data-cleaning

List with attributes of persons loaded into pandas dataframe df2 . For cleanup I want to replace value zero ( 0 or '0' ) by np.nan . df2.dtypes ID object Name object Weight float64 Height float64 BootSize object SuitSize object Type object dtype: object Working code to set value zero to np.nan : df2.loc[df2['Weight'] == 0,'Weight'] = np.nan df2.loc[df2['Height'] == 0,'Height'] = np.nan df2.loc[df2['BootSize'] == '0','BootSize'] = np.nan df2.loc[df2['SuitSize'] == '0','SuitSize'] = np.nan Believe this can be done in a similar/shorter way: df2[["Weight","Height","BootSize","SuitSize"]].astype

Dataset HAVE is a tibble edgelist of phone call data from the characters of Recess : Student Friend nCalls TJ Spinelli 3 TJ Gretchen 7 TJ Gus 6 TJ Vince 8 TJ King Bob 1 TJ Mikey 2 Spinelli TJ 3 Spinelli Vince 2 Randall Ms. Finster 17 Dataset NEED includes all original columns from HAVE but includes a new variable, nCallsPerStudent , that is exactly what it sounds like: Student Friend nCalls nCallsPerStudent TJ Spinelli 3 27 TJ Gretchen 7 27 TJ Gus 6 27 TJ Vince 8 27 TJ King Bob 1 27 TJ Mikey 2 27 Spinelli TJ 3 5 Spinelli Vince 2 5 Randall Ms. Finster 17 17 How do I get from HAVE to NEED ? We

I have been working with Coldfusion 9 lately (background in PHP primarily) and I am scratching my head trying to figure out how to 'clean/sanitize' input / string that is user submitted. I want to make it HTMLSAFE, eliminate any javascript, or SQL query injection, the usual. I am hoping I've overlooked some kind of function that already comes with CF9. Can someone point me in the proper direction? Stephen Moretti This an addition to Kyle's suggestions not an alternative answer, but the comments panel is a bit rubbish for links. Take a look a the ColdFusion string functions . You've got

I am working on HCUP data and this has range of values in one single column that needs to be split into multiple columns. Below is the HCUP data frame for reference : code label 61000-61003 excision of CNS 0169T-0169T ventricular shunt The desired output should be : code label 61000 excision of CNS 61001 excision of CNS 61002 excision of CNS 61003 excision of CNS 0169T ventricular shunt My approach to this problem is using the package splitstackshape and using this code library(data.table) library(splitstackshape) cSplit(hcup, "code", "-")[, list(code = code_1:code_2, by = label)] This

I have a dataframe: Name Section 1 James P3 2 Sam 2.5C 3 Billy T35 4 Sarah A85 5 Felix 5I How do I split numeric values into a separate column called Section_Number and also split alphabetic values to Section_Letter. Desired results Name Section Section_Number Section_Letter 1 James P3 3 P 2 Sam 2.5C 2.5 C 3 Billy T35 35 T 4 Sarah A85 85 A 5 Felix 5L 5 L Use str.replace with str.extract by [A-Z]+ for all uppercase strings: df['Section_Number'] = df['Section'].str.replace('([A-Z]+)', '') df['Section_Letter'] = df['Section'].str.extract('([A-Z]+)') print (df) Name Section Section_Number Section