Shellviz
Visualizations
JSON
Objects can be visualized using the interactive JSON explorer. This allows you to collapse and expand JSON objects and copy sub-sections
user = {'name': 'Jane Smith', 'age': 31}
sv.visualize(user)- Python
Tables
JSON data can be examined in two ways: as a color-coded, collapsible view, and as a series of cards that can be searched or filtered.
sv.visualize([{ "year": "1945", "sightings": 6 }, { "year": "1946", "sightings": 8 }, { "year": "1947", "sightings": 34 }, { "year": "1948", "sightings": 8 }, { "year": "1949", "sightings": 15 }, { "year": "1950", "sightings": 25 }, { "year": "1951", "sightings": 20 }, { "year": "1952", "sightings": 48 }, { "year": "1953", "sightings": 34 }, { "year": "1954", "sightings": 50 }, { "year": "1955", "sightings": 31 }, { "year": "1956", "sightings": 38 }, { "year": "1957", "sightings": 67 }, { "year": "1958", "sightings": 40 }, { "year": "1959", "sightings": 47 }, { "year": "1960", "sightings": 64 }, { "year": "1961", "sightings": 39 }, { "year": "1962", "sightings": 55 }, { "year": "1963", "sightings": 75 }, { "year": "1964", "sightings": 77 }, { "year": "1965", "sightings": 167 }, { "year": "1966", "sightings": 169 }, { "year": "1967", "sightings": 178 }, { "year": "1968", "sightings": 183 }, { "year": "1969", "sightings": 138 }, { "year": "1970", "sightings": 126 }, { "year": "1971", "sightings": 110 }, { "year": "1972", "sightings": 146 }, { "year": "1973", "sightings": 209 }, { "year": "1974", "sightings": 241 }, { "year": "1975", "sightings": 279 }, { "year": "1976", "sightings": 246 }, { "year": "1977", "sightings": 239 }, { "year": "1978", "sightings": 301 }, { "year": "1979", "sightings": 221 }, { "year": "1980", "sightings": 211 }, { "year": "1981", "sightings": 146 }, { "year": "1982", "sightings": 182 }, { "year": "1983", "sightings": 132 }, { "year": "1984", "sightings": 172 }, { "year": "1985", "sightings": 192 }, { "year": "1986", "sightings": 173 }, { "year": "1987", "sightings": 193 }, { "year": "1988", "sightings": 203 }, { "year": "1989", "sightings": 220 }, { "year": "1990", "sightings": 217 }, { "year": "1991", "sightings": 210 }, { "year": "1992", "sightings": 228 }, { "year": "1993", "sightings": 285 }, { "year": "1994", "sightings": 381 }, { "year": "1995", "sightings": 1336 }, { "year": "1996", "sightings": 862 }, { "year": "1997", "sightings": 1248 }, { "year": "1998", "sightings": 1812 }, { "year": "1999", "sightings": 2906 }, { "year": "2000", "sightings": 2780 }, { "year": "2001", "sightings": 3105 }, { "year": "2002", "sightings": 3176 }, { "year": "2003", "sightings": 3896 }, { "year": "2004", "sightings": 4208 }, { "year": "2005", "sightings": 3996 }, { "year": "2006", "sightings": 3590 }, { "year": "2007", "sightings": 4195 }, { "year": "2008", "sightings": 4705 }, { "year": "2009", "sightings": 4297 }, { "year": "2010", "sightings": 2531 }])- Python
Data can be provided as a list of lists (e.g. [[1,2,3], [4,5,6]]), or list of dicts (e.g. [{"a": 1, "b": 2}, {"a": 3, "b": 4}]). If a list of dicts is provided, the keys will be used as the column names.
Data can be downloaded as a CSV file from the table visualization. This can be very handy for generating CSV files without additional boilerhead code.
Charts
Area Charts
ufo_sightings = [{"year": "2009", "sightings": 4297}, [...], {"year": "2010", "sightings": 2531}]
sightings_by_year = [s["sightings"] for s in ufo_sightings]
sv.visualize(sightings_by_year)- Python
- JS
Pie Charts
Coming soon
Text
Text
Preview text. Will format with markdown
Raw Data
Show data as raw values with no formatting
Numbers
Numbers
Numbers can be visualized with a dedicated layout that resizes to fit the provided number:
Progress
Show a progress bar displaying 0-100% value. Can parse a wide variety of text, including strings, integers, and 0.0-1.0 floats.