The McDonalds Dataset was taken which had details about the Food items and their Nutritional value. The Data Analysis of the dataset was done in Python using Python Libraries and tools. The report has been prepared in a simple crisp and easy to read manner, keeping in mind the reviewer of the article. Special attention has been given to spacing and colouring to make the article more interesting. All insights are present right below the codes.

1. ***McDonalds Dataset Analysis***
Report Summary
The McDonalds Dataset has been selected for the Data Analysis. The Dataset consists of details
about the Food items and their Nutritional value.
The Data Analysis of the dataset was done in Python, making use of Python Libraries and Tools.
The report has been prepared in a simple, crisp and easy to read manner, keeping in mind the
reviewer of the article.
Special attention has been given to spacing and colouring to make the article more interesting to
read. All insights are present right below the codes.
Some of the Questions we will be covering in this report.
Plot graphically which food categories have the highest and lowest varieties.
Which all variables have an outlier?
Which variables have the highest correlation? Plot them and find out the value?
Which category contributes to the maximum % of Cholesterol in a diet (% daily value)?
Which item contributes maximum to the Sodium intake?
Which 4 food items contain the most amount of Saturated Fat?
Preliminary Analysis of the Dataset
In [4]: import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(color_codes=True)
In [5]: mcd_df = pd.read_csv("D:New Download - 2019Great Learning 2020Data
SetsMcD.csv")
# Transpose - so that we can have a look at all the columns of the Datas
et
mcd_df.head()
Preliminary Analysis of the Dataset
In [23]: mcd_df.describe().T
Inference :-
Describe tells us the distribution of the numerical data of the
McDonals Dataset. Deviation
Here, Mean and Standard especially help us to understand the
variation of the numerical data in the dataset
columns which are present on the Left side.
In [16]: mcd_df.info()
Inference :-
Info tells us about the type of value within each column.
Here, in our Data set we have Integer, Float and Object types.
Also, we can observe that there are " Missing Values " .
As every column has the the same 260 values.
In [24]: mcd_df.shape
Inference :-
Shape tells us about the Rows & Columns in the Dataset.
As we can see there are 260 Rows and 24 Columns.
In [28]: mcd_df.isnull().sum()
Inference :-
isnull tells us about the Null values in the Dataset.
As we can see the Columns have " 0 " has outcome.
Which represents No Null Values.
Getting to know the McDonalds Dataset better
In [30]: mcd_df.head()
In [44]: mcd_df['Category'].value_counts()
In [45]: mcd_df['Category'].nunique()
Inference :-
First column is Category .
We have 9 different types of Food offered at McDonalds.
The complete list which has Coffee, Tea, Breakfast items is available
above.
In [50]: mcd_df['Item'].value_counts().head(10)
In [46]: mcd_df['Item'].nunique()
Inference :-
2nd column is Item .
We have 260 different types of Food items available at McDonalds.
The rest of the columns has Nutrional details of each of the Food
items.
Note :-
We now have a fair idea, as to what is present in our McDonald
dataset.
Having established this, let us now proceed to the Questions and their
Answers.
Q1. Plot graphically which food categories have the
highest and lowest varieties.
In [61]: plt.figure(figsize=(15,7))
mcd_df["Category"].hist()
Inference :-
The highest sales is of :-
Coffee and Tea
The lowest sales is of :-
Salads
Desserts
Q2. Which all variables have an outlier ?
Ans. Here, we shall evaluate the columns one-by-one and find out
which columns have outliers.
To find the outliers we will be taking the help of Boxplots.
In [64]: mcd_df.describe()
The describe function identifies all the numerical columns for us.
Outliers can be determined only for columns which are Numerical in
nature.
Now, we have all the Numerical column names we shall use them one-
by-one below.
In [91]: plt.figure(figsize=(15,10))
sns.boxplot(data=mcd_df[['Calories','Calories from Fat','Total Fat','Sat
urated Fat']]);
Inference :-
The columns which have outlier :-
Calories
Calories from Fat
Total fat
The columns which don't have an outlier :-
Saturated Fat
In [92]: plt.figure(figsize=(15,10))
sns.boxplot(data=mcd_df[['Saturated Fat (% Daily Value)' ,'Trans Fat','C
holesterol','Cholesterol (% Daily Value)']]);
Inference :-
The columns which have an outlier :-
Saturated Fat (% Daily Value)
Cholestrol
Cholestrol (% Daily Value)
The columns which don't have an outlier :-
Trans Fat
In [93]: plt.figure(figsize=(3,5))
sns.boxplot(y = mcd_df["Trans Fat"]);
Inference :-
We drew the Boxplot again for " Trans Fat " as it was un-clear from the
previous graph about the presence of outlier.
