FrankRuns

You have a CSV file called `locations.csv` with columns: name, longitude, latitude, type (including 'Customer' rows), DCs, and plants. 

I want you to:

1. Filter the data to only include rows where `type == 'Customer'`.

2. Generate synthetic one-period demand for these customers:

   - Normal scenario: Draw from a normal distribution (mean=100, std=20), clip negatives at 0.

FrankRuns

Generate a Python Script for [Project Objective] Visualization with [Visualization Tools] in a Jupyter Notebook

Body:
Objective:

Clearly describe the purpose of the project, the type of data involved, and the key insights or lessons you aim to convey through visualization. Mention whether you have an existing dataset or need to generate synthetic data.

Example:
Create a Python script to visualize supply chain network scenarios using Folium maps. The visualization should compare an optimal distribution strategy (multiple Distribution Centers) versus a suboptimal one (single Distribution Center) to highlight the impact on costs and delivery times. If no data file is provided, generate synthetic data for Distribution Centers (DCs) and Customers.

FrankRuns

Write a Python script to generate synthetic supply chain data with the following rules: 

Here is the detailed description based on the supply chain network:

Distribution Centers (DCs)
Washington, DC:
Located in the northeastern United States near major population centers.
Likely serves as a key hub for East Coast distribution.
Dallas, TX:
Positioned centrally in the southern United States.

FrankRuns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
from scipy.spatial.distance import cdist
import matplotlib.cm as cm

# Set random seed for reproducibility
np.random.seed(42)

FrankRuns

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import dowhy
from dowhy import CausalModel
import networkx as nx
import math
import sklearn 
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

FrankRuns

# Replicate Bob's results from this LinkedIn post:
# https://www.linkedin.com/posts/bob-wilson-77a22ab_people-sometimes-say-ab-testing-requires-activity-7152792859878871040-X1Sr?utm_source=share&utm_medium=member_desktop


### Implement Fisher's Exact Test

# Create the contingency table
contingency_table <- matrix(c(0, 4, 7, 3), nrow = 2)
dimnames(contingency_table) <- list(c("Control", "Treatment"),

FrankRuns

# Load Required Libraries
if (!require("MASS")) install.packages("MASS")
library(MASS)

# Define TV Shows
# A vector of TV show titles
tv_shows <- c(
  "Breaking Bad", "Game of Thrones", "The Wire", 
  "Stranger Things", "The Crown", "Mad Men", 
  "The Sopranos", "Friends", "The Office", 

FrankRuns

# load packages
library(tidyverse)
library(tidytuesdayR) # Used for loading datasets from the TidyTuesday project

# load datasets
tuesdata <- tidytuesdayR::tt_load('2023-11-28')
drwho_episodes <- tuesdata$drwho_episodes
drwho_directors <- tuesdata$drwho_directors
drwho_writers <- tuesdata$drwho_writers

FrankRuns

# purpose: visualize linear trend for all data and subset of data

# libraries
library(dplyr)
library(ggplot2)

# read data 
d <- read.csv("mycsvfile.csv")

# quickl look

FrankRuns

# purpose: helper script to determine my max heart rate

# load libraries
library(dplyr)
library(rethinking)

#### Read and Filter Data ----

# grab data