Table of Contents

About

This is a project I made to analyze the amount of protein the various animal and vegetable products people can consume.

In this post, I learned the basics of using matplotlib. A future post might be refining this post

The data was imported from
Bohrer, B. M. (2017). Review: Nutrient density and nutritional value of meat products and non-meat foods high in protein. Trends in Food Science & Technology, 65, 103-112. doi:10.1016/j.tifs.2017.04.016 .

Importing libraries

The categories variables map to the text from the table. I made these variables to test the function

import pandas as pd
import matplotlib as plt
import matplotlib.pyplot as plt; plt.rcdefaults()
import matplotlib.pyplot as plt
import numpy as np

df1 = pd.read_csv("Nutrition.csv", encoding= 'unicode_escape')
df2 = pd.read_csv("nutrition-cost.csv", encoding= 'unicode_escape')

# categories
meat = "Meat, raw/unprepared unless noted otherwise"
fish = "Fish, raw/unprepared"
non_meat = "Non-meat, raw/unprepared"
from IPython.core.display import display, HTML  
display(HTML("<style>.container { width:100% !important; }</style>"))

Raw Data

Once we have loaded the csv files, I will take a look to see that the values are correct and check if there are any noticeable trends

df1
Category Product Energy value (kcal) Protein (g) Fat (g) Saturated fat (g) Cholesterol (mg) Vitamin B12 (mcg) Na (mg) P (mg) Fe (mg) Zn (mg)
0 Meat, raw/unprepared unless noted otherwise Beef ribeye (longissimus thoracis) 166 17.51 22.07 9.000 68 3.11 56.00 168 1.87 3.85
1 Meat, raw/unprepared unless noted otherwise Beef strip loin (longissimus lumborum) 228 20.61 15.49 6.251 81 1.06 52.00 186 1.47 3.53
2 Meat, raw/unprepared unless noted otherwise Beef tenderloin (psoas major) 247 19.61 18.16 8.410 85 1.01 50.00 180 1.42 2.9
3 Meat, raw/unprepared unless noted otherwise Beef top sirloin (gluteus mediuset al.) 201 20.30 12.71 5.127 75 1.05 52.00 187 1.48 3.55
4 Meat, raw/unprepared unless noted otherwise Beef tri-tip (m. tensor fasciae latae) 142 21.26 5.63 1.729 61 1.04 54.00 198 1.54 3.85
5 Meat, raw/unprepared unless noted otherwise Beef eye of round (semitendinosus) 124 23.27 3.44 1.276 60 1.83 53.00 221 1.45 3.4
6 Meat, raw/unprepared unless noted otherwise Beef brisket (deep pectoral et al.) 132 21.47 5.11 1.844 67 1.81 83.00 222 2.06 5.21
7 Meat, raw/unprepared unless noted otherwise Beef flank (rectus abdominis) 155 21.22 7.17 2.978 65 1.09 54.00 195 1.55 3.7
8 Meat, raw/unprepared unless noted otherwise Beef, ground 80% lean, 20% fat 254 17.17 20.00 7.581 71 2.14 66.00 158 1.94 4.18
9 Meat, raw/unprepared unless noted otherwise Beef, ground 90% lean, 10% fat 176 20.00 10.00 3.927 65 2.21 66.00 184 2.24 4.79
10 Meat, raw/unprepared unless noted otherwise Beef, ground 93% lean, 7% fat 152 20.85 7.00 2.878 63 2.23 66.00 192 2.33 4.97
11 Meat, raw/unprepared unless noted otherwise Beef, ground 97% lean, 3% fat 121 21.98 3.00 1.480 60 2.26 66.00 203 2.44 5.21
12 Meat, raw/unprepared unless noted otherwise Pork loin (longissimus lumborum) 198 19.74 12.58 4.360 63 0.53 50.00 197 0.79 1.74
13 Meat, raw/unprepared unless noted otherwise Pork ham (biceps femoris et al.) 245 17.43 18.87 6.540 73 0.63 47.00 199 0.85 1.93