The columns which don't have an outlier :-
Trans Fat
In [115]: mcd_df["Trans Fat"].head(10)
In [117]: mcd_df["Trans Fat"].unique()
We can see that " Trans Fat " has only 5 values and the same is
seen
in the above Boxplot
In [121]: plt.figure(figsize=(9,11))
sns.boxplot(data=mcd_df[['Cholesterol','Cholesterol (% Daily Value)','Ca
lories from Fat']]);
Inference :-
The columns which have an outlier :-
Calories from Fat
Cholestrol
Cholestrol (% Daily Value)
In [105]: plt.figure(figsize=(7,5))
sns.boxplot(data=mcd_df[['Total Fat','Total Fat (% Daily Value)','Sodium
(% Daily Value)']]);
Inference :-
The columns which have an outlier :-
Total Fat
Total Fat (% Daily Value)
Sodium (% Daily Value)
In [101]: plt.figure(figsize=(3,5))
sns.boxplot(y = mcd_df["Sodium"]);
Inference :-
The columns which have an outlier :-
Sodium
In [ ]:
In [111]: plt.figure(figsize=(15,10))
sns.boxplot(data=mcd_df[['Carbohydrates', 'Carbohydrates (% Daily Valu
e)','Dietary Fiber', 'Dietary Fiber (% Daily Value)']]);
Inference :-
The columns which have an outlier :-
Carbohydrates
Carbohydrates (% Daily Value)
Dietary Fiber (% Daily Value)
The columns which don't have an outlier :-
Dietary Fiber
An intresting observation, Dietary Fiber does not have an outlier but
the same column's Dietary Fiber (% Daily Value) has an outlier.
It denotes that Dietary Fiber in certain, has food items, which are
significantly different in Fiber concentration than the other food
items.
In [112]: plt.figure(figsize=(7,5))
sns.boxplot(data=mcd_df[['Sugars', 'Protein']]);
Inference :-
The columns which have an outlier :-
Sugars
Protien
In [113]: plt.figure(figsize=(15,10))
sns.boxplot(data=mcd_df[['Vitamin A (% Daily Value)', 'Vitamin C (% Dail
y Value)', 'Calcium (% Daily Value)', 'Iron (% Daily Value)']]);
Inference :-
The columns which have an outlier :-
Vitamin A (% Daily Value)
Vitamin C (% Daily Value)
Calcium (% Daily Value)
Iron (% Daily Value)
Q3. Which variables have the highest correlation? Plot
them and find out the value ?
Ans. For depicting the variables with the Highest correlation, I will be
selecting the variable - pair which has correlation value of atleast 50%
and higher.
I have selected 50% value, as variables having 50% or Higher
correlational value represents a High degree of inter - dependence on
each other.
In [6]: correlation = mcd_df.corr()
correlation
In [8]: plt.figure(figsize=(15,11))
sns.heatmap(correlation,annot = True)
Inference :-
List of Variables which have Correlation with each other.
This list is with respect to Calories :-
Calories & Iron [ 0.64 ]
Calories & Sodium [ 0.71 ]
Calories & Cholestrol [ 0.68 ]
Calories & Saturated Fat [ 0.85 ]
This list is with respect to Total Fat :-
Total Fat & Iron [ 0.73 ]
Total Fat & Protien [ 0.81 ]
Total Fat & Dietary Fiber [ 0.58 ]
Total Fat & Sodium [ 0.85 ]
Total Fat & Cholestrol [ 0.68 ]
Total Fat & Saturated Fat [ 0.85 ]
This list is with respect to Saturated Fat :-
Saturated Fat & Iron [ 0.58 ]
Saturated Fat & Protien [ 0.6 ]
Saturated Fat & Carbohydrates [ 0.58 ]
Saturated Fat & Sodium [ 0.58 ]
Saturated Fat & Cholestrol [ 0.63 ]
Total Fat & Trans Fat [ 0.62 ]
This list is with respect to Cholestrol :-
Cholestrol & Iron [ 0.65 ]
Cholestrol & Protien [ 0.56 ]
Cholestrol & Sodium [ 0.62 ]
This list is with respect to Sodium :-
Sodium & Iron [ 0.87 ]
Sodium & Protien [ 0.87 ]
Sodium & Dietary Fiber [ 0.69 ]
This list is with respect to Carbohydrates :-
Carbohydrates & Calcium [ 0.59 ]
Carbohydrates & Sugars [ 0.76 ]
This list is with respect to Dietary Fiber :-
Dietary Fiber & Iron [ 0.74 ]
This list is with respect to Sugars :-
Sugars & Calcium [ 0.6 ]
This list is with respect to Protien :-
Protien & Iron [ 0.79 ]
The above list help us to understand Food Nutrition in a better way. By
understanding
By understanding the correlation between the Food constituents
like Protien, Iron,
Calcium, Vitamin A etc, of the food we eat.
