Examining the data
******************


csvstat: statistics without code
================================

In the previous section we saw how we could use *csvlook* and *csvcut*
to view slices of our data. This is a good tool for exploring a
dataset, but in practice we usually need to get the broadest possible
view before we can start diving into specifics.

csvstat is designed to give us just such a broad understanding of our
data. Inspired by the "summary()" function from the computational
statistics programming language “R”, "csvstat" will generate summary
statistics for all the data in a CSV file.

Let’s examine summary statistics for a few columns from our dataset.
As we learned in the last section, we can use "csvcut" and a pipe to
pick out the columns we want:

   csvcut -c county,acquisition_cost,ship_date data.csv | csvstat

   1. county
     Text
     Nulls: False
     Unique values: 35
     Max length: 10
     5 most frequent values:
         DOUGLAS:      760
         DAKOTA:       42
         CASS: 37
         HALL: 23
         LANCASTER:    18
   2. acquisition_cost
     Number
     Nulls: False
     Min: 0.0
     Max: 412000.0
     Sum: 5430787.55
     Mean: 5242.072924710424710424710425
     Median: 6000.0
     Standard Deviation: 13368.07836799839045093904423
     Unique values: 75
     5 most frequent values:
         6800.0:       304
         10747.0:      195
         6000.0:       105
         499.0:        98
         0.0:  81
   3. ship_date
     Date
     Nulls: False
     Min: 2006-03-07
     Max: 2014-01-30
     Unique values: 84
     5 most frequent values:
         2013-04-25:   495
         2013-04-26:   160
         2008-05-20:   28
         2012-04-16:   26
         2006-11-17:   20

   Row count: 1036

*csvstat* infers the type of data in each column and then performs
basic statistics on it. The particular statistics computed depend on
the type of the column (numbers, text, dates, etc).

In this example the first column, "county" was identified as type
"Text". We see that there are "35" counties represented in the dataset
and that "DOUGLAS" is far and away the most frequently occurring. A
quick Google search shows that there are "93" counties in Nebraska, so
we know that either not every county received equipment or that the
data is incomplete. We can also find out that Douglas county contains
Omaha, the state’s largest city by far.

The "acquisition_cost" column is type "Number". We see that the
largest individual cost was "412000.0". (Probably dollars, but let’s
not presume.) Total acquisition costs were "5430787.55".

Lastly, the "ship_date" column (type "Date") shows us that the
earliest data is from "2006" and the latest from "2014". We may also
note that an unusually large amount of equipment was shipped in April,
2013.

As a journalist, this quick glance at the data gave me a tremendous
amount of information about the dataset. Although we have to be
careful about assuming to much from this quick glance (always double-
check the numbers mean what you think they mean!) it can be an
invaluable way to familiarize yourself with a new dataset.


csvgrep: find the data you need
===============================

After reviewing the summary statistics you might wonder what equipment
was received by a particular county. To get a simple answer to the
question we can use csvgrep to search for the state’s name amongst the
rows. Let’s also use "csvcut" to just look at the columns we care
about and "csvlook" to format the output:

   csvcut -c county,item_name,total_cost data.csv | csvgrep -c county -m LANCASTER | csvlook

   | county    | item_name                      | total_cost |
   | --------- | ------------------------------ | ---------- |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | LIGHT ARMORED VEHICLE          |          0 |
   | LANCASTER | LIGHT ARMORED VEHICLE          |          0 |
   | LANCASTER | LIGHT ARMORED VEHICLE          |          0 |
   | LANCASTER | MINE RESISTANT VEHICLE         |    412,000 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |

"LANCASTER" county contains Lincoln, Nebraska, the capital of the
state and its second-largest city. The "-m" flag means “match” and
will find text anywhere in a given column–in this case the "county"
column. For those who need a more powerful search you can also use
"-r" to search for a regular expression.


csvsort: order matters
======================

Now let’s use csvsort to sort the rows by the "total_cost" column, in
reverse (descending) order:

   csvcut -c county,item_name,total_cost data.csv | csvgrep -c county -m LANCASTER | csvsort -c total_cost -r | csvlook

   | county    | item_name                      | total_cost |
   | --------- | ------------------------------ | ---------- |
   | LANCASTER | MINE RESISTANT VEHICLE         |    412,000 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | IMAGE INTENSIFIER,NIGHT VISION |      6,800 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | RIFLE,5.56 MILLIMETER          |        120 |
   | LANCASTER | LIGHT ARMORED VEHICLE          |          0 |
   | LANCASTER | LIGHT ARMORED VEHICLE          |          0 |
   | LANCASTER | LIGHT ARMORED VEHICLE          |          0 |

Two interesting things should jump out about this sorted data: that
"LANCASTER" county got a very expensive "MINE RESISTANT VEHICLE" and
that it also go three other "LIGHT ARMORED VEHICLE".

What commands would you use to figure out if other counties also
received large numbers of vehicles?


Summing up
==========

At this point you should be able to use csvkit to investigate the
basic properties of a dataset. If you understand this section, you
should be ready to move onto Power tools.
