Miller in 10 minutes¶
CSV-file examples¶
Suppose you have this CSV data file:
$ cat example.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
purple,triangle,0,51,81.2290,8.5910
red,square,0,64,77.1991,9.5310
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430
mlr cat
is like cat – it passes the data through unmodified:
$ mlr --csv cat example.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
purple,triangle,0,51,81.2290,8.5910
red,square,0,64,77.1991,9.5310
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430
but it can also do format conversion (here, you can pretty-print in tabular format):
$ mlr --icsv --opprint cat example.csv
color shape flag index quantity rate
yellow triangle 1 11 43.6498 9.8870
red square 1 15 79.2778 0.0130
red circle 1 16 13.8103 2.9010
red square 0 48 77.5542 7.4670
purple triangle 0 51 81.2290 8.5910
red square 0 64 77.1991 9.5310
purple triangle 0 65 80.1405 5.8240
yellow circle 1 73 63.9785 4.2370
yellow circle 1 87 63.5058 8.3350
purple square 0 91 72.3735 8.2430
mlr head
and mlr tail
count records rather than lines. Whethere you’re getting the first few records or the last few, the CSV header is included either way:
$ mlr --csv head -n 4 example.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
$ mlr --csv tail -n 4 example.csv
color,shape,flag,index,quantity,rate
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430
You can sort primarily alphabetically on one field, then secondarily numerically descending on another field:
$ mlr --icsv --opprint sort -f shape -nr index example.csv
color shape flag index quantity rate
yellow circle 1 87 63.5058 8.3350
yellow circle 1 73 63.9785 4.2370
red circle 1 16 13.8103 2.9010
purple square 0 91 72.3735 8.2430
red square 0 64 77.1991 9.5310
red square 0 48 77.5542 7.4670
red square 1 15 79.2778 0.0130
purple triangle 0 65 80.1405 5.8240
purple triangle 0 51 81.2290 8.5910
yellow triangle 1 11 43.6498 9.8870
You can use cut
to retain only specified fields, in the same order they appeared in the input data:
$ mlr --icsv --opprint cut -f flag,shape example.csv
shape flag
triangle 1
square 1
circle 1
square 0
triangle 0
square 0
triangle 0
circle 1
circle 1
square 0
You can also use cut -o
to retain only specified fields in your preferred order:
$ mlr --icsv --opprint cut -o -f flag,shape example.csv
flag shape
1 triangle
1 square
1 circle
0 square
0 triangle
0 square
0 triangle
1 circle
1 circle
0 square
You can use cut -x
to omit fields you don’t care about:
$ mlr --icsv --opprint cut -x -f flag,shape example.csv
color index quantity rate
yellow 11 43.6498 9.8870
red 15 79.2778 0.0130
red 16 13.8103 2.9010
red 48 77.5542 7.4670
purple 51 81.2290 8.5910
red 64 77.1991 9.5310
purple 65 80.1405 5.8240
yellow 73 63.9785 4.2370
yellow 87 63.5058 8.3350
purple 91 72.3735 8.2430
You can use filter
to keep only records you care about:
$ mlr --icsv --opprint filter '$color == "red"' example.csv
color shape flag index quantity rate
red square 1 15 79.2778 0.0130
red circle 1 16 13.8103 2.9010
red square 0 48 77.5542 7.4670
red square 0 64 77.1991 9.5310
$ mlr --icsv --opprint filter '$color == "red" && $flag == 1' example.csv
color shape flag index quantity rate
red square 1 15 79.2778 0.0130
red circle 1 16 13.8103 2.9010
You can use put
to create new fields which are computed from other fields:
$ mlr --icsv --opprint put '$ratio = $quantity / $rate; $color_shape = $color . "_" . $shape' example.csv
color shape flag index quantity rate ratio color_shape
yellow triangle 1 11 43.6498 9.8870 4.414868 yellow_triangle