We can now, go ahead select and eat food items that are according to
our own
Fitness Goals or Health Goals.
Q4. Which category contributes to the maximum
% of Cholesterol in a diet (% daily value)?
In [17]: mcd_df['Category'].nunique()
print('The number of Categories is -', mcd_df['Category'].nunique())
In [46]: mcd_df['Category'].unique()
from pandas import DataFrame
Category_List = sorted(mcd_df['Category'].unique())
type(sorted(mcd_df['Category'].unique()))
a = pd.DataFrame(Category_List,columns=[''] )
a.index += 1
print('n n Let us view the different food categories that are availab
le to us :- n n ',a )
In [93]: b = pd.pivot_table(mcd_df, 'Cholesterol (% Daily Value)', index=['Catego
ry'])
Result = b.sort_values(('Cholesterol (% Daily Value)'), ascending=False)
Result
Inference :-
There are 9 categories.
There is a list above wherein we can see all the 9 different Categories
like Breakfast, Salads, Desserts, Beverages etc.
Now, every Category has a corresponding value of Cholesterol (%
Daily Value).
We have sorted and arranged the Categories from High to Low based
on the Cholesterol (% Daily Value).
Q5. Which item contributes maximum to the
Sodium intake?
In [80]: mcd_df['Item'].nunique()
print('n n The different type of Food items available at McD are -', m
cd_df['Item'].nunique())
> Let us have a look at some of the Food Items
In [88]: mcd_df['Item'].head(16)
In [97]: c = pd.pivot_table(mcd_df, 'Sodium', index=['Item'])
Result_1 = c.sort_values(('Sodium'), ascending=False)
Result_1.head(15)
Inference :-
The item with the Highest Sodium content is Chicken McNuggets (40
piece)
Q6. Which 4 food items contains the most
amount of Saturated Fat ?
In [95]: c = pd.pivot_table(mcd_df, 'Saturated Fat', index=['Item'])
Result_1 = c.sort_values(('Saturated Fat'), ascending=False)
Result_1.head(15)
Inference :-
The items with the Highest Saturated Fat content are :-
McFlurry with M&M’s Candies (Medium)
Big Breakfast with Hotcakes (Large Biscuit)
Chicken McNuggets (40 piece)
Frappé Chocolate Chip (Large)
# This is the end of the Data Analysis of the McDonals Dataset. # Thank You for the review.
Out[5]:
Category Item
Serving
Size
Calories
Calories
from Fat
Total
Fat
Total
Fat (%
Daily
Value)
Saturated
Fat
Saturated
Fat (%
Daily
Value)
Trans
Fat
...
0 Breakfast
Egg
McMuffin
4.8 oz
(136 g)
300 120 13.0 20 5.0 25 0.0 ...
1 Breakfast
Egg
White
Delight
4.8 oz
(135 g)
250 70 8.0 12 3.0 15 0.0 ...
2 Breakfast
Sausage
McMuffin
3.9 oz
(111 g)
370 200 23.0 35 8.0 42 0.0 ...
3 Breakfast
Sausage
McMuffin
with Egg
5.7 oz
(161 g)
450 250 28.0 43 10.0 52 0.0 ...
4 Breakfast
Sausage
McMuffin
with Egg
Whites
5.7 oz
(161 g)
400 210 23.0 35 8.0 42 0.0 ...
