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# Split by certain byte size
split -b
 
# Generate suffixes of length N
split -a
 
# Split using hex suffixes
split -x

SORT & UNIQ

The preceding commands are obvious: they do what they say they do. These two provide the most punch in tandem (i.e. unique word counts). This is due to uniq, which only operates on duplicate adjacent lines. Thus, the reason to sort before piping the output through. One interesting note is that sort -u will achieve the same results as the typical sort file.txt | uniq pattern.

Sort does have a sneakily useful ability for data scientists: the ability to sort an entire CSV based on a particular column.

# Sorting a CSV file by the second column alphabetically
sort -t, -k2 filename.csv


# Numerically
sort -t, -k2n filename.csv


# Reverse order
sort -t, -k2nr filename.csv

The -t option here is to specify the comma as our delimiter. More often than not spaces or tabs are assumed. Furthermore, the -k flag is for specifying our key.

Useful options:

# Ignore case
sort -f
 
# Reverse sort order
sort -r
 
# scramble order
sort -R
 
# Count number of occurrences
uniq -c
 
# Only print duplicate lines
uniq -d

CUT

Cut is for removing columns. To illustrate, if we only wanted the first and third columns:

cut -d, -f 1,3 filename.csv

To select every column other than the first:

cut -d, -f 2- filename.csv

In combination with other commands, cut serves as a filter:

# Print first 10 lines of column 1 and 3, where "some_string_value" is present
head filename.csv | grep "some_string_value" | cut -d, -f 1,3

Finding out the number of unique values within the second column:

cat filename.csv | cut -d, -f 2 | sort | uniq | wc -l


# Count occurences of unique values, limiting to first 10 results
cat filename.csv | cut -d, -f 2 | sort | uniq -c | head

PASTE

Paste is a niche command with an interesting function. If you have two files that you need merged, and they are already sorted, paste has you covered.

# names.txt
adam
john
zach

# jobs.txt
lawyer
youtuber
developer

# Join the two into a CSV
paste -d ',' names.txt jobs.txt > person_data.txt

# Output
adam,lawyer
john,youtuber
zach,developer

JOIN

Join is a simplistic, quasi-tangential, SQL. The largest differences being that joinwill return all columns and matches can only be on one field. By default, join will try and use the first column as the match key. For a different result, the following syntax is necessary:

# Join the first file (-1) by the second column
# and the second file (-2) by the first
join -t, -1 2 -2 1 first_file.txt second_file.txt

The standard join is an inner join. However, an outer join is also viable through the -a flag. Another noteworthy quirk is the -e flag, which can be used to substitute a value if a missing field is found.

# Outer join, replace blanks with NULL in columns 1 and 2
# -o which fields to substitute - 0 is key, 1.1 is first column, etc...
join -t, -1 2 -a 1 -a2 -e ' NULL' -o '0,1.1,2.2' first_file.txt second_file.txt

Useful options:

# Print unpairable lines
join -a
 
# Replace missing input fields
join -e
 
# Equivalent to -1 FIELD -2 FIELD
join -j

GREP

Global search for a regular expression and print, or grep; likely, the most well known command, and with good reason. Grep has a lot of power, especially for finding your way around large codebases. Within the realm of data science, it acts as a refining mechanism for other commands. Although its standard usage is valuable as well.

# Recursively search and list all files in directory containing 'word'
grep -lr 'word' .


# List number of files containing word
grep -lr 'word' . | wc -l

Count total number of lines containing word/pattern

grep -c 'some_value' filename.csv


# Same thing, but in all files in current directory by file name
grep -c 'some_value' *

Grep for multiple values using the or operator – \| 

grep "first_value\|second_value" filename.csv

Useful options:

# Make grep output colorful
alias grep="grep --color=auto"
 
# Use extended regular expressions
grep -E
 
# Only match whole words
grep -w
 
# Print name of files with match
grep -l
 
# Inverted matching
grep -v

SED

At its core sed is a stream editor that operates on a line-by-line basis. It excels at substitutions, but can also be leveraged for all out refactoring.