14 Meat, raw/unprepared unless noted otherwise Pork, ground 84% lean, 16% fat 218 17.99 16.00 4.930 68 0.73 68.00 161 0.88 1.91
15 Meat, raw/unprepared unless noted otherwise Pork, ground 96% lean, 4% fat 121 21.10 4.00 1.420 59 0.64 67.00 190 0.86 1.93
16 Meat, raw/unprepared unless noted otherwise Lamb loin (longissimus lumborum) 310 16.32 26.63 11.760 74 2.04 56.00 152 1.61 2.53
17 Meat, raw/unprepared unless noted otherwise Lamb leg (biceps femoris et al) 230 17.91 17.07 7.430 69 2.5 56.00 170 1.66 3.32
18 Meat, raw/unprepared unless noted otherwise Lamb, ground 85% lean, 15% fat 255 17.14 20.71 9.926 73 – 77.00 – 1.41 –
19 Meat, raw/unprepared unless noted otherwise Chicken breast (pectoralis major) 120 22.50 2.62 0.563 73 0.21 45.00 213 0.37 0.68
20 Meat, raw/unprepared unless noted otherwise Chicken thigh (iliotibialis et al.) 221 16.52 16.61 4.524 98 0.62 81.00 157 0.68 1.29
21 Meat, raw/unprepared unless noted otherwise Turkey breast (pectoralis major) 157 21.89 7.02 1.910 65 0.42 59.00 186 1.20 1.57
22 Meat, raw/unprepared unless noted otherwise Turkey thigh (iliotibialis et al.) 116 20.60 3.69 0.782 78 2.17 75.00 177 1.42 2.95
23 Meat, raw/unprepared unless noted otherwise Turkey, ground, 93% lean, 15% fat 150 18.73 8.34 2.170 74 1.2 69.00 193 1.17 2.53
24 Meat, raw/unprepared unless noted otherwise Pork bacon, cured, unprepared 417 12.62 39.69 13.296 66 0.5 662.00 144 0.41 1.18
25 Fish, raw/unprepared Tuna, yellowfin 109 24.40 0.49 0.172 39 2.08 45.00 278 0.77 0.37
26 Fish, raw/unprepared Salmon, Atlantic 142 19.84 6.34 0.981 55 3.18 44.00 200 0.80 0.64
27 Fish, raw/unprepared Pollock, Atlantic 92 19.44 0.98 0.135 71 3.19 86.00 221 0.46 0.47
28 Fish, raw/unprepared Halibut, Atlantic 91 18.56 1.33 0.292 49 1.1 68.00 236 0.16 0.36
29 Fish, raw/unprepared Tilapia 96 20.08 1.70 0.585 50 1.58 52.00 170 0.56 0.33
30 Fish, raw/unprepared Catfish, wild channel 95 16.38 2.82 0.722 58 2.23 43.00 209 0.30 0.51
31 Non-meat, raw/unprepared Chicken eggs, whole 143 12.56 9.51 3.126 372 0.89 142.00 198 1.75 1.29
32 Non-meat, raw/unprepared Yogurt, Greek non-fat 59 10.19 0.39 0.117 5 0.75 36.00 135 0.07 0.52
33 Non-meat, raw/unprepared Kale 49 4.28 0.93 0.091 0 0 38.00 92 1.47 0.56
34 Non-meat, raw/unprepared Lentils, sprouted 106 8.96 0.55 0.057 0 0 11.00 173 3.21 1.51
35 Non-meat, raw/unprepared Broccoli, heads 34 2.82 0.37 0.039 0 0 33.00 66 0.73 0.41
36 Non-meat, raw/unprepared Green peas 81 5.42 0.40 0.071 0 0 5.00 108 1.47 1.24
37 Non-meat, raw/unprepared Spinach 23 2.86 0.39 0.063 0 0 79.00 49 2.71 0.53
38 Non-meat, raw/unprepared Black beans, mature 341 21.60 1.42 0.366 0 0 5.00 352 5.02 3.65
39 Non-meat, raw/unprepared Pinto beans, mature 62 5.25 0.90 0.109 0 0 153.00 94 1.97 0.5
40 Non-meat, raw/unprepared Lima beans, immature 113 6.84 0.86 0.198 0 0 0.78 136 3.14 0.78
41 Non-meat, raw/unprepared Kidney beans, mature 29 4.20 0.50 0.720 0 0 6.00 37 0.81 0.4
42 Non-meat, raw/unprepared Great northern beans, mature 339 21.86 1.14 0.356 0 0 14.00 447 5.47 2.31
43 Non-meat, raw/unprepared Tofu, firm, prepared with CaSO4 and MgCl2 78 9.04 4.17 0.793 0 0 12.00 121 1.61 0.83
44 Non-meat, raw/unprepared Tofu, soft, prepared with CaSO4 and MgCl2 61 7.17 3.69 0.533 0 0 8.00 92 1.11 0.64
