data to daily data in R keeping values?


By : user3099505
Date : March 29 2020, 07:55 AM
I wish this help you Here is a tidyr and zoo package answer that uses 'last observation carried forward' after inserting a sequence of dates with NA:
code :
library(tidyverse)
library(zoo)

data %>%
  complete(thedate = seq.Date(min(thedate), max(thedate), by="day")) %>%
  do(na.locf(.))
library(tidyverse)

data %>%
  complete(thedate = seq.Date(min(thedate), max(thedate), by="day")) %>%
  fill(gdp)
How can I use the `td` command from the `tempdisagg` package to disaggregate monthly data into daily data frequency?

How can I use the `td` command from the `tempdisagg` package to disaggregate monthly data into daily data frequency?


By : a7berry
Date : March 29 2020, 07:55 AM
Hope this helps It looks like the tempdisagg package doesn't allow for monthly to daily disaggregation. From the td() help file 'to' argument:
code :
library(quantmod)
library(xts)
library(zoo)
library(tidyverse)
library(lubridate)

# Get price data to use as an example
getSymbols('MSFT')

#This data has more information than we want, remove unwanted columns:
msft <- Ad(MSFT) 

#Add new column that acts as an 'indexed price' rather than 
# actual price data.  This is to show that calculated returns
# don't depend on real prices, data indexed to a value is fine.
msft$indexed <- scale(msft$MSFT.Adjusted, center = FALSE)

#split into two datasets  
msft2 <- msft$indexed
msft$indexed <- NULL


#msft contains only closing data, msft2 only contains scaled data (not actual prices)
#  move from daily data to monthly, to replicate the question's situation.
a <- monthlyReturn(msft)
b <- monthlyReturn(msft2)

#prove returns based on rescaled(indexed) data and price data is the same:
all.equal(a,b)

# subset to a single year
a <- a['2019']
b <- b['2019']

#add column with days in each month
a$dim <- days_in_month(a) 
a$day_avg <- a$monthly.returns / a$dim  ## <- This must've been left out

day_avgs <- data.frame(day_avg = rep(a$day_avg, a$dim))


# daily averages timesereis from monthly returns.
z <- zoo(day_avgs$day_avg, 
         seq(from = as.Date("2019-01-01"), 
             to = as.Date("2019-12-31"), 
             by = 1)) %>%
  as.xts()

#chart showing they are the same:
PerformanceAnalytics::charts.PerformanceSummary(cbind(a$monthly.returns, z))

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