Grep suits the bill for all my requirement for efficient search of files containing text in Linux and Mac. The following commands detail the use cases of grep.

Search for pattern
grep -rnw 'folder' -e 'text'

-r stands for recursive.
-n is the line number.
-w stands for whole word match.

Example: grep -rnw . -e 'import'
This searches for the the text ‘import’ in the current directory recursively.

Found this tip at stackoverflow.com.

I recently received a blob of data which needed some alteration to properly suit the CSV format. I found that the data file ‘xyz.dat’ contained close to 600,000 rows so writing a VBA script in Excel to format the data was not an option. I opted to do it using some plain commands in Unix. I had to attain the following purpose:

1. Remove all rows with blank lines.
2. Remove all rows that didn’t start with the number ‘1’
3. Trim the file to 1000 rows to sample the data.

I executed the following commands to attain the above objectives.

To remove all blank lines I used this command. I am basically asking grep to treat the text as ASCII text and using a regular expression to identify blank rows:
cat xyz.dat | grep -a -v -e '^[[:space:]]*$' > xyz_no_space.dat

To remove all rows that don’t start with ‘1’ I used this command. This command is similar to above command except for the regular expression to identify rows that start with ‘1’.
cat xyz_no_space.dat | grep -a '^1' > xyz_no_space_start_with_1.dat

At this point we have the data we want but for sampling purpose I need to copy first 1000 rows into another file. This command uses ‘sed’ to do it.
sed -n -e '1,1000p' xyz_no_space_start_with_1.dat > xyz_trimmed_1_1000.dat

This command basically renames the *.dat file into *.csv for convenience.
mv xyz_trimmed_1_1000.dat xyz_trimmed_1_1000.csv

A summary of all commands I executed is listed below:
cat xyz.dat | grep -a -v -e '^[[:space:]]*$' > xyz_no_space.dat
cat xyz_no_space.dat | grep -a '^1' > xyz_no_space_start_with_1.dat
sed -n -e '1,1000p' xyz_no_space_start_with_1.dat > xyz_trimmed_1_1000.dat
mv xyz_trimmed_1_1000.dat xyz_trimmed_1_1000.csv