- Is it bad to use uppercase letters for html tags?
What is the best practice?
<HTML> or <html>And why we should stick with one particular case?
However all browsers seems to interpret both cases and returns the expected output.
- Case insensitive string matching in awk
Assume a multi-line text file
filein which some lines start with whitespaces.$ cat filefoo Baz baz QUX QUx QuuxBaZ QuxBazaaRFurther assume that I wish to convert all those lines that start with a keyword (e.g. "baz") to lowercase letters, irrespective if (a) that keyword is written in lower- or uppercase letters (or any combination thereof) itself, and (b) that keyword is preceeded by whitespaces.
$ cat file | sought_commandfoo Baz # not to lowercase (line does not start with keyword) baz qux # to lowercase QUx Quuxbaz qux # to lowercaseBazaaR # not to lowercase (line does not start with keyword, but merely with a word containing the keyword)I believe that awk is the tool to do it, but I am uncertain how to implement the case-insensitivity for the keyword matching.
$ cat file | awk '{ if($1 ~ /^ *baz/) print tolower($0); else print $0}'foo Baz baz qux QUx QuuxBaZ Qux # ERROR HERE: was not replaced, b/c keyword not recognized.BazaaREDIT 1:Adding
IGNORECASE=1appears to resolve the case-insensitivity, but now incorrectly converts the last line to lowercase.$ cat file | awk '{IGNORECASE=1; if($1~/^ *baz/) print tolower($0); else print $0}'foo Baz baz qux QUx Quuxbaz quxbazaar # ERROR HERE: should not be converted to lowercase, as keyword not present (emphasis on word!). - Convert whole dataframe from lower case to upper case with Pandas
I have a dataframe like the one displayed below:
# Create an example dataframe about a fictional armyraw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks'], 'company': ['1st', '1st', '2nd', '2nd'], 'deaths': ['kkk', 52, '25', 616], 'battles': [5, '42', 2, 2], 'size': ['l', 'll', 'l', 'm']}df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'deaths', 'battles', 'size'])
My goal is to transform every single string inside of the dataframe to upper case so that it looks like this:
Notice: all data types are objects and must not be changed; the output must contain all objects. I want to avoid to convert every single column one by one... I would like to do it generally over the whole dataframe possibly.
What I tried so far is to do this but without success
df.str.upper() - How to capitalize the first character of each word in a string
Is there a function built into Java that capitalizes the first character of each word in a String, and does not affect the others?
Examples:
jon skeet->Jon Skeetmiles o'Brien->Miles O'Brien(B remains capital, this rules out Title Case)old mcdonald->Old Mcdonald*
*(
Old McDonaldwould be find too, but I don't expect it to be THAT smart.)A quick look at the Java String Documentation reveals only
toUpperCase()andtoLowerCase(), which of course do not provide the desired behavior. Naturally, Google results are dominated by those two functions. It seems like a wheel that must have been invented already, so it couldn't hurt to ask so I can use it in the future. - How to rename all folders and files to lowercase on Linux?
I have to rename a complete folder tree recursively so that no uppercase letter appears anywhere (it's C++ sourcecode, but that shouldn't matter). Bonus points for ignoring CVS and SVN control files/folders. Preferred way would be a shell script, since shell should be available at any Linux box.
There were some valid arguments about details of the file renaming.
I think files with same lowercase names should be overwritten, it's the user's problem. When checked out on a case-ignoring file system would overwrite the first one with the latter, too.
I would consider A-Z characters and transform them to a-z, everything else is just calling for problems (at least with source code).
The script would be needed to run a build on a Linux system, so I think changes to CVS or SVN control files should be omitted. After all, it's just a scratch checkout. Maybe an "export" is more appropriate.