45 Non-meat, raw/unprepared Hummus 166 7.90 9.60 1.437 0 0 379.00 176 2.44 1.83
46 Non-meat, raw/unprepared Peanuts 567 25.80 49.24 6.279 0 0 18.00 376 4.58 3.27
47 Non-meat, raw/unprepared Almonds 579 21.15 49.93 3.802 0 0 1.00 481 3.71 3.12
48 Non-meat, raw/unprepared Cashews 553 18.22 43.85 7.783 0 0 12.00 593 6.68 5.78 
df2
Category Product Retail Cost/100 g (US$) Source of estimationb Energy value (kcal/US$) Protein (g/US$) Vitamin B12 (mcg/US$) P (mg/US$) Fe (mg/US$) Zn (mg/US$)
0 Meat, raw/unprepared unless noted otherwise Beef ribeye (longissimus thoracis) 1.81000 USDA, AMS, 2016 91.71 9.67 1.72 92.82 1.03 2.13
1 Meat, raw/unprepared unless noted otherwise Beef strip loin (longissimus lumborum) 1.72620 USDA, AMS, 2016 132.08 11.94 0.61 107.75 0.85 2.04
2 Meat, raw/unprepared unless noted otherwise Beef tenderloin (psoas major) 2.44490 USDA, AMS, 2016 101.03 8.02 0.41 73.62 0.58 1.19
3 Meat, raw/unprepared unless noted otherwise Beef top sirloin (gluteus medius et al.) 1.12440 USDA, AMS, 2016 178.77 18.05 0.93 166.32 1.32 3.16
4 Meat, raw/unprepared unless noted otherwise Beef tri-tip (m. tensor fasciae latae) 1.18610 USDA, AMS, 2016 119.72 17.92 0.88 166.94 1.30 3.25
5 Meat, raw/unprepared unless noted otherwise Beef eye of round (semitendinosus) 1.01410 USDA, AMS, 2016 122.27 22.95 1.8 217.92 1.43 3.35
6 Meat, raw/unprepared unless noted otherwise Beef brisket (deep pectoralet al.) 0.89950 USDA, AMS, 2016 146.75 23.87 2.01 246.81 2.29 5.79
7 Meat, raw/unprepared unless noted otherwise Beef flank (rectus abdominis) 1.69980 USDA, AMS, 2016 91.19 12.48 0.64 114.72 0.91 2.18
8 Meat, raw/unprepared unless noted otherwise Beef, ground 80% lean, 20% fat 0.73190 USDA, AMS, 2016 347.03 23.46 2.92 215.87 2.65 5.71
9 Meat, raw/unprepared unless noted otherwise Beef, ground 90% lean, 10% fat 1.03620 USDA, AMS, 2016 169.86 19.30 2.13 177.58 2.16 4.62
10 Meat, raw/unprepared unless noted otherwise Beef, ground 93% lean, 7% fat 1.03620 USDA, AMS, 2016 146.69 20.12 2.15 185.3 2.25 4.8
11 Meat, raw/unprepared unless noted otherwise Beef, ground 97% lean, 3% fat 1.03620 USDA, AMS, 2016 116.78 21.21 2.18 195.91 2.35 5.03
12 Meat, raw/unprepared unless noted otherwise Pork loin (longissimus lumborum) 0.41230 USDA, AMS, 2016 480.27 47.88 1.29 477.85 1.92 4.22
13 Meat, raw/unprepared unless noted otherwise Pork ham (biceps femoriset al.) 0.70550 USDA, AMS, 2016 347.28 24.71 0.89 282.08 1.20 2.74
14 Meat, raw/unprepared unless noted otherwise Pork bacon, cured, unprepared 4.72890 USDA, AMS, 2016 88.18 2.67 0.11 30.45 0.09 0.25
15 Meat, raw/unprepared unless noted otherwise Pork, ground 84% lean, 16% fat 0.59300 USDA, AMS, 2016 367.60 30.34 1.23 271.48 1.48 3.22
16 Meat, raw/unprepared unless noted otherwise Pork, ground 96% lean, 4% fat 0.59300 USDA, AMS, 2016 204.03 35.58 1.08 320.38 1.45 3.25
17 Meat, raw/unprepared unless noted otherwise Lamb loin (longissimus lumborum) 1.78570 USDA, AMS, 2016 173.60 9.14 1.14 85.12 0.90 1.42
18 Meat, raw/unprepared unless noted otherwise Lamb leg (biceps femoris et al) 1.76150 USDA, AMS, 2016 130.57 10.17 1.42 96.51 0.94 1.88
19 Meat, raw/unprepared unless noted otherwise Lamb, ground 85% lean, 15% fat 1.24120 USDA, AMS, 2016 205.45 13.81 – – 1.14 –
