One approach is to use the tidy function from the broom package. It is versatile and can organize most R statistical results into a data frame. library(broom) set.seed(1) x <- rnorm(1000) x[sample(1:length(x), 100)] <- NA df <- tidy(summary(x)) df minimum q1 median mean q3 maximum NA’s 1 -3.008 -0.6834 -0.01371 0.00106 0.6978 3.81 100 As … Read more
Using a toy example… df <- data.frame(a=LETTERS[1:10],b=LETTERS[3:12],stringsAsFactors = FALSE) limits <- c(“E”,”H”) sapply(df,function(x){ del.min <- grep(limits[1],x) del.max <- grep(limits[2],x) x[del.min:del.max] <- “” return(x)}) a b [1,] “A” “C” [2,] “B” “D” [3,] “C” “” [4,] “D” “” [5,] “” “” [6,] “” “” [7,] “” “I” [8,] “” “J” [9,] “I” “K” [10,] “J” “L”
First, move the non-date columns in to the index, then use replace and reset_index: df = df.set_index(‘CashFlows’) df = df.rename(columns=lambda x: ‘year_’+str(x.year))
You can try following code: import pandas as pd df1 = pd.read_csv(“Insert file path”) df2 = pd.read_csv(“Insert file path”) df1[‘Date’] = pd.to_datetime(df1[‘Date’] ,errors=”coerce”,format=”%Y-%m-%d”) df2[‘Date’] = pd.to_datetime(df2[‘Date’] ,errors=”coerce”,format=”%Y-%m-%d”) df = df1.merge(df2,how=’inner’, on =’Date’) df.to_csv(‘data.csv’,index=False) This should solve your problem.
I think I understand what you’re asking. You just want to have a new dataframe that calculates the time difference between the three different entries for each unique order id? So, I start by creating the dataframe: data = [ [11238,3943,201805030759165986,’新建订单’,20180503075916,’2018/5/3 07:59:16′,’2018/5/3 07:59:16′], [11239,3943,201805030759165986,’新建订单’,20180503082115,’2018/5/3 08:21:15′,’2018/5/3 08:21:15′], [11240,3943,201805030759165986,’新建订单’,20180503083204,’2018/5/3 08:32:04′,’2018/5/3 08:32:04′], [11241,3941,201805030856445991,’新建订单’,20180503085644,’2018/5/3 08:56:02′,’2018/5/3 08:56:44′], [11242,3941,201805022232081084,’初审成功’,20180503085802,’2018/5/3 08:58:02′,’2018/5/3 08:58:02′], … Read more