5 rows × 24 columns
Out[23]:
count mean std min 25% 50% 75% max
Calories 260.0 368.269231 240.269886 0.0 210.000 340.0 500.00 1880.0
Calories from Fat 260.0 127.096154 127.875914 0.0 20.000 100.0 200.00 1060.0
Total Fat 260.0 14.165385 14.205998 0.0 2.375 11.0 22.25 118.0
Total Fat (% Daily Value) 260.0 21.815385 21.885199 0.0 3.750 17.0 35.00 182.0
Saturated Fat 260.0 6.007692 5.321873 0.0 1.000 5.0 10.00 20.0
Saturated Fat (% Daily Value) 260.0 29.965385 26.639209 0.0 4.750 24.0 48.00 102.0
Trans Fat 260.0 0.203846 0.429133 0.0 0.000 0.0 0.00 2.5
Cholesterol 260.0 54.942308 87.269257 0.0 5.000 35.0 65.00 575.0
Cholesterol (% Daily Value) 260.0 18.392308 29.091653 0.0 2.000 11.0 21.25 192.0
Sodium 260.0 495.750000 577.026323 0.0 107.500 190.0 865.00 3600.0
Sodium (% Daily Value) 260.0 20.676923 24.034954 0.0 4.750 8.0 36.25 150.0
Carbohydrates 260.0 47.346154 28.252232 0.0 30.000 44.0 60.00 141.0
Carbohydrates (% Daily
Value)
260.0 15.780769 9.419544 0.0 10.000 15.0 20.00 47.0
Dietary Fiber 260.0 1.630769 1.567717 0.0 0.000 1.0 3.00 7.0
Dietary Fiber (% Daily Value) 260.0 6.530769 6.307057 0.0 0.000 5.0 10.00 28.0
Sugars 260.0 29.423077 28.679797 0.0 5.750 17.5 48.00 128.0
Protein 260.0 13.338462 11.426146 0.0 4.000 12.0 19.00 87.0
Vitamin A (% Daily Value) 260.0 13.426923 24.366381 0.0 2.000 8.0 15.00 170.0
Vitamin C (% Daily Value) 260.0 8.534615 26.345542 0.0 0.000 0.0 4.00 240.0
Calcium (% Daily Value) 260.0 20.973077 17.019953 0.0 6.000 20.0 30.00 70.0
Iron (% Daily Value) 260.0 7.734615 8.723263 0.0 0.000 4.0 15.00 40.0
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 260 entries, 0 to 259
Data columns (total 24 columns):
Category 260 non-null object
Item 260 non-null object
Serving Size 260 non-null object
Calories 260 non-null int64
Calories from Fat 260 non-null int64
Total Fat 260 non-null float64
Total Fat (% Daily Value) 260 non-null int64
Saturated Fat 260 non-null float64
Saturated Fat (% Daily Value) 260 non-null int64
Trans Fat 260 non-null float64
Cholesterol 260 non-null int64
Cholesterol (% Daily Value) 260 non-null int64
Sodium 260 non-null int64
Sodium (% Daily Value) 260 non-null int64
Carbohydrates 260 non-null int64
Carbohydrates (% Daily Value) 260 non-null int64
Dietary Fiber 260 non-null int64
Dietary Fiber (% Daily Value) 260 non-null int64
Sugars 260 non-null int64
Protein 260 non-null int64
Vitamin A (% Daily Value) 260 non-null int64
Vitamin C (% Daily Value) 260 non-null int64
Calcium (% Daily Value) 260 non-null int64
Iron (% Daily Value) 260 non-null int64
dtypes: float64(3), int64(18), object(3)
memory usage: 48.8+ KB
Out[24]: (260, 24)
Out[28]: Category 0
Item 0
Serving Size 0
Calories 0
Calories from Fat 0
Total Fat 0
Total Fat (% Daily Value) 0
Saturated Fat 0
Saturated Fat (% Daily Value) 0
Trans Fat 0
Cholesterol 0
Cholesterol (% Daily Value) 0
Sodium 0
Sodium (% Daily Value) 0
Carbohydrates 0
Carbohydrates (% Daily Value) 0
Dietary Fiber 0
Dietary Fiber (% Daily Value) 0
Sugars 0
Protein 0
Vitamin A (% Daily Value) 0
Vitamin C (% Daily Value) 0
Calcium (% Daily Value) 0
Iron (% Daily Value) 0
dtype: int64
Out[30]:
Category Item
Serving
Size
Calories
Calories
from Fat
Total
Fat
Total
Fat (%
Daily
Value)
Saturated
Fat
Saturated
Fat (%
Daily
Value)
Trans
Fat
...
0 Breakfast
Egg
McMuffin
4.8 oz
(136 g)
300 120 13.0 20 5.0 25 0.0 ...
1 Breakfast
Egg
White
Delight
4.8 oz
(135 g)
250 70 8.0 12 3.0 15 0.0 ...
2 Breakfast
Sausage
McMuffin
3.9 oz
(111 g)
370 200 23.0 35 8.0 42 0.0 ...