20 Meat, raw/unprepared unless noted otherwise Chicken breast (pectoralis major) 0.70110 USDA, AMS, 2016 171.17 32.09 0.3 303.82 0.53 0.97
21 Meat, raw/unprepared unless noted otherwise Chicken thigh (iliotibialiset al.) 0.28440 USDA, AMS, 2016 777.09 58.09 2.18 552.05 2.39 4.54
22 Meat, raw/unprepared unless noted otherwise Turkey breast (pectoralis major) 0.59300 USDA, AMS, 2016 264.74 36.91 0.71 313.64 2.02 2.65
23 Meat, raw/unprepared unless noted otherwise Turkey thigh (iliotibialiset al.) 0.66140 USDA, AMS, 2016 175.39 31.15 3.28 267.62 2.15 4.46
24 Meat, raw/unprepared unless noted otherwise Turkey, ground, 93% lean, 15% fat 0.78480 USDA, AMS, 2016 191.12 23.86 1.53 245.91 1.49 3.22
25 Fish, raw/unprepared Tuna, yellowfin 5.01820 Marketplace assessment 21.72 4.86 0.41 55.4 0.15 0.07
26 Fish, raw/unprepared Salmon, Atlantic 4.54060 Marketplace assessment 31.27 4.37 0.7 44.05 0.18 0.14
27 Fish, raw/unprepared Pollock, Atlantic 0.81430 Marketplace assessment 112.98 23.87 3.92 271.4 0.56 0.58
28 Fish, raw/unprepared Halibut, Atlantic 7.30520 Marketplace assessment 12.46 2.54 0.15 32.31 0.02 0.05
29 Fish, raw/unprepared Tilapia 2.00220 Marketplace assessment 47.95 10.03 0.79 84.91 0.28 0.16
30 Fish, raw/unprepared Catfish, wild channel 2.36250 Marketplace assessment 40.21 6.93 0.94 88.47 0.13 0.22
31 Non-meat, raw/unprepared Chicken eggs, whole 0.18330 USDA, AMS, 2016 780.00 68.51 4.85 1080 9.55 7.04
32 Non-meat, raw/unprepared Yogurt, Greek non-fat 0.86420 USDA, AMS, 2016 68.27 11.79 0.87 156.21 0.08 0.6
33 Non-meat, raw/unprepared Kale 0.61950 USDA, ERS, 2013 79.10 6.91 0 148.51 2.37 0.9
34 Non-meat, raw/unprepared Lentils, sprouted 0.30640 USDA, ERS, 2013 345.91 29.24 0 564.54 10.48 4.93
35 Non-meat, raw/unprepared Broccoli, heads 0.36160 USDA, ERS, 2013 94.04 7.80 0 182.54 2.02 1.13
36 Non-meat, raw/unprepared Green peas 0.36380 USDA, ERS, 2013 222.67 14.90 0 296.9 4.04 3.41
37 Non-meat, raw/unprepared Spinach 0.84440 USDA, ERS, 2013 27.24 3.39 0 58.03 3.21 0.63
38 Non-meat, raw/unprepared Black beans, mature 0.32849 USDA, ERS, 2013 1038.09 65.76 0 1071.58 15.28 11.11
39 Non-meat, raw/unprepared Pinto beans, mature 0.26455 USDA, ERS, 2013 234.36 19.84 0 355.31 7.45 1.89
40 Non-meat, raw/unprepared Lima beans, immature 0.38140 USDA, ERS, 2013 296.28 17.93 0 356.58 8.23 2.05
41 Non-meat, raw/unprepared Kidney beans, mature 0.36820 USDA, ERS, 2013 78.77 11.41 0 100.5 2.20 1.09
42 Non-meat, raw/unprepared Great northern beans, mature 0.33730 USDA, ERS, 2013 1005.02 64.81 0 1325.2 16.22 6.85
43 Non-meat, raw/unprepared Tofu, firm, prepared with CaSO4 and MgCl2 0.97710 Marketplace assessment 79.83 9.25 0 123.84 1.65 0.85
44 Non-meat, raw/unprepared Tofu, soft, prepared with CaSO4 and MgCl2 0.44500 Marketplace assessment 137.08 16.11 0 206.74 2.49 1.44
45 Non-meat, raw/unprepared Hummus 0.92500 Marketplace assessment 179.46 8.54 0 190.27 2.64 1.98
46 Non-meat, raw/unprepared Peanuts 0.87960 Marketplace assessment 644.58 29.33 0 427.45 5.21 3.72
47 Non-meat, raw/unprepared Almonds 1.98200 Marketplace assessment 292.14 10.67 0 242.69 1.87 1.57
48 Non-meat, raw/unprepared Cashews 2.20240 Marketplace assessment 251.09 8.27 0 269.25 3.03 2.62

