Restaurants with vegetarian options in USA

Francisco Carrillo Pérez — Thu, 07 Sep 2017 09:34:43 +0000

Some months ago, I found this Kaggle's Dataset with restaurants that serve vegetarian and vegan food in the US. I thought I could do something interesting with it.

I've been procrastinating it until last week when I proposed myself that I would use it to learn how to implement a web interactive map.

At first, I plot the data with Plotly to see how it works. It was interesting, but it didn't work as good as I thought, because the dataset is really large.

Then I thought, why not using a JavaScript library and then use it with HTML and CSS for creating a webpage? That's how I found LeafletJS which is an amazing library for plotting geolocation data in a map.

Since the dataset is quite large, I've also used a plugin for LeafletJS that is called MarkerClusterer which cluster the points based in their proximity.

The result could be checked here and all the code could be found in the Github repository.

I hope this could help someone!

Happy coding :)

Paco

Analyzing sugar in McDonald's menu's items

Francisco Carrillo Pérez — Tue, 13 Jun 2017 22:03:53 +0000

Recently I've found a data set in Kaggle which is composed with the nutrition facts of every item in McDonald's Menu (Dataset).

I know for a while that the consumption of sugar in our life is way upon the one recommended by the health care organizations, so I wanted to analyzed how much sugar where in the McDonald menu's items and which of them don't have any kind of added sugar.

For this I've used a Jupyter Notebook with the following libraries: plotly and pandas. Here I'm going to explain the different steps I've followed, but the complete notebook could be checked in my Github Repository.

The information is in a cvs. First let's load the information to see how it is structured:

menu = pd.read_csv('./menu.csv')
menu.head(10)

With this we could see the information of the dataset. This means, the columns and the rows.

Ok, the one I'm interested in is sugar, so I'm going to create a new pandas data frame composed by the column with the item's name and the amount of sugar, and Aldo I'm going to order them in an increasing order:

df_sugars = pd.DataFrame(columns=('Item','Sugars'))
df_sugars['Item'] = menu['Item']
df_sugars['Sugars'] = menu['Sugars']
print("Let's sort them by the amount of sugar they have in a ascending order: ")
df_sugars = df_sugars.sort_values('Sugars', ascending=[True])
print(df_sugars.head(10))

So now that I have this, I want to check which are the menu items that don't have any amount of sugar:

print("Number of items in the menu: "+str(len(menu.index)))
print("Number of items without sugar in the menu: "+str(len(df_sugars.loc[df_sugars['Sugars'] == 0])))
print(df_sugars.loc[df_sugars['Sugars'] == 0])

And I obtain the following result:

Number of items in the menu: 260
Number of items without sugar in the menu: 25
                             Item  Sugars
145                Coffee (Small)       0
99              Kids French Fries       0
96             Small French Fries       0
81   Chicken McNuggets (20 piece)       0
114             Diet Coke (Small)       0
115            Diet Coke (Medium)       0
116             Diet Coke (Large)       0
117             Diet Coke (Child)       0
122        Diet Dr Pepper (Small)       0
123       Diet Dr Pepper (Medium)       0
124        Diet Dr Pepper (Large)       0
98             Large French Fries       0
80   Chicken McNuggets (10 piece)       0
79    Chicken McNuggets (6 piece)       0
136           Dasani Water Bottle       0
137              Iced Tea (Small)       0
138             Iced Tea (Medium)       0
139              Iced Tea (Large)       0
140              Iced Tea (Child)       0
78    Chicken McNuggets (4 piece)       0
146               Coffee (Medium)       0
38                     Hash Brown       0
147                Coffee (Large)       0
125        Diet Dr Pepper (Child)       0
97            Medium French Fries       0

So only 25 elements of 260, which means that only the 9.61% of the items in McDonalds doesn't have any amount of sugar. Now, let's do the plot to see this graphically, for this I'm going to use the Plotly library:

print("Let's start with the bar chart")

data = [go.Bar(
             y = df_sugars['Sugars'].values,
            x = df_sugars['Item'].values,
    )]

py.iplot(data, filename='basic-bar')

Also, I'm going to plot a scatter plot:

# Now let's plot a scatter plot
# This plot is based on the one made by Anisotropic:
# https://www.kaggle.com/arthurtok/super-sized-we-mcdonald-s-nutritional-metrics

trace = go.Scatter(
    y = df_sugars['Sugars'].values,
    x = df_sugars['Item'].values,
    mode='markers',
    marker=dict(
        size= df_sugars['Sugars'].values,
        #color = np.random.randn(500), #set color equal to a variable
        color = df_sugars['Sugars'].values,
        colorscale='Portland',
        showscale=True
    ),
    text = menu['Item'].values
)
data = [trace]

layout= go.Layout(
    autosize= True,
    title= 'Scatter plot of Sugars per Item on the Menu',
    hovermode= 'closest',
    xaxis=dict(
        showgrid=False,
        zeroline=False,
        showline=False
    ),
    yaxis=dict(
        title= 'Sugars(g)',
        ticklen= 5,
        gridwidth= 2,
        showgrid=False,
        zeroline=False,
        showline=False
    ),
    showlegend= False
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig,filename='scatterChol')

The OMS tell that the max amount of sugar per day should be 50g. Let's see the items of the menu go over this threshold:

# First let's add a new column to the dataframe, all equal to 50
df_sugars['Amount of Sugar recommended (g)'] = 50

# Let's plot them

trace1 = go.Bar(
    y = df_sugars['Sugars'].values,
    x = df_sugars['Item'].values,
    name='Sugars(g)'
)
trace2 = go.Bar(
    y = df_sugars['Amount of Sugar recommended (g)'].values,
    x = df_sugars['Item'].values,
    name='Recommended value of sugar OMS (g)'
)

data = [trace1, trace2]
layout = go.Layout(
    barmode='group'
)

layout= go.Layout(
    autosize= True,
    title= 'Relation between OMSs recommendation and  Sugars per Item on the Menu',
    hovermode= 'closest',
    xaxis=dict(
        showgrid=False,
        zeroline=False,
        showline=False
    ),
    yaxis=dict(
        title= 'Sugars(g)',
        ticklen= 5,
        gridwidth= 2,
        showgrid=False,
        zeroline=False,
        showline=False
    ),
    showlegend= False
)
fig = go.Figure(data=data, layout=layout)
graph = py.iplot(fig, filename='grouped-bar')

So as you could see, there's a lot of items in the menu that are bad for our health
for not saying all of them. For seeing the items in a more detailed way, you could check the notebook because the plots are interactive.

I hope you like this short analysis I've made. Check the repository in Github! :)

Forem: Francisco Carrillo Pérez

Restaurants with vegetarian options in USA

Analyzing sugar in McDonald's menu's items