3 Breakfast
Sausage
McMuffin
with Egg
5.7 oz
(161 g)
450 250 28.0 43 10.0 52 0.0 ...
4 Breakfast
Sausage
McMuffin
with Egg
Whites
5.7 oz
(161 g)
400 210 23.0 35 8.0 42 0.0 ...
5 rows × 24 columns
Out[44]: Coffee & Tea 95
Breakfast 42
Smoothies & Shakes 28
Chicken & Fish 27
Beverages 27
Beef & Pork 15
Snacks & Sides 13
Desserts 7
Salads 6
Name: Category, dtype: int64
Out[45]: 9
Out[50]: Diet Coke (Large) 1
Sausage, Egg & Cheese McGriddles 1
Premium McWrap Chicken Sweet Chili (Grilled Chicken) 1
Regular Iced Coffee (Large) 1
Caramel Mocha (Medium) 1
Minute Maid Orange Juice (Large) 1
Sausage Biscuit with Egg Whites (Regular Biscuit) 1
Caramel Mocha (Small) 1
Hash Brown 1
Chocolate Shake (Large) 1
Name: Item, dtype: int64
Out[46]: 260
Out[61]: <matplotlib.axes._subplots.AxesSubplot at 0x2374bcdcb70>
Out[64]:
Calories
Calories
from Fat
Total Fat
Total Fat
(% Daily
Value)
Saturated
Fat
Saturated
Fat (%
Daily
Value)
Trans Fat Cho
count 260.000000 260.000000 260.000000 260.000000 260.000000 260.000000 260.000000 260
mean 368.269231 127.096154 14.165385 21.815385 6.007692 29.965385 0.203846 54
std 240.269886 127.875914 14.205998 21.885199 5.321873 26.639209 0.429133 87
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0
25% 210.000000 20.000000 2.375000 3.750000 1.000000 4.750000 0.000000 5
50% 340.000000 100.000000 11.000000 17.000000 5.000000 24.000000 0.000000 35
75% 500.000000 200.000000 22.250000 35.000000 10.000000 48.000000 0.000000 65
max 1880.000000 1060.000000 118.000000 182.000000 20.000000 102.000000 2.500000 575
8 rows × 21 columns
Out[115]: 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 1.0
6 0.0
7 0.0
8 0.0
9 0.0
Name: Trans Fat, dtype: float64
Out[117]: array([0. , 1. , 0.5, 1.5, 2.5])
Out[6]:
Calories
Calories
from Fat
Total Fat
Total Fat
(% Daily
Value)
Saturated
Fat
Saturated
Fat (%
Daily
Value)
Trans Fat Cholest
Calories 1.000000 0.904588 0.904409 0.904123 0.845564 0.847631 0.522441 0.596
Calories from
Fat
0.904588 1.000000 0.999663 0.999725 0.847008 0.849592 0.433686 0.682
Total Fat 0.904409 0.999663 1.000000 0.999765 0.846707 0.849293 0.431453 0.680
Total Fat (%
Daily Value)
0.904123 0.999725 0.999765 1.000000 0.847379 0.849973 0.433016 0.680
Saturated Fat 0.845564 0.847008 0.846707 0.847379 1.000000 0.999279 0.620611 0.631
Saturated Fat
(% Daily
Value)
0.847631 0.849592 0.849293 0.849973 0.999279 1.000000 0.620210 0.633
Trans Fat 0.522441 0.433686 0.431453 0.433016 0.620611 0.620210 1.000000 0.253
Cholesterol 0.596399 0.682161 0.680547 0.680940 0.631210 0.633603 0.253935 1.000
Cholesterol (%
Daily Value)
0.595208 0.681607 0.680000 0.680378 0.630334 0.632712 0.251502 0.999
Sodium 0.712309 0.846624 0.846158 0.846728 0.584075 0.588694 0.187580 0.624
Sodium (%
Daily Value)
0.713415 0.847276 0.846780 0.847368 0.585323 0.589958 0.188339 0.624
Carbohydrates 0.781539 0.461672 0.461213 0.460516 0.591261 0.591322 0.463250 0.270
Carbohydrates
(% Daily
Value)
0.781242 0.461463 0.461005 0.460298 0.591743 0.591655 0.462891 0.272
Dietary Fiber 0.538894 0.581274 0.580837 0.580592 0.351818 0.356831 0.054918 0.435
Dietary Fiber
(% Daily
Value)
0.540014 0.575621 0.575206 0.575033 0.347152 0.351797 0.058301 0.440