Largest values

This Dataframe displays the 10 products that have the most energy value (kcal/US$). I wanted to see which protein product gave you the most Energy for how much you pay.

df2.nlargest(n = 10, columns = "Energy value (kcal/US$)")
Category Product Retail Cost/100 g (US$) Source of estimationb Energy value (kcal/US$) Protein (g/US$) Vitamin B12 (mcg/US$) P (mg/US$) Fe (mg/US$) Zn (mg/US$)
38 Non-meat, raw/unprepared Black beans, mature 0.32849 USDA, ERS, 2013 1038.09 65.76 0 1071.58 15.28 11.11
42 Non-meat, raw/unprepared Great northern beans, mature 0.33730 USDA, ERS, 2013 1005.02 64.81 0 1325.2 16.22 6.85
31 Non-meat, raw/unprepared Chicken eggs, whole 0.18330 USDA, AMS, 2016 780.00 68.51 4.85 1080 9.55 7.04
21 Meat, raw/unprepared unless noted otherwise Chicken thigh (iliotibialiset al.) 0.28440 USDA, AMS, 2016 777.09 58.09 2.18 552.05 2.39 4.54
46 Non-meat, raw/unprepared Peanuts 0.87960 Marketplace assessment 644.58 29.33 0 427.45 5.21 3.72
12 Meat, raw/unprepared unless noted otherwise Pork loin (longissimus lumborum) 0.41230 USDA, AMS, 2016 480.27 47.88 1.29 477.85 1.92 4.22
15 Meat, raw/unprepared unless noted otherwise Pork, ground 84% lean, 16% fat 0.59300 USDA, AMS, 2016 367.60 30.34 1.23 271.48 1.48 3.22
13 Meat, raw/unprepared unless noted otherwise Pork ham (biceps femoriset al.) 0.70550 USDA, AMS, 2016 347.28 24.71 0.89 282.08 1.20 2.74
8 Meat, raw/unprepared unless noted otherwise Beef, ground 80% lean, 20% fat 0.73190 USDA, AMS, 2016 347.03 23.46 2.92 215.87 2.65 5.71
34 Non-meat, raw/unprepared Lentils, sprouted 0.30640 USDA, ERS, 2013 345.91 29.24 0 564.54 10.48 4.93

Plotting Function

These are functions I wrote to plot energy value and protein content.

Arguments

The arguments include a dataframe, and a string variable called category, the variable category helps determine whether we will plot a specific protein product or if it will plot all graphs.

What Occurs During a Function Call

When the function is called, the variables skipCheck, categoryCase, and columnTitleCase are initialized the variables that end in case, convert the lowercase to a title to match the dictionary entry. Next, a dictionary is initialized for all the categories of the protein products. In the next line, we will use a if statement to check which dataframe we are processing, since df2 has the retail cost, we will look for retail cost, if the column exists, we will set columnDictionary to the second columnCostDictionary we initialized, otherwise the default dictionary will be columnRegularDictionary. After initializing the dictionary, we will check to see if the argument for category exists in the dictionaries we initialized previously. If category is not in columnDictionary we will have the function exit and spit a message telling the user they made a misspelling.

Once we finish the verification, we will set the plot specific setttings. The first if statement will check if categoryCase (the first letters of all words capitalized), is in the dictionary categoryDictionary. If categoryCase matches Meat, Fish, or Non_Meat, we will have it plot only for the specifc protein product. Otherwise, we will use the settings for a graph containing all protein products.

