joseberlines

import numpy as np
import pandas as pd
from ipydatagrid import DataGrid,TextRenderer
import ipydatagrid as grid
print(grid.__version__)

df = pd.DataFrame(
    data={
        "Col1HasAVeryLongName": ['qwerwqerqweddg  d dgggfgfg  dg dg drwqerwqer','qwer  wer  qwerdg wqer','asdf  asdfasdasdfsadf'],
        "Col2MediumName": [4, 5, 6],

joseberlines

import ipyvuetify as vue
from ipywidgets import Output
from IPython.display import Javascript

b1 = vue.Btn(class_='mx-2 light-blue darken-1', color='primary',children=['open lemonde'])
b2 = vue.Btn(class_='mx-2 light-blue darken-1', color='primary',children=['open nyt'])
b3 = vue.Btn(class_='mx-2 light-blue darken-1', color='primary',children=['open all'])
out=Output()

def open_lemonde(widget, event, data):

joseberlines

# THE DATA 
# L is a list of dicts (all have the same keys)
L=[{'name':'e'+str(i), 
    'explanation':'objection1',
    'law':(),
    'CS':('barcelona','Vigo')} for i in range(1000000)]


# option 1: generator
next(item for item in L if item["name"] == myvalue)

joseberlines

L=[{'name':'e'+str(i), 
    'explanation':'objection1',
    'law':(),
    'CS':('barcelona','Vigo')} for i in range(1000000)]

#%%timeit
next(item for item in L if item["name"] == "e300000")
# 22.3 ms ± 2.58 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

#%%timeit

joseberlines

# 100000 elements list
L=[{'name':'e'+str(i), 'explanation':'objection1','law':(),'CS':('..claim','..notclear')} for i in range(100000)]

next(item for item in L if item["name"] == "e3")
# 716 ns ± 62.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

[d for d in L if d['name']=='e3'][0]
# 6.66 ms ± 520 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

joseberlines

df = pd.DataFrame(L).set_index('name')


df.loc[['e4']].reset_index().to_dict('records')
#1.09 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


df.loc[['e9']].reset_index().to_dict('records')
#1.1 ms ± 22.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

joseberlines

import pandas as pd
# list of 1000

L=[{'name':'e'+str(i), 
    'explanation':'objection1',
    'law':(),
    'CS':('Barcelona','Vigo')} for i in range(1000)]

#%%timeit
next(item for item in L if item["name"] == "e500")

joseberlines

# list of 1000 dictionaries
L=[{'name':'e'+str(i),
    'explanation':'objection1',
    'law':(),
    'CS':('Barcelona','Vigo')} for i in range(1000)]

joseberlines

rail_lines = [{'id': 'line1', 'source': 'BER', 'target': 'MUN', 'speed': '200km/h'},
         {'id': 'line2', 'source': 'MUN', 'target': 'FRA', 'speed': '200km/h'},
         {'id': 'line3', 'source': 'FRA', 'target': 'BER', 'speed': '250km/h'}, 
         {'id': 'line4', 'source': 'BER', 'target': 'HAM', 'speed': '300km/h'}, 
         {'id': 'line5', 'source': 'BER', 'target': 'LEP', 'speed': '300km/h'},
         {'id': 'line6', 'source': 'NUR', 'target': 'LEP', 'speed': '150km/h'}, 
         {'id': 'line7', 'source': 'NUR', 'target': 'FRA', 'speed': '150km/h'},
         {'id': 'line8', 'source': 'BER', 'target': 'PAR', 'speed': '400km/h'}, 
         {'id': 'line9', 'source': 'PAR', 'target': 'LYO', 'speed': '400km/h'}, 
         {'id': 'line10', 'source': 'LYO', 'target': 'BAR', 'speed': '400km/h'},

joseberlines

rails_df['speed_int']=rails_df['speed'].str.replace('km/h','').astype('int')
min_speed = rails_df['speed_int'].min()
max_speed = rails_df['speed_int'].max()
span_speed = max_speed-min_speed

rails_df['score'] = rails_df['speed_int'].apply(lambda x: (x-min_speed)/span_speed)
rails_df['width'] = rails_df['score'] *7 +5

G=transform_into_ipycytoscape(stations_df,rails_df)
display(G)