format line wrapping and fix escaped characters

1 files changed, 46 insertions, 49 deletions

diff --git a/content/blog/2020-09-25-happiness-map.md b/content/blog/2020-09-25-happiness-map.md
index a9ba336..77d9c55 100644
--- a/content/blog/2020-09-25-happiness-map.md
+++ b/content/blog/2020-09-25-happiness-map.md
@@ -8,28 +8,28 @@ draft = false
 # Background Information
 
 The dataset (obtained from
-[Kaggle](https://www.kaggle.com/unsdsn/world-happiness)) used in this
-article contains a list of countries around the world, their happiness
-rankings and scores, as well as other national scoring measures.
+[Kaggle](https://www.kaggle.com/unsdsn/world-happiness)) used in this article
+contains a list of countries around the world, their happiness rankings and
+scores, as well as other national scoring measures.
 
 Fields include:
 
--   Overall rank
--   Country or region
--   GDP per capita
--   Social support
--   Healthy life expectancy
--   Freedom to make life choices
--   Generosity
--   Perceptions of corruption
+- Overall rank
+- Country or region
+- GDP per capita
+- Social support
+- Healthy life expectancy
+- Freedom to make life choices
+- Generosity
+- Perceptions of corruption
 
-There are 156 records. Since there are \~195 countries in the world, we
-can see that around 40 countries will be missing from this dataset.
+There are 156 records. Since there are ~195 countries in the world, we can see
+that around 40 countries will be missing from this dataset.
 
 # Install Packages
 
-As always, run the `install` command for all packages needed
-to perform analysis.
+As always, run the `install` command for all packages needed to perform
+analysis.
 
 ``` python
 !pip install folium geopandas matplotlib numpy pandas
@@ -38,9 +38,9 @@ to perform analysis.
 # Import the Data
 
 We only need a couple packages to create a choropleth map. We will use
-[Folium](https://python-visualization.github.io/folium/), which provides
-map visualizations in Python. We will also use geopandas and pandas to
-wrangle our data before we put it on a map.
+[Folium](https://python-visualization.github.io/folium/), which provides map
+visualizations in Python. We will also use geopandas and pandas to wrangle our
+data before we put it on a map.
 
 ``` python
 # Import the necessary Python packages
@@ -49,14 +49,14 @@ import geopandas as gpd
 import pandas as pd
 ```
 
-To get anything to show up on a map, we need a file that will specify
-the boundaries of each country. Luckily, GeoJSON files exist (for free!)
-on the internet. To get the boundaries of every country in the world, we
-will use the GeoJSON link shown below.
+To get anything to show up on a map, we need a file that will specify the
+boundaries of each country. Luckily, GeoJSON files exist (for free!) on the
+internet. To get the boundaries of every country in the world, we will use the
+GeoJSON link shown below.
 
-GeoPandas will take this data and load it into a dataframe so that we
-can easily match it to the data we\'re trying to analyze. Let\'s look at
-the GeoJSON dataframe:
+GeoPandas will take this data and load it into a dataframe so that we can easily
+match it to the data we're trying to analyze. Let's look at the GeoJSON
+dataframe:
 
 ``` python
 # Load the GeoJSON data with geopandas
@@ -67,9 +67,9 @@ geo_data.head()
 ![GeoJSON
 Dataframe](https://img.cleberg.net/blog/20200925-world-choropleth-map/geojson_df.png)
 
-Next, let\'s load the data from the Kaggle dataset. I\'ve downloaded
-this file, so update the file path if you have it somewhere else. After
-loading, let\'s take a look at this dataframe:
+Next, let's load the data from the Kaggle dataset. I've downloaded this file,
+so update the file path if you have it somewhere else. After loading, let's
+take a look at this dataframe:
 
 ``` python
 # Load the world happiness data with pandas
@@ -82,12 +82,11 @@ Dataframe](https://img.cleberg.net/blog/20200925-world-choropleth-map/happiness_
 
 # Clean the Data
 
-Some countries need to be renamed, or they will be lost when you merge
-the happiness and GeoJSON dataframes. This is something I discovered
-when the map below showed empty countries. I searched both data frames
-for the missing countries to see the naming differences. Any countries
-that do not have records in the `happy_data` df will not show
-up on the map.
+Some countries need to be renamed, or they will be lost when you merge the
+happiness and GeoJSON dataframes. This is something I discovered when the map
+below showed empty countries. I searched both data frames for the missing
+countries to see the naming differences. Any countries that do not have records
+in the `happy_data` df will not show up on the map.
 
 ``` python
 # Rename some countries to match our GeoJSON data
@@ -112,11 +111,11 @@ happy_data.at[democratic_congo_index, 'Country or region'] = 'Democratic Republi
 # Merge the Data
 
 Now that we have clean data, we need to merge the GeoJSON data with the
-happiness data. Since we\'ve stored them both in dataframes, we just
-need to call the `.merge()` function.
+happiness data. Since we've stored them both in dataframes, we just need to
+call the `.merge()` function.
 
-We will also rename a couple columns, just so that they\'re a little
-easier to use when we create the map.
+We will also rename a couple columns, just so that they're a little easier to
+use when we create the map.
 
 ``` python
 # Merge the two previous dataframes into a single geopandas dataframe
@@ -133,10 +132,9 @@ Dataframe](https://img.cleberg.net/blog/20200925-world-choropleth-map/merged_df.
 # Create the Map
 
 The data is finally ready to be added to a map. The code below shows the
-simplest way to find the center of the map and create a Folium map
-object. The important part is to remember to reference the merged
-dataframe for our GeoJSON data and value data. The columns specify which
-geo data and value data to use.
+simplest way to find the center of the map and create a Folium map object. The
+important part is to remember to reference the merged dataframe for our GeoJSON
+data and value data. The columns specify which geo data and value data to use.
 
 ``` python
 # Assign centroids to map
@@ -164,17 +162,16 @@ folium.Choropleth(
 ).add_to(world_map)
 ```
 
-Let\'s look at the resulting map.
+Let's look at the resulting map.
 
 ![Choropleth
 Map](https://img.cleberg.net/blog/20200925-world-choropleth-map/map.png)
 
 # Create a Tooltip on Hover
 
-Now that we have a map set up, we could stop. However, I want to add a
-tooltip so that I can see more information about each country. The
-`tooltip_data` code below will show a popup on hover with all
-the data fields shown.
+Now that we have a map set up, we could stop. However, I want to add a tooltip
+so that I can see more information about each country. The `tooltip_data` code
+below will show a popup on hover with all the data fields shown.
 
 ``` python
     # Adding labels to map
@@ -219,8 +216,8 @@ folium.LayerControl().add_to(world_map)
 world_map
 ```
 
-The final image below will show you what the tooltip looks like whenever
-you hover over a country.
+The final image below will show you what the tooltip looks like whenever you
+hover over a country.
 
 ![Choropleth Map
 Tooltip](https://img.cleberg.net/blog/20200925-world-choropleth-map/tooltip_map.png)