With the plot specifc settings complete, we can set the general plot settings and return the plot object. Outside of the function, we will use the .show() command to display the plot we generated. I left the .show() outside of the function so that I could prevent myself from running out of cpu.

def plotNutrition(df, category, columnTitle):
    """
    This script will plot a graph from a the following variables 
        df, dataframe to be plotted
        category, the following arguments are allowed: 
            Meat
            Fish
            Non_Meat
            
        ColumnTitle , columns allowed to be plotted
    """
    # Variable initialization
    skipCheck = False
    categoryCase = category.title()
    columnTitleCase = columnTitle.title()
    
    # Dictionaries to reference
    categoryDictionary = {"Meat": "Meat, raw/unprepared unless noted otherwise", "Fish": "Fish, raw/unprepared", "Non_Meat" : "Non-meat, raw/unprepared"}
    columnRegularDictionary = {"Energy Value" : "Energy value (kcal)", "Protein": "Protein (g)", "Fat" : "Fat (g)", "Saturated Fat" : "Saturated fat (g)", "Cholesterol" : "Cholesterol (mg)", "Vitamin" : "Vitamin B12 (mcg)", "Sodium" :"Na (mg)", "Phosphorus" : "P (mg)", "Iron" : "Fe (mg)", "Zinc" : "Zn (mg)"}
    columnCostDictionary = {"Retail Cost" : "Retail Cost/100\xa0g (US$)", "Energy Value" : "Energy value (kcal/US$)", "Protein" : "Protein (g/US$)", "Vitamin" : "Vitamin B12 (mcg/US$)", "Phosphorus" : "P (mg/US$)", "Iron" : "Fe (mg/US$)", "Zinc" : "Zn (mg/US$)" }
    
    # Check if which dataframe we are using, if there is "Retail Cost/100 g (US$)", we will use the cost dictionary
    allList = df.columns.tolist()
    if("Retail Cost/100\xa0g (US$)" in allList):
        columnDictionary = columnCostDictionary
    else:
        columnDictionary = columnRegularDictionary
        
    # Check if the arguement for category is exists in the dictionary, if it is not in the dictionary, it will exit the function
    if(categoryCase == "All"):
        skipCheck = True
    if((categoryCase not in categoryDictionary) & (skipCheck != True)):
        return print("spelling error in category argument, your category argument was " + categoryCase)
    
    # Initialize all dataframes needed for plotting, if the category name is inside of the category dictionary, we change the plot settings to focus only on the category name
    if(categoryCase in categoryDictionary):
        df_specific = df[df["Category"] == categoryDictionary[categoryCase]].sort_values(by = [columnDictionary[columnTitleCase]])
        objects = df_specific["Product"]
        performance = df_specific[columnDictionary[columnTitleCase]]
    else:
        df = df.sort_values(by = [columnDictionary[columnTitleCase]])
        objects = df["Product"]
        performance = df[columnDictionary[columnTitleCase]]
        plt.tick_params(axis='y', which='major', labelsize=6)    
        #plt.tick_params(axis='x', which='major', labelsize=6)    
        
    #Plotting settings
    y_pos = np.arange(len(objects))
    plt.barh(y_pos, performance, align='center', alpha=0.5)
    plt.yticks(y_pos, objects)
    plt.xticks(rotation=45)
    plt.xlabel(columnDictionary[columnTitleCase])
    plt.title(categoryCase + ' ' + columnTitleCase + ' Comparison')
    plt.figure(dpi=900)    
        
    return plt

Charts

After all the work, Behold! We have the final product

All Energy Values Comparison 

plotNutrition(df1, "all", "energy value").show()
plotNutrition(df1, "meat", "energy value").show()
plotNutrition(df1, "fish", "energy value").show()
plotNutrition(df1, "non_meat", "energy value").show()

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All Protein Values Comparison 

plotNutrition(df1, "all", "protein").show()
plotNutrition(df1, "meat", "protein").show()
plotNutrition(df1, "fish", "protein").show()
plotNutrition(df1, "non_meat", "protein").show()

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All Fat Values Comparison 

plotNutrition(df1, "all", "fat").show()
plotNutrition(df1, "meat", "fat").show()
plotNutrition(df1, "fish", "fat").show()
plotNutrition(df1, "non_meat", "fat").show()

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All Saturated Fat Values Comparison 

plotNutrition(df1, "all", "saturated fat").show()
plotNutrition(df1, "meat", "saturated fat").show()
plotNutrition(df1, "fish", "saturated fat").show()
plotNutrition(df1, "non_meat", "saturated fat").show()

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All Cholesterol Values Comparison 

plotNutrition(df1, "all", "Cholesterol").show()
plotNutrition(df1, "meat", "Cholesterol").show()
plotNutrition(df1, "fish", "Cholesterol").show()
plotNutrition(df1, "non_meat", "Cholesterol").show()

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All Vitamn Values Comparison 

plotNutrition(df1, "all", "Vitamin").show()
plotNutrition(df1, "meat", "Vitamin").show()
plotNutrition(df1, "fish", "Vitamin").show()
plotNutrition(df1, "non_meat", "Vitamin").show()

