4. Going elsewhere with your data - csvkit 2.0.1 documentation

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# 4. Going elsewhere with your data

## 4.1. csvjson: going online

Very frequently one of the last steps in any data analysis is to get the data onto the web for display as a table, map or chart. CSV is rarely the ideal format for this. More often than not what you want is JSON and that’s where csvjson comes in. csvjson takes an input CSV and outputs neatly formatted JSON. For the sake of illustration, let’s use csvcut and csvgrep to convert just a small slice of our data:

    csvcut -c county,item_name data.csv | csvgrep -c county -m "GREELEY" | csvjson --indent 4

    [
        {
            "county": "GREELEY",
            "item_name": "RIFLE,7.62 MILLIMETER"
        },
        {
            "county": "GREELEY",
            "item_name": "RIFLE,7.62 MILLIMETER"
        },
        {
            "county": "GREELEY",
            "item_name": "RIFLE,7.62 MILLIMETER"
        }
    ]

A common usage of turning a CSV into a JSON file is for usage as a lookup table in the browser. This can be illustrated with the ACS data we looked at earlier, which contains a unique `fips` code for each county:

    csvjson --indent 4 --key fips acs2012_5yr_population.csv | head

    {
        "31001": {
            "fips": "31001",
            "name": "Adams County, NE",
            "total_population": "31299",
            "margin_of_error": "0"
        },
        "31003": {
            "fips": "31003",
            "name": "Antelope County, NE",
            "...": "..."
        }
    }

For making maps, csvjson can also output GeoJSON, see its csvjson for more details.

## 4.2. csvpy: going into code

For the programmers out there, the command line is rarely as functional as just writing a little bit of code. csvpy exists just to make a programmer’s life easier. Invoking it simply launches a Python interactive terminal, with the data preloaded into a CSV reader:

    $ csvpy data.csv
    Welcome! "data.csv" has been loaded in a reader object named "reader".
    >>> print(len(list(reader)))
    1037
    >>> quit()

In addition to being a time-saver, because this uses agate, the reader is Unicode aware.

## 4.3. csvformat: for legacy systems

It is a foundational principle of csvkit that it always outputs cleanly formatted CSV data. None of the normal csvkit tools can be forced to produce pipe or tab-delimited output, despite these being common formats. This principle is what allows the csvkit tools to chain together so easily and hopefully also reduces the amount of crummy, non-standard CSV files in the world. However, sometimes a legacy system just has to have a pipe-delimited file and it would be crazy to make you use another tool to create it. That’s why we’ve got csvformat.

Pipe-delimited:

    csvformat -D \| data.csv

Tab-delimited:

    csvformat -T data.csv

Quote every cell:

    csvformat -U 1 data.csv

Ampersand-delimited, dollar-signs for quotes, quote all strings, and asterisk for line endings:

    csvformat -D \& -Q \$ -U 2 -M \* data.csv

You get the picture.

## 4.4. Summing up

Thus concludes the csvkit tutorial. At this point, I hope, you have a sense a breadth of possibilities these tools open up with a relatively small number of command-line tools. Of course, this tutorial has only scratched the surface of the available options, so remember to check the Reference documentation for each tool as well.

So armed, go forth and expand the empire of the king of tabular file formats.