red square 1 15 79.2778 0.0130 6098.292308 red_square
red circle 1 16 13.8103 2.9010 4.760531 red_circle
red square 0 48 77.5542 7.4670 10.386260 red_square
purple triangle 0 51 81.2290 8.5910 9.455127 purple_triangle
red square 0 64 77.1991 9.5310 8.099790 red_square
purple triangle 0 65 80.1405 5.8240 13.760388 purple_triangle
yellow circle 1 73 63.9785 4.2370 15.099953 yellow_circle
yellow circle 1 87 63.5058 8.3350 7.619172 yellow_circle
purple square 0 91 72.3735 8.2430 8.779995 purple_square
Even though Miller’s main selling point is name-indexing, sometimes you really want to refer to a field name by its positional index. Use $[[3]]
to access the name of field 3 or $[[[3]]]
to access the value of field 3:
$ mlr --icsv --opprint put '$[[3]] = "NEW"' example.csv
color shape NEW index quantity rate
yellow triangle 1 11 43.6498 9.8870
red square 1 15 79.2778 0.0130
red circle 1 16 13.8103 2.9010
red square 0 48 77.5542 7.4670
purple triangle 0 51 81.2290 8.5910
red square 0 64 77.1991 9.5310
purple triangle 0 65 80.1405 5.8240
yellow circle 1 73 63.9785 4.2370
yellow circle 1 87 63.5058 8.3350
purple square 0 91 72.3735 8.2430
$ mlr --icsv --opprint put '$[[[3]]] = "NEW"' example.csv
color shape flag index quantity rate
yellow triangle NEW 11 43.6498 9.8870
red square NEW 15 79.2778 0.0130
red circle NEW 16 13.8103 2.9010
red square NEW 48 77.5542 7.4670
purple triangle NEW 51 81.2290 8.5910
red square NEW 64 77.1991 9.5310
purple triangle NEW 65 80.1405 5.8240
yellow circle NEW 73 63.9785 4.2370
yellow circle NEW 87 63.5058 8.3350
purple square NEW 91 72.3735 8.2430
JSON-file examples¶
OK, CSV and pretty-print are fine. But Miller can also convert between a few other formats – let’s take a look at JSON output:
$ mlr --icsv --ojson put '$ratio = $quantity/$rate; $shape = toupper($shape)' example.csv
{ "color": "yellow", "shape": "TRIANGLE", "flag": 1, "index": 11, "quantity": 43.6498, "rate": 9.8870, "ratio": 4.414868 }
{ "color": "red", "shape": "SQUARE", "flag": 1, "index": 15, "quantity": 79.2778, "rate": 0.0130, "ratio": 6098.292308 }
{ "color": "red", "shape": "CIRCLE", "flag": 1, "index": 16, "quantity": 13.8103, "rate": 2.9010, "ratio": 4.760531 }
{ "color": "red", "shape": "SQUARE", "flag": 0, "index": 48, "quantity": 77.5542, "rate": 7.4670, "ratio": 10.386260 }
{ "color": "purple", "shape": "TRIANGLE", "flag": 0, "index": 51, "quantity": 81.2290, "rate": 8.5910, "ratio": 9.455127 }
{ "color": "red", "shape": "SQUARE", "flag": 0, "index": 64, "quantity": 77.1991, "rate": 9.5310, "ratio": 8.099790 }
{ "color": "purple", "shape": "TRIANGLE", "flag": 0, "index": 65, "quantity": 80.1405, "rate": 5.8240, "ratio": 13.760388 }
{ "color": "yellow", "shape": "CIRCLE", "flag": 1, "index": 73, "quantity": 63.9785, "rate": 4.2370, "ratio": 15.099953 }
{ "color": "yellow", "shape": "CIRCLE", "flag": 1, "index": 87, "quantity": 63.5058, "rate": 8.3350, "ratio": 7.619172 }
{ "color": "purple", "shape": "SQUARE", "flag": 0, "index": 91, "quantity": 72.3735, "rate": 8.2430, "ratio": 8.779995 }
Or, JSON output with vertical-formatting flags:
$ mlr --icsv --ojsonx tail -n 2 example.csv
{
"color": "yellow",
"shape": "circle",
"flag": 1,
"index": 87,
"quantity": 63.5058,
"rate": 8.3350
}
{
"color": "purple",
"shape": "square",
"flag": 0,
"index": 91,
"quantity": 72.3735,
"rate": 8.2430
}
Sorts and stats¶
Now suppose you want to sort the data on a given column, and then take the top few in that ordering. You can use Miller’s then
feature to pipe commands together.
