CanvasXpress Matrix Data Options

Import Core Packages for this Demo

from canvasxpress.canvas import CanvasXpress
from canvasxpress.render.jupyter import CXNoteBook
import pandas as pd
import requests
from uuid import uuid4
import json

Common Chart Components

Establish essential configurations to avoid chart creation repetition:

mpg_cx_config = {
    "aes": {
        "x": "displ",
        "y": "hwy",
        "color": "class"
    },
    "colorBy": "class",
    "graphOrientation": "vertical",
    "graphType": "Scatter2D",
    "isCxplot": True,
    "remoteTransitionEffect": "none",
    "smpLabelRotate": 90,
    "sortOnGrouping": "ascending",
    "theme": "GGPlot",
    "transformAxis": False,
    "widthFactor": 2,
    "xAxis": ["displ"],
    "xAxisTitle": "displ",
    "yAxis": ["hwy"],
    "yAxisTitle": "hwy",
}

mpg_cx_after_render = [
    [
        "geom_point",
        [
            {
                "color": "class"
            },
            None,
            None
        ],
    ],
    [
        "gg_labels",
        [
            {
                "xAxisTitle": "displ",
                "yAxisTitle": "hwy"
            }
        ],
    ],
    [
        "drawRangesAfterWheelEvent",
        [None,None,None,None,0],
    ],
    [
        "clickGraphMaxMin",
        [False,False,True],
    ]
]

XYZ data dict

Arrange data into a Python dict formatted to CanvasXpress XYZ.

Show the code

mpg_df = pd.read_csv("https://raw.githubusercontent.com/sidsriv/Introduction-to-Data-Science-in-python/master/mpg.csv", index_col=0)

mpg_xyz_data = {
    "y": {
        "vars": ["displ","hwy"],
        "smps": [i+1 for i in range(mpg_df.index.size)],
        "data": [
            mpg_df["displ"].to_list(),
            mpg_df["hwy"].to_list(),
        ]
    },
    "x": {
        factor: mpg_df[factor].to_list()
        for factor in mpg_df.columns
        if factor not in ["displ", "hwy"]
    },
}

example_df_cx = CanvasXpress(
    render_to=str(uuid4()),
    data=mpg_xyz_data,
    config = mpg_cx_config,
    after_render = mpg_cx_after_render,
    width=500,
    height=400,
)

example_df_cx_renderer = CXNoteBook(example_df_cx)
example_df_cx_renderer.render()

URL pass-through to CanvasXpress

Obtain the CSV data as a URL (text) and pass it through to CanvasXpress:

Show the code

from canvasxpress.data.url import CXUrlData

example_mpg_csv_df = CanvasXpress(
    render_to=str(uuid4()),
    data=CXUrlData("https://raw.githubusercontent.com/sidsriv/Introduction-to-Data-Science-in-python/master/mpg.csv"),
    config = mpg_cx_config,
    after_render = mpg_cx_after_render,
    width=500,
    height=400,
)

example_mpg_csv_df_renderer = CXNoteBook(example_mpg_csv_df)
example_mpg_csv_df_renderer.render()

Raw text pass-through to CanvasXpress

Obtain the CSV data as raw text and pass it through to CanvasXpress for interpretation:

Show the code

from canvasxpress.data.text import CXTextData

mpg_text = requests.get("https://raw.githubusercontent.com/sidsriv/Introduction-to-Data-Science-in-python/master/mpg.csv").content.decode("UTF-8")

example_text_cx = CanvasXpress(
    render_to=str(uuid4()),
    data=CXTextData(mpg_text),
    config = mpg_cx_config,
    after_render = mpg_cx_after_render,
    width=500,
    height=400,
)

example_text_cx_renderer = CXNoteBook(example_text_cx)
example_text_cx_renderer.render()

JSON pass-through to CanvasXpress

Obtain the CSV data as a CSV (text) and transform it for CanvasXpress:

Show the code

from canvasxpress.data.keypair import CXJSONData

mpg_df = pd.read_csv("https://raw.githubusercontent.com/sidsriv/Introduction-to-Data-Science-in-python/master/mpg.csv", index_col=0)

mpg_xyz_data = {
    "y": {
        "vars": ["displ","hwy"],
        "smps": [i+1 for i in range(mpg_df.index.size)],
        "data": [
            mpg_df["displ"].to_list(),
            mpg_df["hwy"].to_list(),
        ]
    },
    "x": {
        factor: mpg_df[factor].to_list()
        for factor in mpg_df.columns
        if factor not in ["displ", "hwy"]
    },
}

example_mpg_json = CanvasXpress(
    render_to=str(uuid4()),
    data=CXJSONData(json.dumps(mpg_xyz_data)),
    config = mpg_cx_config,
    after_render = mpg_cx_after_render,
    width=500,
    height=400,
)

example_mpg_json_renderer = CXNoteBook(example_mpg_json)
example_mpg_json_renderer.render()

Pandas DataFrame with metadata

Use a Pandas DataFrame and pass with it meta X data.

Show the code

from canvasxpress.data.matrix import CXDataframeData

df_all = pd.read_csv("https://raw.githubusercontent.com/sidsriv/Introduction-to-Data-Science-in-python/master/mpg.csv", index_col=0)

mpg_dfd = CXDataframeData(
    (df_all[["displ", "hwy"]]).transpose()
)

mpg_dfd.profile.vars=["displ", "hwy"]
mpg_dfd.profile.x = {
    factor: df_all[factor].to_list()
    for factor in df_all.columns
    if factor not in ["displ", "hwy"]
}

example_dfd_cx = CanvasXpress(
    render_to=str(uuid4()),
    data=mpg_dfd,
    config=mpg_cx_config,
    after_render=mpg_cx_after_render,
    width=500,
    height=400,
)

example_dfd_cx = CXNoteBook(example_dfd_cx)
example_dfd_cx.render()