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How to assign regular strings for quarterly and monthly observation labels to the row names of a data frame?


How to assign regular strings for quarterly and monthly observation labels to the row names of a data frame?

By : Denis L Presciliano
Date : November 20 2020, 01:01 AM
hope this fix your issue [added since 150 chs restriction in the title]: "without having to enter all the dates of relevant data?" , Based on what I understand from your question, you could do:
code :
library(lubridate)
row.names(df) <- as.yearqtr(seq(ymd('1965-01-01'), by = '1 quarter', length.out=(50)))


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How to select data by monthly and weekly and quarterly

How to select data by monthly and weekly and quarterly


By : Bayya
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I have the an Order model in the following , You want something along the lines:
code :
date = DateTime.now.utc
Order.where('created_at >= ? and created_at <= ?', date.beginning_of_month, date.utc.end_of_month).sum('price')
how to convert monthly data to quarterly in pandas

how to convert monthly data to quarterly in pandas


By : Fabiano Brites
Date : March 29 2020, 07:55 AM
this will help you can use pd.PeriodIndex(..., freq='Q') in conjunction with groupby(..., axis=1):
code :
In [63]: df
Out[63]:
   1996-04  1996-05  2000-07  2000-08  2010-10  2010-11  2010-12
0        1        2        3        4        1        1        1
1       25       19       37       40        1        2        3
2       10       20       30       40        4        4        5

In [64]: df.groupby(pd.PeriodIndex(df.columns, freq='Q'), axis=1).mean()
Out[64]:
   1996Q2  2000Q3    2010Q4
0     1.5     3.5  1.000000
1    22.0    38.5  2.000000
2    15.0    35.0  4.333333
In [66]: res = (df.groupby(pd.PeriodIndex(df.columns, freq='Q'), axis=1)
                  .mean()
                  .rename(columns=lambda c: str(c).lower()))

In [67]: res
Out[67]:
   1996q2  2000q3    2010q4
0     1.5     3.5  1.000000
1    22.0    38.5  2.000000
2    15.0    35.0  4.333333

In [68]: res.columns.dtype
Out[68]: dtype('O')
Monthly lags as quarterly data

Monthly lags as quarterly data


By : B Elishama Lakra
Date : March 29 2020, 07:55 AM
This might help you Here is what I understood: you want to be able to perform aggregate functions on each month in a quarter and you want to compare the first, second, and third month in each quarter. In this case you should use something like the below:
code :
library(lubridate)
library(tidyverse)

dates = tibble(dt = seq.Date(from = as.Date("2016-01-01", "%Y-%m-%d"), to = as.Date("2017-02-01", "%Y-%m-%d"), "days")) %>%
  mutate(rnd_dat = sample(10, nrow(dates), replace = TRUE))

df1 <- dates %>%
  mutate(yr = year(dt),
         mnth = month(dt),
         quarter = quarter(dt),
         the_lag = if_else(mnth %% 3 == 0, 3, mnth %% 3))
Converting monthly data to quarterly in R

Converting monthly data to quarterly in R


By : Hemanth Subbiah
Date : March 29 2020, 07:55 AM
seems to work fine From what I can see my best guess is that you load the data using read.csv or read.table from utils. Instead of NA the original file that you were loading into R contained the empty string "" (the first level of the factor in the dput), which caused the entire column to be seen as character and turned to factors upon loading. In this case you should do someting like this:
code :
data <- read.csv(<YOURFILE>, <YOURARGUMENTS>, colClasses = "character",
 stringsAsFactors = FALSE) # or read.table(... whichever you are using
data[[1]] <- factor(data[[1]])
for(i in 2:ncol(data)) data[[i]] <- as.numeric(data[[i]])
Combining Monthly and Quarterly Data

Combining Monthly and Quarterly Data


By : giyom
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further ???
That's three question marks, but it doesn't really differ in information from one.
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