Adding data

Data types

Vizzu currently supports two types of data series: dimensions and measures. Dimensions slice the data cube Vizzu uses, whereas measures are values within the cube.

Dimensions are categorical series that can contain strings and numbers, but both will be treated as strings. Temporal data such as dates or timestamps should also be added as dimensions. By default, Vizzu will draw the elements on the chart in the order they are provided in the data set. Thus we suggest adding temporal data in a sorted format from oldest to newest.

Measures at the moment can only be numerical.

Adding data

There are multiple ways you can add data to ipyvizzu:

• Using pandas DataFrame - More info
• Specify data by series - column after column if you think of a spreadsheet. - More info
• Specify data by records - row after row. - More info
• Data cube form - More info
• JSON - More info

Using pandas DataFrame

Note: Data().add_data_frame() arguments are:

• data_frame (mandatory): pandas DataFrame object
• default_measure_value (optional, default: 0): ipyvizzu fills empty measure values with this number.
• default_dimension_value (optional, default: ""): ipyvizzu fills the empty dimension values with this string.

Note: ipyvizzu makes a difference between two types of data, numeric (measure) and not numeric (dimension). A column's dtype specifies that the column is handled as a measure or as a dimension.

import pandas as pd

from ipyvizzu import Data

data_frame = pd.DataFrame(
    {
        "Genres": [
            "Pop",
            "Rock",
            "Jazz",
            "Metal",
            "Pop",
            "Rock",
            "Jazz",
            "Metal",
            "Pop",
            "Rock",
            "Jazz",
            "Metal",
        ],
        "Kinds": [
            "Hard",
            "Hard",
            "Hard",
            "Hard",
            "Smooth",
            "Smooth",
            "Smooth",
            "Smooth",
            "Experimental",
            "Experimental",
            "Experimental",
            "Experimental",
        ],
        "Popularity": [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, 94, 58],
    }
)

data_pd = Data()
data_pd.add_data_frame(data_frame)

It is also possible to add pandas.DataFrame.index to Data() with the Data().add_data_frame_index() function. Note: Data().add_data_frame_index() arguments are:

• data_frame (mandatory): pandas DataFrame object
• name (mandatory): name of the index series

import pandas as pd

from ipyvizzu import Data

data_frame = pd.DataFrame({"Popularity": [114, 96, 78]}, index=["x", "y", "z"])

data_pd = Data()
data_pd.add_data_frame_index(data_frame, "DataFrameIndex")
data_pd.add_data_frame(data_frame)

When you specify the data by series or by records, it has to be in first normal form. Here is an example of that:

Genres	Kinds	Popularity
Pop	Hard	114
Rock	Hard	96
Jazz	Hard	78
Metal	Hard	52
Pop	Smooth	56
Rock	Smooth	36
Jazz	Smooth	174
Metal	Smooth	121
Pop	Experimental	127
Rock	Experimental	83
Jazz	Experimental	94
Metal	Experimental	58

Data specified by series

from ipyvizzu import Data

data_series = Data()
data_series.add_series(
    "Genres",
    [
        "Pop",
        "Rock",
        "Jazz",
        "Metal",
        "Pop",
        "Rock",
        "Jazz",
        "Metal",
        "Pop",
        "Rock",
        "Jazz",
        "Metal",
    ],
    type="dimension",
)
data_series.add_series(
    "Kinds",
    [
        "Hard",
        "Hard",
        "Hard",
        "Hard",
        "Smooth",
        "Smooth",
        "Smooth",
        "Smooth",
        "Experimental",
        "Experimental",
        "Experimental",
        "Experimental",
    ],
    type="dimension",
)
data_series.add_series(
    "Popularity", [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, 94, 58], type="measure"
)

Data specified by records

from ipyvizzu import Data

data_records = Data()

data_records.add_series("Genres", type="dimension")
data_records.add_series("Kinds", type="dimension")
data_records.add_series("Popularity", type="measure")

record = ["Pop", "Hard", 114]

data_records.add_record(record)

records = [
    ["Rock", "Hard", 96],
    ["Jazz", "Hard", 78],
    ["Metal", "Hard", 52],
    ["Pop", "Smooth", 56],
    ["Rock", "Smooth", 36],
    ["Jazz", "Smooth", 174],
    ["Metal", "Smooth", 121],
    ["Pop", "Experimental", 127],
    ["Rock", "Experimental", 83],
    ["Jazz", "Experimental", 94],
    ["Metal", "Experimental", 58],
]

data_records.add_records(records)

Data cube

		Genres
		Pop	Rock	Jazz	Metal
Kinds	Hard	114	96	78	52
	Smooth	56	36	74	121
	Experimental	127	83	94	58
		Popularity

from ipyvizzu import Data

data_cube = Data()

data_cube.add_dimension("Genres", ["Pop", "Rock", "Jazz", "Metal"])
data_cube.add_dimension("Kinds", ["Hard", "Smooth", "Experimental"])

data_cube.add_measure(
    "Popularity",
    [
        [114, 96, 78, 52],
        [56, 36, 174, 121],
        [127, 83, 94, 58],
    ],
)

JSON

Content of ./music_data.json (in this example the data stored in the Data Cube format):

{
    "dimensions": [
        {"name": "Genres", "values": [ "Pop", "Rock", "Jazz", "Metal"]},
        {"name": "Kinds", "values": [ "Hard", "Smooth", "Experimental"]}
    ],
    "measures": [
        {
            "name": "Popularity",
            "values":  [
                [114, 96, 78, 52],
                [56, 36, 174, 121],
                [127, 83, 94, 58]
            ]
        }
    ]
}

from ipyvizzu import Data

data_json = Data.from_json("./music_data.json")

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