Introduction 1
- Lecture1.1
  
  Introduction
Getting the Data 4
- Lecture2.1
  
  Introduction
- Lecture2.2
  
  The Data
- Lecture2.3
  
  Merging Data
- Lecture2.4
  
  Merging Data Part 2
Location Groups 3
- Lecture3.1
  
  Introduction
- Lecture3.2
  
  Grid Basics
- Lecture3.3
  
  Building the Grid
Creating the New Data 3
- Lecture4.1
  
  Introduction
- Lecture4.2
  
  Location Codes
- Lecture4.3
  
  Boundary Lines
Mapping the Data 3
- Lecture5.1
  
  Introduction
- Lecture5.2
  
  Map Basics
- Lecture5.3
  
  Finishing the Map

Location Codes

Let’s import libraries, and open our data up.

import pandas as pd
import numpy as np
import gmplot
Assess_Combined = pd.DataFrame.from_csv("Assess_Combined.csv",encoding="UTF-8")

Now, we want a way to represent both the latitude and longitude together. Here is how we will do it.

print(Assess_Combined["Lat Group"].astype(str)+"-"+Assess_Combined["Lon Group"].astype(str))

First off, we have to use .astype(str) to convert our integer groups to strings. We are then putting the two together with a dash to seperate them. Let’s assign this to a new column which we will call loc code.

Assess_Combined["LocCode"] = Assess_Combined["Lat Group"].astype(str)+"-"+Assess_Combined["Lon Group"].astype(str)

Challenge

Turn our data into a dataset of each location code, and their average values for each variable.

Prev Introduction

Next Boundary Lines

Data Science

Challenge

Leave A Reply Cancel reply

Data Science

Introduction 1

Getting the Data 4

Location Groups 3

Creating the New Data 3

Mapping the Data 3

Location Codes

Challenge

Leave A Reply Cancel reply

Login with your site account

Register a new account