tomron

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

import pandas as pd

import argparse
import sys


def parse_arguments(args):

tomron

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

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

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

# impor spark, set spark context
from pyspark import SparkContext, SparkConf
from pyspark.sql.context import SQLContext
import sys
import os

if len(sys.argv) == 1:
    sys.stderr.write("Must enter input file to convert")
    sys.exit()
input_file = sys.argv[1]

tomron

"""
`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

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

import sys
import time

import boto3

def lambda_handler(event, context):
    conn = boto3.client("emr")
    # chooses the first cluster which is Running or Waiting
    # possibly can also choose by name or already have the cluster id
    clusters = conn.list_clusters()

tomron

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

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',