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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 |
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import plotly.graph_objects as go | |
import plotly.offline as pyo | |
import pandas as pd | |
import argparse | |
import sys | |
def parse_arguments(args): |
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""" | |
`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( |
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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) |
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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', |
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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), |
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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, |
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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]) |
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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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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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