ipysheet

ipysheet verbindet ipywidgets mit tabellarischen Daten. Es fügt im Wesentlichen zwei Widgets hinzu: ein Cell widget und ein Sheet widget. Darüberhinaus gibt es noch Hilfsfunktionen zum Erstellen von Tabellenzeilen und -spalten sowie zur Formatierung und Gestaltung von Zellen.

Siehe auch:

• Interactive spreadsheets in Jupyter

• GitHub

• Docs

Installation

ipysheet lässt sich einfach mit Pipenv installieren:

$ pipenv install ipysheet

Import

import ipysheet

Zellformatierung

sheet1 = ipysheet.sheet()
cell0 = ipysheet.cell(0, 0, 0, numeric_format="0.0", type="numeric")
cell1 = ipysheet.cell(1, 0, "Hello", type="text")
cell2 = ipysheet.cell(0, 1, 0.1, numeric_format="0.000", type="numeric")
cell3 = ipysheet.cell(1, 1, 15.9, numeric_format="0.00", type="numeric")
cell4 = ipysheet.cell(2, 2, "14-02-2019", date_format="DD-MM-YYYY", type="date")

sheet1

Beispiele

Interaktive Tabelle

from ipywidgets import FloatSlider, Image, IntSlider


slider = FloatSlider()
sheet2 = ipysheet.sheet()
cell1 = ipysheet.cell(0, 0, slider, style={"min-width": "122px"})
cell3 = ipysheet.cell(1, 0, 42.0, numeric_format="0.00")
cell_sum = ipysheet.cell(2, 0, 42.0, numeric_format="0.00")


@ipysheet.calculation(inputs=[(cell1, "value"), cell3], output=cell_sum)
def calculate(a, b):
    return a + b


sheet2

Numpy

import numpy as np

from ipysheet import from_array, to_array


arr = np.random.randn(6, 10)

sheet = from_array(arr)
sheet

arr = np.array([True, False, True])

sheet = from_array(arr)
sheet

to_array(sheet)

array([[ True],
       [False],
       [ True]])

Tabellensuche

import numpy as np
import pandas as pd

from ipysheet import from_dataframe
from ipywidgets import Text, VBox, link


df = pd.DataFrame(
    {
        "A": 1.0,
        "B": pd.Timestamp("20130102"),
        "C": pd.Series(1, index=list(range(4)), dtype="float32"),
        "D": np.array([False, True, False, False], dtype="bool"),
        "E": pd.Categorical(["test", "train", "test", "train"]),
        "F": "foo",
    }
)

df.loc[[0, 2], ["B"]] = np.nan


s = from_dataframe(df)

search_box = Text(description="Search:")
link((search_box, "value"), (s, "search_token"))

VBox((search_box, s))

Plotten editierbarer Tabellen

import bqplot.pyplot as plt
import numpy as np

from ipysheet import cell, column, sheet
from ipywidgets import HBox
from traitlets import link


size = 18
scale = 100.0
np.random.seed(0)
x_data = np.arange(size)
y_data = np.cumsum(np.random.randn(size) * scale)

fig = plt.figure()
axes_options = {
    "x": {"label": "Date", "tick_format": "%m/%d"},
    "y": {"label": "Price", "tick_format": "0.0f"},
}

scatt = plt.scatter(x_data, y_data, colors=["red"], stroke="black")
fig.layout.width = "70%"

sheet1 = sheet(rows=size, columns=2)
x_column = column(0, x_data)
y_column = column(1, y_data)

link((scatt, "x"), (x_column, "value"))
link((scatt, "y"), (y_column, "value"))

HBox((sheet1, fig))