Here are the records with the top three index
values:
$ mlr --icsv --opprint sort -f shape -nr index then head -n 3 example.csv
color shape flag index quantity rate
yellow circle 1 87 63.5058 8.3350
yellow circle 1 73 63.9785 4.2370
red circle 1 16 13.8103 2.9010
Lots of Miller commands take a -g
option for group-by: here, head -n 1 -g shape
outputs the first record for each distinct value of the shape
field. This means we’re finding the record with highest index
field for each distinct shape
field:
$ mlr --icsv --opprint sort -f shape -nr index then head -n 1 -g shape example.csv
color shape flag index quantity rate
yellow circle 1 87 63.5058 8.3350
purple square 0 91 72.3735 8.2430
purple triangle 0 65 80.1405 5.8240
Statistics can be computed with or without group-by field(s):
$ mlr --icsv --opprint --from example.csv stats1 -a count,min,mean,max -f quantity -g shape
shape quantity_count quantity_min quantity_mean quantity_max
triangle 3 43.649800 68.339767 81.229000
square 4 72.373500 76.601150 79.277800
circle 3 13.810300 47.098200 63.978500
$ mlr --icsv --opprint --from example.csv stats1 -a count,min,mean,max -f quantity -g shape,color
shape color quantity_count quantity_min quantity_mean quantity_max
triangle yellow 1 43.649800 43.649800 43.649800
square red 3 77.199100 78.010367 79.277800
circle red 1 13.810300 13.810300 13.810300
triangle purple 2 80.140500 80.684750 81.229000
circle yellow 2 63.505800 63.742150 63.978500
square purple 1 72.373500 72.373500 72.373500
If your output has a lot of columns, you can use XTAB format to line things up vertically for you instead:
$ mlr --icsv --oxtab --from example.csv stats1 -a p0,p10,p25,p50,p75,p90,p99,p100 -f rate
rate_p0 0.013000
rate_p10 2.901000
rate_p25 4.237000
rate_p50 8.243000
rate_p75 8.591000
rate_p90 9.887000
rate_p99 9.887000
rate_p100 9.887000
Choices for printing to files¶
Often we want to print output to the screen. Miller does this by default, as we’ve seen in the previous examples.
Sometimes we want to print output to another file: just use > outputfilenamegoeshere at the end of your command:
% mlr --icsv --opprint cat example.csv > newfile.csv
# Output goes to the new file;
# nothing is printed to the screen.
% cat newfile.csv
color shape flag index quantity rate
yellow triangle 1 11 43.6498 9.8870
red square 1 15 79.2778 0.0130
red circle 1 16 13.8103 2.9010
red square 0 48 77.5542 7.4670
purple triangle 0 51 81.2290 8.5910
red square 0 64 77.1991 9.5310
purple triangle 0 65 80.1405 5.8240
yellow circle 1 73 63.9785 4.2370
yellow circle 1 87 63.5058 8.3350
purple square 0 91 72.3735 8.2430
Other times we just want our files to be changed in-place: just use mlr -I:
% cp example.csv newfile.txt
% cat newfile.txt
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
purple,triangle,0,51,81.2290,8.5910
red,square,0,64,77.1991,9.5310
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430
% mlr -I --icsv --opprint cat newfile.txt
% cat newfile.txt
color shape flag index quantity rate
yellow triangle 1 11 43.6498 9.8870
red square 1 15 79.2778 0.0130
red circle 1 16 13.8103 2.9010
red square 0 48 77.5542 7.4670
purple triangle 0 51 81.2290 8.5910
red square 0 64 77.1991 9.5310
purple triangle 0 65 80.1405 5.8240
yellow circle 1 73 63.9785 4.2370
yellow circle 1 87 63.5058 8.3350
purple square 0 91 72.3735 8.2430
Also using mlr -I
you can bulk-operate on lots of files: e.g.:
mlr -I --csv cut -x -f unwanted_column_name *.csv
If you like, you can first copy off your original data somewhere else, before doing in-place operations.
Lastly, using tee
within put
, you can split your input data into separate files per one or more field names:
$ mlr --csv --from example.csv put -q 'tee > $shape.".csv", $*'
$ cat circle.csv
color,shape,flag,index,quantity,rate
red,circle,1,16,13.8103,2.9010
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
$ cat square.csv
color,shape,flag,index,quantity,rate
red,square,1,15,79.2778,0.0130
red,square,0,48,77.5542,7.4670
red,square,0,64,77.1991,9.5310
purple,square,0,91,72.3735,8.2430
$ cat triangle.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
purple,triangle,0,51,81.2290,8.5910
purple,triangle,0,65,80.1405,5.8240
Other-format examples¶
What’s a CSV file, really? It’s an array of rows, or records, each being a list of key-value pairs, or fields: for CSV it so happens that all the keys are shared in the header line and the values vary data line by data line.
For example, if you have:
shape,flag,index
circle,1,24
square,0,36
then that’s a way of saying:
shape=circle,flag=1,index=24
shape=square,flag=0,index=36
Data written this way are called DKVP, for delimited key-value pairs.
We’ve also already seen other ways to write the same data:
CSV PPRINT JSON
shape,flag,index shape flag index [
circle,1,24 circle 1 24 {
square,0,36 square 0 36 "shape": "circle",
"flag": 1,
"index": 24
},
DKVP XTAB {
shape=circle,flag=1,index=24 shape circle "shape": "square",
shape=square,flag=0,index=36 flag 1 "flag": 0,
index 24 "index": 36
}
shape square ]
flag 0
index 36
Anything we can do with CSV input data, we can do with any other format input data. And you can read from one format, do any record-processing, and output to the same format as the input, or to a different output format.