dataframe

问题 df have id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 df want- add a new row to the measure for each id, called spend, calculated by subtracting measure=income - measure=savings, for each of the periods t1,t2,t3, for each id id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 1 spend 9 13 9 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 2 spend 1 14 68 Trying: df.loc[df['Measure'] == 'spend'] = df.loc[df[

问题 df have id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 df want- add a new row to the measure for each id, called spend, calculated by subtracting measure=income - measure=savings, for each of the periods t1,t2,t3, for each id id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 1 spend 9 13 9 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 2 spend 1 14 68 Trying: df.loc[df['Measure'] == 'spend'] = df.loc[df[

问题 So I have around 13 years of stock market data of daily low high open close. The problem is the markets are closed sometimes in between and hence Monday to Friday might not appear continuously sometimes. Look below Date Day Open High Low Close Adjusted Close 0 17-09-2007 Monday 6898 6977.2 6843 6897.1 6897.100098 1 18-09-2007 Tuesday 6921.15 7078.95 6883.6 7059.65 7059.649902 2 19-09-2007 Wednesday 7111 7419.35 7111 7401.85 7401.850098 3 20-09-2007 Thursday 7404.95 7462.9 7343.6 7390.15 7390

问题 So I have around 13 years of stock market data of daily low high open close. The problem is the markets are closed sometimes in between and hence Monday to Friday might not appear continuously sometimes. Look below Date Day Open High Low Close Adjusted Close 0 17-09-2007 Monday 6898 6977.2 6843 6897.1 6897.100098 1 18-09-2007 Tuesday 6921.15 7078.95 6883.6 7059.65 7059.649902 2 19-09-2007 Wednesday 7111 7419.35 7111 7401.85 7401.850098 3 20-09-2007 Thursday 7404.95 7462.9 7343.6 7390.15 7390

问题 I have a simple data frame with specifies start and end positions within lists. These start and end positions define i number of regions. Now I would like to test whether a given position lies within such a region and if yes I need to know in which region (i). Here is a simple example data frame: start <- list(c(5,10,15), c(5) ,c(6,11),c(6,11)) end <- list(c(7,11,17), c(10), c(8,12),c(8,12)) imax <- c(3,1,2,2) position <- c(11,6,9,8) example <- data.frame(start = I(start), end = I(end), imax

问题 I have a simple data frame with specifies start and end positions within lists. These start and end positions define i number of regions. Now I would like to test whether a given position lies within such a region and if yes I need to know in which region (i). Here is a simple example data frame: start <- list(c(5,10,15), c(5) ,c(6,11),c(6,11)) end <- list(c(7,11,17), c(10), c(8,12),c(8,12)) imax <- c(3,1,2,2) position <- c(11,6,9,8) example <- data.frame(start = I(start), end = I(end), imax

问题 I have the following sample CSV. ,cid1,cid2,cid3 rid1,0.1,0.4,0.3 rid2,1.0,0.1,0.5 rid3,0.2,0.5,0.1 rid4,0.3,0.4,0.8 rid5,0.2,0.3,0.7 rid6,0.9,0.2,0.1 rid7,0.4,0.8,0.9 rid8,0.6,0.5,0.7 rid9,0.3,0.9,0.4 I want to show n rows with the highest value for every column in the file. Ideally, I would like to get the following output (for n = 3). cid1 rid2 1.0 cid1 rid6 0.9 cid1 rid8 0.6 # Blank lines separating columns are optional. cid2 rid9 0.9 cid2 rid7 0.8 cid2 rid8 0.5 cid3 rid7 0.9 cid3 rid4 0

问题 I am very new to both python and pandas. I would like to know how to convert dataframe elements from hex string input to integer number, also I have followed the solution provided by: convert pandas dataframe column from hex string to int However, it is still not working. The following is my code: df = pd.read_csv(filename, delim_whitespace = True, header = None, usecols = range(7,23,2)) for i in range(num_frame): skipheader = lineNum[header_padding + i*2] data = df.iloc[skipheader:skipheader

问题 I have a dataframe with columns col1,col2,col3. col1,col2 are strings. col3 is a Map[String,String] defined below |-- col3: map (nullable = true) | |-- key: string | |-- value: string (valueContainsNull = true) I have grouped by col1,col2 and aggregated using collect_list to get an Array of Maps and stored in col4. df.groupBy($"col1", $"col2").agg(collect_list($"col3").as("col4")) |-- col4: array (nullable = true) | |-- element: map (containsNull = true) | | |-- key: string | | |-- value:

问题 data[, allkneePR := Reduce(`|`, lapply(.SD, `==`, "0082")), .SDcols=PR1:PR3] Hey, I'm trying to look for different diagnoses c("0082", "0083", "0084") across a range of rows and columns in data.table (the dataset is huge). If one of the values is "0082" or "0083" or "0084" in any of the columns PR1:PR3 I want another column that indicates true. Right now this works with the above code, but I am trying to add in multiple diagnoses, not just "0082". I tried the any() function which doesn't work