# Auto-generated by leda
import leda
leda.set_interact_mode(leda.StaticIpywidgetsInteractMode())
# Auto-generated by leda
import os
from leda.vendor.static_ipywidgets.static_ipywidgets import interact as static_interact
static_interact.IMAGE_MANAGER = static_interact.InlineImageManager()
import dataclasses
import leda
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
leda.init("matplotlib")
plt.rcParams.update({"figure.max_open_warning": 0})
%%HTML
<!-- Auto-generated by leda -->
<h2>Table of Contents</h2>
<ol type='I'>
<li type='I'><a href='#leda-demo:-matplotlib'>leda demo: matplotlib</a></li>
<ol type='A'>
<li type='A'><a href='#Info'>Info</a></li>
<li type='A'><a href='#Data'>Data</a></li>
<li type='A'><a href='#Visualization'>Visualization</a></li>
<ol type='1'>
<li type='1'><a href='#Simple'>Simple</a></li>
<li type='1'><a href='#Objects-as-Params'>Objects as Params</a></li>
</ol>
</ol>
</ol>
Widgets
Use the %%interact expr0;expr1;...
cell magic to set widgets for that cell.
Each expression is of the form x=y
, where x
becomes the local var of the cell and y
can be a:
list
to indicate choices for a dropdown widgettuple
to indicate values for an int slider (start, stop, and optional step).E.g.:
%%interact column=list(df.columns)
%%interact column=list(df.columns);mult=[1, 2, 3]
%%interact column=list(df.columns);window=(10, 50)
%%interact column=list(df.columns);window=(10, 50, 5)
Table of Contents
Use the %toc
line magic to substitute with a table of contents in static mode.
Toggles
Click the Toggle input cells
button at the bottom to reveal input cells.
Using randomly generated data (with fixed seed).
df = pd.DataFrame(np.random.RandomState(42).rand(100, 10), columns=list("abcdefghij"))
%%interact column=list(df.columns);mult=[1, 2, 3]
(df[[column]] * mult).plot(figsize=(15, 8), lw=2, title=f"column={column!r}, mult={mult}")
Generating results: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:00<00:00, 47.39it/s] Generating HTML: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:04<00:00, 6.95it/s]
%%interact column=list(df.columns);window=(10, 50, 5)
ax = df[[column]].iloc[-window:].plot(figsize=(15, 8), lw=2,
title=f"column={column!r}, window={window}")
ax
Generating results: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:02<00:00, 41.32it/s] Generating HTML: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:12<00:00, 7.49it/s]
@dataclasses.dataclass(frozen=True)
class Calculator:
def calc(self, df: pd.DataFrame) -> pd.DataFrame:
raise NotImplementedError
@dataclasses.dataclass(frozen=True)
class CumSumCalculator(Calculator):
def calc(self, df: pd.DataFrame) -> pd.DataFrame:
return df.cumsum()
@dataclasses.dataclass(frozen=True)
class EWMMeanCalculator(Calculator):
com: float
def calc(self, df: pd.DataFrame) -> pd.DataFrame:
return df.ewm(com=self.com).mean()
calcs = [CumSumCalculator(), EWMMeanCalculator(com=5), EWMMeanCalculator(com=10)]
%%interact column_group=["abc", "def", "ghij"];calc=calcs
calced_df = calc.calc(df[list(column_group)])
calced_df.plot(figsize=(15, 8), lw=2, title=f"column_group={column_group!r}, calc={calc}")
Generating results: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 46.43it/s] Generating HTML: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:01<00:00, 6.73it/s]
# Auto-generated by leda
import leda
leda.show_input_toggle()
# Auto-generated by leda
import leda
leda.show_std_output_toggle()