tomron’s gists

tomron / barchart_stwd.py

Created November 21, 2024 19:07

Bar Chart based on Storytelling With Data - better bar chart visualization

	from typing import Any
	import pandas as pd

	import plotly.graph_objects as go
	import pandas as pd


	def barchart(
	df: pd.DataFrame, x_col: str, y_col: str,
	title: str \| None = None,

tomron / missleading_plots.py

Created September 15, 2022 10:11

Think outside of the box plot - code accompanying my talk in DataTLV about box plots

	nimport numpy as np
	import pandas as pd
	import sys
	import plotly.graph_objects as go
	from plotly.subplots import make_subplots
	import plotly
	import plotly.express as px

	import matplotlib.pyplot as plt

tomron / csv_to_radar.py

Last active January 28, 2025 06:45

CSV to radar plot - turns csv file into a radar plot that can be exported or shown in the browser

	import plotly.graph_objects as go
	import plotly.offline as pyo

	import pandas as pd

	import argparse
	import sys


	def parse_arguments(args):

tomron / __init__.py

Created October 22, 2021 07:31

Other pie - creat a pie chart with the top `n-1` values as separate sectors and other sector for the remaining values

	"""
	`plotly.express` is a terse, consistent, high-level wrapper around `plotly.graph_objects`
	for rapid data exploration and figure generation. Learn more at https://plotly.express/
	"""
	from __future__ import absolute_import
	from plotly import optional_imports

	pd = optional_imports.get_module("pandas")
	if pd is None:
	raise ImportError(

tomron / missing_values_read_csv.py

Last active August 15, 2021 06:57

How to deal with non trivial missing values when using pandas read_csv

	import pandas as pd
	import numpy as np
	import time

	url = "http://archive.ics.uci.edu/ml/machine-learning-databases/mammographic-masses/mammographic_masses.data"
	names = ['BI-RADS', 'Age', 'Shape', 'Margin', 'Density', 'Severity']


	def manual_convert():
	df = pd.read_csv(url, names=names)

tomron / plotly_bar_chart_links.py

Created November 17, 2020 07:17

Add links to Plotly bar chart

	import pandas as pd
	import plotly.graph_objects as go

	df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv")

	df = df.sort_values(by='GDP (BILLIONS)', ascending=False).head(10)

	fig = go.Figure([go.Bar(x=list(range(len(df))), y=df['GDP (BILLIONS)'])])

	fig.update_xaxes(tickmode = 'array',

tomron / plotly_back_to_back_chart.py

Last active May 31, 2021 18:58

Back to back bar chart with Plotly

	import numpy as np
	import matplotlib.pyplot as plt
	import plotly.graph_objects as go

	women_pop = np.array([5., 30., 45., 22.])
	men_pop = np.array( [5., 25., 50., 20.])
	y = list(range(len(women_pop)))

	fig = go.Figure(data=[
	go.Bar(y=y, x=women_pop, orientation='h', name="women", base=0),

tomron / seasonal_decompose_plotly.py

Last active August 6, 2024 13:33

A nicer seasonal decompose chart using plotly.

	from statsmodels.tsa.seasonal import seasonal_decompose
	import plotly.tools as tls

	def plotSeasonalDecompose(
	x,
	model='additive',
	filt=None,
	period=None,
	two_sided=True,
	extrapolate_trend=0,

tomron / networkx_post.py

Created January 17, 2020 19:02

Code for post about networkx features - https://tomron.net/2020/01/17/3-interesting-features-of-networkx/

	import networkx as nx
	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt


	# Multigraph example

	G = nx.MultiGraph()
	G.add_nodes_from([1, 2, 3])

tomron / sprt.py

Last active April 30, 2019 09:58

Sequential probability ratio test implementation (https://en.wikipedia.org/wiki/Sequential_probability_ratio_test) for exponential distribution. Usage - `t = sprt.SPRT(0.05, 0.8, 1, 2); t.test([1, 2, 3, 4, 5])`

	import numpy as np

	"""
	Implements Sequential probability ratio test
	https://en.wikipedia.org/wiki/Sequential_probability_ratio_test

	"""
	class SPRT:

	def __init__(self, alpha, beta, mu0, mu1):

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