Data

Data types

ipyvizzu currently supports two types of data series: dimensions and measures. Dimensions slice the data cube ipyvizzu 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, ipyvizzu 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
Specify data by series - column after column if you think of a spreadsheet
Specify data by records - row after row
Using data cube form
Using JSON

Tip

You should set the data in the first animate call.

chart.animate(data)

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

Using `pandas` DataFrame

Use add_df method for adding pandas DataFrame to Data.

import pandas as pd
from ipyvizzu import Data


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

data = Data()
data.add_df(df)

Note

There is a max_rows limit of 100k for dataframes to prevent potential browser memory issues. If your dataframe surpasses this limit, it will be randomly sampled down. The default value can be adjusted via the max_rows parameter of the add_df function.

df = pd.DataFrame(data)

data = Data()
data.add_df(df, max_rows=110000)

Info

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.

It is also possible to add the data frame's index as a series column while adding the data frame

import pandas as pd
from ipyvizzu import Data


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

data = Data()
data.add_df(df, include_index="IndexColumnName")

or later with the add_df_index method.

import pandas as pd
from ipyvizzu import Data


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

data = Data()
data.add_df_index(df, column_name="IndexColumnName")
data.add_df(df)

Note

If you want to work with pandas DataFrame and ipyvizzu, you need to install pandas or install it as an extra:

pip install ipyvizzu[pandas]

Using csv

Download music_data.csv here.

import pandas as pd
from ipyvizzu import Data


df = pd.read_csv(
    "https://ipyvizzu.vizzuhq.com/0.17/assets/data/music_data.csv"
)

data = Data()
data.add_df(df)

Using Excel spreadsheet

Download music_data.xlsx here.

import pandas as pd
from ipyvizzu import Data


df = pd.read_excel(
    "https://ipyvizzu.vizzuhq.com/0.17/assets/data/music_data.xlsx"
)

data = Data()
data.add_df(df)

Using Google Sheets

import pandas as pd
from ipyvizzu import Data


google_sheet_id = "<Google Sheet id>"
worksheet_name = "<Worksheet name>"

df = pd.read_csv(
    f"https://docs.google.com/spreadsheets/d/{google_sheet_id}/gviz/tq?tqx=out:csv&sheet={worksheet_name}"
)

data = Data()
data.add_df(df)

For example if the url is https://docs.google.com/spreadsheets/d/abcd1234/edit#gid=0 then google_sheet_id here is abcd1234.

Using SQLite

import pandas as pd
import sqlite3
from ipyvizzu import Data


# establish a connection to the SQLite database
conn = sqlite3.connect("mydatabase.db")
# read data from a SQLite table into a pandas DataFrame
df = pd.read_sql("SELECT * FROM mytable", conn)
# close the connection
conn.close()

data = Data()
data.add_df(df)

Note

You'll need to adjust the SQL query and the database connection parameters to match your specific use case.

Using MySQL

import pandas as pd
import mysql.connector
from ipyvizzu import Data


# establish a connection to the MySQL database
conn = mysql.connector.connect(
    user="myusername",
    password="mypassword",
    host="myhost",
    database="mydatabase",
)
# read data from a MySQL table into a pandas DataFrame
df = pd.read_sql("SELECT * FROM mytable", con=conn)
# close the connection
conn.close()

data = Data()
data.add_df(df)

Note

You'll need to adjust the SQL query and the database connection parameters to match your specific use case.

Using PostgreSQL

import pandas as pd
import psycopg2
from ipyvizzu import Data


# establish a connection to the PostgreSQL database
conn = psycopg2.connect(
    user="myusername",
    password="mypassword",
    host="myhost",
    port="5432",
    database="mydatabase",
)
# read data from a PostgreSQL table into a pandas DataFrame
df = pd.read_sql("SELECT * FROM mytable", con=conn)
# close the connection
conn.close()

data = Data()
data.add_df(df)

Note

You'll need to adjust the SQL query and the database connection parameters to match your specific use case.