Sugars 0.259598 -0.115285 -0.115446 -0.115761 0.197734 0.195928 0.334756 -0.135
Protein 0.787847 0.807913 0.807773 0.807922 0.603028 0.606581 0.388249 0.561
Vitamin A (%
Daily Value)
0.108844 0.056731 0.054434 0.054038 0.064972 0.065376 0.075833 0.080
Vitamin C (%
Daily Value)
-0.068747 -0.087331 -0.089354 -0.089353 -0.179672 -0.178059 -0.076612 -0.082
Calcium (%
Daily Value)
0.428426 0.161034 0.162860 0.162031 0.403311 0.401139 0.385331 0.132
Iron (% Daily
Value)
0.643552 0.735894 0.734685 0.735478 0.578062 0.580488 0.325476 0.655
21 rows × 21 columns
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x27addfb9b70>
The number of Categories is - 9
Let us view the different food categories that are available to us :-
1 Beef & Pork
2 Beverages
3 Breakfast
4 Chicken & Fish
5 Coffee & Tea
6 Desserts
7 Salads
8 Smoothies & Shakes
9 Snacks & Sides
Out[93]:
Cholesterol (% Daily Value)
Category
Breakfast 50.952381
Beef & Pork 28.933333
Chicken & Fish 25.222222
Salads 17.333333
Smoothies & Shakes 14.714286
Coffee & Tea 9.378947
Snacks & Sides 6.230769
Desserts 4.857143
Beverages 0.185185
The different type of Food items available at McD are - 260
Out[88]: 0 Egg McMuffin
1 Egg White Delight
2 Sausage McMuffin
3 Sausage McMuffin with Egg
4 Sausage McMuffin with Egg Whites
5 Steak & Egg McMuffin
6 Bacon, Egg & Cheese Biscuit (Regular Biscuit)
7 Bacon, Egg & Cheese Biscuit (Large Biscuit)
8 Bacon, Egg & Cheese Biscuit with Egg Whites (R...
9 Bacon, Egg & Cheese Biscuit with Egg Whites (L...
10 Sausage Biscuit (Regular Biscuit)
11 Sausage Biscuit (Large Biscuit)
12 Sausage Biscuit with Egg (Regular Biscuit)
13 Sausage Biscuit with Egg (Large Biscuit)
14 Sausage Biscuit with Egg Whites (Regular Biscuit)
15 Sausage Biscuit with Egg Whites (Large Biscuit)
Name: Item, dtype: object
Out[97]:
Sodium
Item
Chicken McNuggets (40 piece) 3600
Big Breakfast with Hotcakes and Egg Whites (Large Biscuit) 2290
Big Breakfast with Hotcakes (Large Biscuit) 2260
Big Breakfast with Hotcakes and Egg Whites (Regular Biscuit) 2170
Big Breakfast with Hotcakes (Regular Biscuit) 2150
Chicken McNuggets (20 piece) 1800
Bacon Clubhouse Crispy Chicken Sandwich 1720
Big Breakfast with Egg Whites (Large Biscuit) 1700
Big Breakfast (Large Biscuit) 1680
Big Breakfast with Egg Whites (Regular Biscuit) 1590
Bacon Clubhouse Grilled Chicken Sandwich 1560
Big Breakfast (Regular Biscuit) 1560
Premium McWrap Chicken & Bacon (Crispy Chicken) 1540
Steak, Egg & Cheese Bagel 1510
Bacon, Egg & Cheese Bagel with Egg Whites 1480
Out[95]:
Saturated Fat
Item
McFlurry with M&M’s Candies (Medium) 20.0
Big Breakfast with Hotcakes (Large Biscuit) 20.0
Chicken McNuggets (40 piece) 20.0
Frappé Chocolate Chip (Large) 20.0
Double Quarter Pounder with Cheese 19.0
Big Breakfast with Hotcakes (Regular Biscuit) 19.0
Big Breakfast (Large Biscuit) 18.0
Frappé Mocha (Large) 17.0
Frappé Chocolate Chip (Medium) 17.0
Big Breakfast (Regular Biscuit) 17.0
Frappé Caramel (Large) 17.0
Big Breakfast with Hotcakes and Egg Whites (Regular Biscuit) 16.0
Big Breakfast with Hotcakes and Egg Whites (Large Biscuit) 16.0
Steak & Egg Biscuit (Regular Biscuit) 16.0
Strawberry Shake (Large) 15.0