The most basic sed command consists of s/old/new/g . This translates to search for old value, replace all occurences in-line with new. Without the /g our command would terminate after the first occurrence on the line.

To get a quick taste of the power lets dive into an example. In this scenario you’ve been given the following file:

balance,name
$1,000,john
$2,000,jack

The first thing we may want to do is remove the dollar signs. The -i flag indicates in-place. The two single quotes are to indicate a zero-length file extension, thus overwriting our initial file. Ideally, you would test each of these individually and then output to a new file.

sed -i '' 's/\$//g' data.txt


# balance,name
# 1,000,john
# 2,000,jack

Next up, the commas in our balance column values:

sed -i '' 's/\([0-9]\),\([0-9]\)/\1\2/g' data.txt

# balance,name
# 1000,john
# 2000,jack

Lastly, Jack up and decided to quite one day. So, removing him:

sed -i '' '/jack/d' data.txt

# balance,name
# 1000,john

AWK

The best for last. Awk is much more than a simple command: it is a full-blown language. Of everything covered in this article, awk is by far the coolest.

Common use cases for awk include:

• Text processing
• Formatted text reports
• Performing arithmetic operations
• Performing string operations

Awk can parallel grep in its most nascent form:

awk '/word/' filename.csv

Or with a little more magic the combination of grep and cut. Here, awk prints the third and fourth column, tab separated, for all lines with our word. -F, merely changes our delimiter to a comma:

awk -F, '/word/ { print $3 "\t" $4 }' filename.csv

Awk comes with a lot of nifty variables built-in. For instance, NF - number of fields - and NR - number of records. To get the fifty-third record in a file:

awk -F, 'NR == 53' filename.csv

An added wrinkle is the ability to filter based off of one or more values. The first example, below, will print the line number and columns for records where the first column equals string.

awk -F, ' $1 == "string" { print NR, $0 } ' filename.csv


# Filter based off of numerical value in second column
awk -F, ' $2 == 1000 { print NR, $0 } ' filename.csv

Multiple numercial expressions:

# Print line number and columns where column three greater
# than 2005 and column five less than one thousand

awk -F, ' $3 >= 2005 && $5 <= 1000 { print NR, $0 } ' filename.csv

Sum the third column:

awk -F, '{ x+=$3 } END { print x }' filename.csv

The sum of the third column, for values where the first column equals “something”.

awk -F, '$1 == "something" { x+=$3 } END { print x }' filename.csv

Get the dimensions of a file:

awk -F, 'END { print NF, NR }' filename.csv


# Prettier version
awk -F, 'BEGIN { print "COLUMNS", "ROWS" }; END { print NF, NR }' filename.csv

Print lines appearing twice:

awk -F, '++seen[$0] == 2' filename.csv

Remove duplicate lines:

# Consecutive lines
awk 'a !~ $0; {a=$0}']


# Nonconsecutive lines
awk '! a[$0]++' filename.csv


# More efficient
awk '!($0 in a) {a[$0];print}

Substitute multiple values using built-in function gsub() :

awk '{gsub(/scarlet|ruby|puce/, "red"); print}'

This awk command will combine multiple CSV files, ignoring the header and then append it at the end.

awk 'FNR==1 && NR!=1{next;}{print}' *.csv > final_file.csv

Need to downsize a massive file? Welp, awk can handle that with help from sed. Specifically, this command breaks one big file into multiple smaller ones based on a line count. This one-liner will also add an extension.

sed '1d;$d' filename.csv | awk 'NR%NUMBER_OF_LINES==1{x="filename-"++i".csv";}{print > x}'


# Example: splitting big_data.csv into data_(n).csv every 100,000 lines
sed '1d;$d' big_data.csv | awk 'NR%100000==1{x="data_"++i".csv";}{print > x}'