Using Microsoft SQL Server

import pandas as pd
import pyodbc
from ipyvizzu import Data


# establish a connection to the Microsoft SQL Server database
conn = pyodbc.connect(
    "Driver={SQL Server};"
    "Server=myserver;"
    "Database=mydatabase;"
    "UID=myusername;"
    "PWD=mypassword"
)
# read data from a SQL Server table into a pandas DataFrame
df = pd.read_sql("SELECT * FROM mytable", con=conn)
# close the connection
conn.close()

data = Data()
data.add_df(df)

Note

You'll need to adjust the SQL query and the database connection parameters to match your specific use case.

Using `pyspark` DataFrame

Use add_df method for adding pyspark DataFrame to Data.

from pyspark.sql import SparkSession
from pyspark.sql.types import (
    StructType,
    StructField,
    StringType,
    IntegerType,
)
from ipyvizzu import Data


spark = SparkSession.builder.appName("ipyvizzu").getOrCreate()
spark_schema = StructType(
    [
        StructField("Genres", StringType(), True),
        StructField("Kinds", StringType(), True),
        StructField("Popularity", IntegerType(), True),
    ]
)
spark_data = [
    ("Pop", "Hard", 114),
    ("Rock", "Hard", 96),
    ("Jazz", "Hard", 78),
    ("Metal", "Hard", 52),
    ("Pop", "Smooth", 56),
    ("Rock", "Experimental", 36),
    ("Jazz", "Smooth", 174),
    ("Metal", "Smooth", 121),
    ("Pop", "Experimental", 127),
    ("Rock", "Experimental", 83),
    ("Jazz", "Experimental", 94),
    ("Metal", "Experimental", 58),
]
df = spark.createDataFrame(spark_data, spark_schema)

data = Data()
data.add_df(df)

Note

If you want to work with pyspark DataFrame and ipyvizzu, you need to install pyspark or install it as an extra:

pip install ipyvizzu[pyspark]

Using `numpy` Array

Use add_np_array method for adding numpy Array to Data.

import numpy as np
from ipyvizzu import Data


numpy_array = np.array(
    [
        ["Pop", "Hard", 114],
        ["Rock", "Hard", 96],
        ["Jazz", "Hard", 78],
        ["Metal", "Hard", 52],
        ["Pop", "Smooth", 56],
        ["Rock", "Experimental", 36],
        ["Jazz", "Smooth", 174],
        ["Metal", "Smooth", 121],
        ["Pop", "Experimental", 127],
        ["Rock", "Experimental", 83],
        ["Jazz", "Experimental", 94],
        ["Metal", "Experimental", 58],
    ]
)

data = Data()
data.add_np_array(
    numpy_array,
    column_name={0: "Genres", 1: "Kinds", 2: "Popularity"},
    column_dtype={2: int},
)

Info

Arrays with dimensions higher than 2 are not supported.
If column_name dictionary is not added, column indices will be used as names.
If column_dtype dictionary is not added, every column will use numpy_array.dtype.

Note

If you want to work with numpy Array and ipyvizzu, you need to install numpy or install it as an extra:

pip install ipyvizzu[numpy]

Specify data by series

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

from ipyvizzu import Data


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

Specify data by records

from ipyvizzu import Data


data = Data()

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

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

data.add_record(record)

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

data.add_records(records)

Where records can be lists as shown above or dictionaries:

records = [
    {
        "Genres": "Pop",
        "Kinds": "Hard",
        "Popularity": 114,
    },
    {
        "Genres": "Rock",
        "Kinds": "Hard",
        "Popularity": 96,
    },
    # ...
]

Using data cube form

Note

In the example below, the record Rock,Experimental,36 has been replaced with Rock,Smooth,36 in order to illustrate that only data with same dimensions can be used in the data cube form.

		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 = Data()

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

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

Using JSON

Download music_data.json here (in this example the data stored in the data cube form).

from ipyvizzu import Data


data = Data.from_json("../assets/data/music_data.json")

Data

Data types

Adding data

Using pandas DataFrame

Using csv

Using Excel spreadsheet

Using Google Sheets

Using SQLite

Using MySQL

Using PostgreSQL

Using Microsoft SQL Server

Using pyspark DataFrame

Using numpy Array

Specify data by series

Specify data by records

Using data cube form

Using JSON

Using `pandas` DataFrame

Using `pyspark` DataFrame

Using `numpy` Array