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.

Installation

ipysheet lässt sich einfach mit Pipenv installieren:

$ pipenv install ipysheet

Import

[1]:
import ipysheet

Zellformatierung

[2]:
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

[3]:
from ipywidgets import FloatSlider, IntSlider, Image

slider = FloatSlider()
sheet2 = ipysheet.sheet()
cell1 = ipysheet.cell(0, 0, slider, style={'min-width': '122px'})
cell3 = ipysheet.cell(1, 0, 42., numeric_format='0.00')
cell_sum = ipysheet.cell(2, 0, 42., numeric_format='0.00')

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

sheet2

Numpy

[4]:
import numpy as np
from ipysheet import from_array, to_array

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

sheet = from_array(arr)
sheet
[5]:
arr = np.array([True, False, True])

sheet = from_array(arr)
sheet
[6]:
to_array(sheet)
[6]:
array([[ True],
       [False],
       [ True]])

Tabellensuche

[7]:
import numpy as np
import pandas as pd
from ipysheet import from_dataframe
from ipywidgets import Text, VBox, link

df = pd.DataFrame({'A': 1.,
                   '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

[8]:
import numpy as np
from traitlets import link
from ipywidgets import HBox
import bqplot.pyplot as plt
from ipysheet import sheet, cell, column

size = 18
scale = 100.
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))

Zum Weiterlesen