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All Sodium Values Comparison 

plotNutrition(df1, "all", "Sodium").show()
plotNutrition(df1, "meat", "Sodium").show()
plotNutrition(df1, "fish", "Sodium").show()
plotNutrition(df1, "non_meat", "Sodium").show()

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All Phosphorus Values Comparison 

plotNutrition(df1, "all", "Phosphorus").show()
plotNutrition(df1, "meat", "Phosphorus").show()
plotNutrition(df1, "fish", "Phosphorus").show()
plotNutrition(df1, "non_meat", "Phosphorus").show()

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All Iron Values Comparison 

plotNutrition(df1, "all", "Iron").show()
plotNutrition(df1, "meat", "Iron").show()
plotNutrition(df1, "fish", "Iron").show()
plotNutrition(df1, "non_meat", "Iron").show()

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All Zinc Values Comparison 

plotNutrition(df1, "all", "Zinc").show()
plotNutrition(df1, "meat", "Zinc").show()
plotNutrition(df1, "fish", "Zinc").show()
plotNutrition(df1, "non_meat", "Zinc").show()

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All Retail Cost Comparison 

allRetailCost = ["retail cost", "energy value", "protein", "vitamin", "phosphorus", "iron", "zinc"]
for i in range(len(allRetailCost)):
    plotNutrition(df2, "all", allRetailCost[i]).show()
    plotNutrition(df2, "meat", allRetailCost[i]).show()
    plotNutrition(df2, "fish", allRetailCost[i]).show()
    plotNutrition(df2, "non_meat", allRetailCost[i]).show()
    

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/home/bedhedd/miniconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 150 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/home/bedhedd/miniconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 150 missing from current font.
  font.load_char(ord(s), flags=flags)

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Testing

This section are tests I made to check dictionaries and lists outside of the function

Initializing Test Variables 

test1 = False
test2 = False
name = "meat"
name2 = "Saturated Fat"
ref2 = {"Meat": "Meat, raw/unprepared unless noted otherwise", "Fish": "Fish, raw/unprepared", "Non_meat" : "Non-meat, raw/unprepared"}
if(name.title() in ref3):
    test1 = True
print(test1)

False

Testing if Saturated Fat Exists in ref4 Dictonary 

ref = {"meat": "Meat, raw/unprepared unless noted otherwise", "fish": "Fish, raw/unprepared", "non_meat" : "Non-meat, raw/unprepared"}
ref1 = {meat: "Meat", fish: "Fish",  non_meat: "Non_meat"} 
ref2 = {"Meat, raw/unprepared unless noted otherwise" : "meat", "Fish": "Fish, raw/unprepared", "Non_meat" : "Non-meat, raw/unprepared"}
ref3 = {"retail cost" : "Retail Cost/100 g (US$)", "energy value" : "Energy value (kcal/US$)", "protein" : "Protein (g/US$)", "vitamin" : "Vitamin B12 (mcg/US$)", "phosphorus" : "P (mg/US$)", "iron" : "Fe (mg/US$)", "zinc" : "Zn (mg/US$)" }
ref4 = {"Energy Value" : "Energy value (kcal)", "Protein": "Protein (g)", "Fat" : "Fat (g)", "Saturated Fat": "Saturated fat (g)", "Cholesterol" : "Cholesterol (mg)", "Vitamin B12" "Vitamin B12 (mcg)" "Sodium" :"Na (mg)", "Phosphorus" : "P (mg)", "Iron" : "Fe (mg)", "Zinc" : "Zn (mg)"}


#ref1["Meat, raw/unprepared unless noted otherwise"]
ref["meat"]
ref3["energy Value".casefold()]
ref4["Saturated Fat"]

if(name2.title() in ref4):
    test1 = True
print(test1)

True

Testing Dictionary Outputs In Relation to Variables 

category = "meat"
columnTitle = "retail cost"

categoryCase = category.casefold()
columnTitleCase = columnTitle.casefold()

categoryDictionary = {"meat": "Meat, raw/unprepared unless noted otherwise", "fish": "Fish, raw/unprepared", "non_meat" : "Non-meat, raw/unprepared"}
columnDictionary = {"retail cost" : "Retail Cost/100 g (US$)", "energy value" : "Energy value (kcal/US$)", "protein" : "Protein (g/US$)", "vitamin" : "Vitamin B12 (mcg/US$)", "phosphorus" : "P (mg/US$)", "iron" : "Fe (mg/US$)", "zinc" : "Zn (mg/US$)" }

#df_specific = df2[df2["Category"] == categoryDictionary[categoryCase]].sort_values(by = [columnDictionary[columnTitleCase]])
#df_specific

columnDictionary[columnTitleCase]

'Retail Cost/100 g (US$)'

Testing Solution To Backslashes Causing Issues in Python 

allList = df2.columns.tolist()
if("Retail Cost/100\xa0g (US$)" in allList):
    print("worked")
else: 
    print("didn't work")

allList

worked

['Category',
 'Product',
 'Retail Cost/100\xa0g (US$)',
 'Source of estimationb',
 'Energy value (kcal/US$)',
 'Protein (g/US$)',
 'Vitamin B12 (mcg/US$)',
 'P (mg/US$)',
 'Fe (mg/US$)',
 'Zn (mg/US$)']
p= []
p.append(ref)

df3 = pd.DataFrame(p)
df3

if(name in p):
    test1 = 

  File "<ipython-input-526-f7fecc95bb11>", line 8
    test1 =
            ^
SyntaxError: invalid syntax

Previous versions of functions

These are previous iterations of the plotNutrition() function, I decided to keep them at the end in case you were curious about the evolution of nutrition function. When I started this project, I was had a early deadline. I started with a longer function so that could plot graphs for specific columns.

At the time, I did not realize that lines 11-15 were relatively the same. I also was not very comfortable with substituting dictionary entries for strings. When I had more time, I started to work on making the plot function more modular. In the final product you will notice that I have formal dictionaries calls.

Dictionary calls make the code more modular, but they also abstract the code more. If you are not careful with keeping track of your dictionary calls, diagnosing errors in the code become much harder

Energy Values Plotting Function 

def plotEV(df, name):
    ref = {"meat": "Meat, raw/unprepared unless noted otherwise", "fish": "Fish, raw/unprepared", "non_meat" : "Non-meat, raw/unprepared"}
    caseInsensitiveName = name.casefold()
    
    if(caseInsensitiveName in ref):
        df_specific = df[df1["Category"] == ref[caseInsensitiveName]].nsmallest(n = len(df), columns = "Energy value (kcal)")
        objects = df_specific["Product"]
        y_pos = np.arange(len(objects))
        performance = df_specific["Energy value (kcal)"]
        
        plt.barh(y_pos, performance, align='center', alpha=0.5)
        plt.yticks(y_pos, objects)
        plt.xlabel('Energy Value (kcal)')
        plt.title(name.title() + ' Energy Values Comparison')
        plt.figure(dpi=300)
    
    else:
        df = df.nsmallest(n = len(df), columns = "Energy value (kcal)")
        objects = df["Product"]
        y_pos = np.arange(len(objects))
        performance = df["Energy value (kcal)"]
        
        plt.barh(y_pos, performance, align='center', alpha=0.5)
        plt.yticks(y_pos, objects)
        plt.xlabel('Energy Value (kcal)')
        plt.title('All Energy Values Comparison')
        plt.tick_params(axis='y', which='major', labelsize=6)
        plt.figure(dpi=300)
       
    return plt

Protein Values Plotting Function 

def plotProtein(df, name):
    ref = {"meat": "Meat, raw/unprepared unless noted otherwise", "fish": "Fish, raw/unprepared", "non_meat" : "Non-meat, raw/unprepared"}
    if(name in ref):
        df_specific = df[df1["Category"] == ref[name]].nsmallest(n = len(df), columns = "Protein (g)")
        objects = df_specific["Product"]
        y_pos = np.arange(len(objects))
        performance = df_specific["Protein (g)"]
        
        plt.barh(y_pos, performance, align='center', alpha=0.5)
        plt.yticks(y_pos, objects)
        plt.xlabel('Protein (g)')
        plt.title(name.title( + ' Protein Values Comparison')
        plt.figure(dpi=300)
    
    else:
        df = df.nsmallest(n = len(df), columns = "Protein (g)")
        objects = df["Product"]
        y_pos = np.arange(len(objects))
        performance = df["Protein (g)"]
        
        plt.barh(y_pos, performance, align='center', alpha=0.5)
        plt.yticks(y_pos, objects)
        plt.xlabel('Protein (g)')
        plt.title('All Protein Values Comparison')
        plt.tick_params(axis='y', which='major', labelsize=5)
        plt.figure(dpi=300)
               
    return plt

  File "<ipython-input-10-ba8e2741f3b2>", line 13
    plt.figure(dpi=300)
      ^
SyntaxError: invalid syntax