Access your meal tracking data stored in MyFitnessPal programatically
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MyFitnessPal

Do you track your eating habits on MyFitnessPal? Have you ever wanted to analyze the information you're entering into MyFitnessPal programatically?

Although MyFitnessPal does have an API, it is private-access only; this creates an unnecessary barrier between you and your data that can be overcome using this library.

Having problems? Issues live on github. Have questions? Ask your questions in our gitter room.

Installation

You can either install from pip:

pip install myfitnesspal

or checkout and install the source from the github repository:

git clone https://github.com/coddingtonbear/python-myfitnesspal.git
cd python-myfitnesspal
python setup.py install

Authentication

It is a good security practice to not type out the passwords for any of your services (including MyFitnessPal) in either a source file or in the console in such a way that somebody else might be able to read them. Toward that end, python-myfitnesspal allows you to use your system keyring.

To store your MyFitnessPal password in the system keyring, run:

myfitnesspal store-password my_username

You will immediately be asked for your password, and that password will be stored in your system keyring for later interactions with MyFitnessPal.

Please note that all examples below assume you've stored your password in your system keyring like above, but you can (as before) provide your password to the module directly as its second argument.

Diary Examples

To access a single day's information:

import myfitnesspal

client = myfitnesspal.Client('my_username')

day = client.get_date(2013, 3, 2)
day
# >> <03/02/13 {'sodium': 3326, 'carbohydrates': 369, 'calories': 2001, 'fat': 22, 'sugar': 103, 'protein': 110}>

To see all meals you can use the Day object's meals property:

day.meals
# >> [<Breakfast {}>,
#    <Lunch {'sodium': 712, 'carbohydrates': 106, 'calories': 485, 'fat': 3, 'sugar': 0, 'protein': 17}>,
#    <Dinner {'sodium': 2190, 'carbohydrates': 170, 'calories': 945, 'fat': 11, 'sugar': 17, 'protein': 53}>,
#    <Snacks {'sodium': 424, 'carbohydrates': 93, 'calories': 571, 'fat': 8, 'sugar': 86, 'protein': 40}>]

To access dinner, you can access it by its index in day.meals:

dinner = day.meals[2]
dinner
# >> <Dinner {'sodium': 2190, 'carbohydrates': 170, 'calories': 945, 'fat': 11, 'sugar': 17, 'protein': 53}>

To get a list of things you ate for dinner, I can use the dinner Meal object's entries property:

dinner.entries
# >> [<Montebello - Spaghetti noodles, 6 oz. {'sodium': 0, 'carbohydrates': 132, 'calories': 630, 'fat': 3, 'sugar': 3, 'protein': 21}>,
#     <Fresh Market - Arrabiatta Organic Pasta Sauce, 0.5 container (3 cups ea.) {'sodium': 1410, 'carbohydrates': 24, 'calories': 135, 'fat': 5, 'sugar': 12, 'protein': 6}>,
#     <Quorn - Meatless and Soy-Free Meatballs, 6 -4 pieces (68g) {'sodium': 780, 'carbohydrates': 14, 'calories': 180, 'fat': 3, 'sugar': 2, 'protein': 26}>]

To access one of the items, use the entries property as a list:

spaghetti = dinner.entries[0]
spaghetti.name
# >> Montebello - Spaghetti noodles, 6 oz.

For a daily summary of your nutrition information, you can use a Day object's totals property:

day.totals
# >> {'calories': 2001,
#     'carbohydrates': 369,
#     'fat': 22,
#     'protein': 110,
#     'sodium': 3326,
#     'sugar': 103}

Or, if you just want to see how many cups of water you've recorded, or the notes you've entered for a day:

day.water
# >> 1
day.notes
# >> "This is the note I entered for this day"

For just one meal:

dinner.totals
# >> {'calories': 945,
#     'carbohydrates': 170,
#     'fat': 11,
#     'protein': 53,
#     'sodium': 2190,
#     'sugar': 17}

For just one entry:

spaghetti.totals
# >> {'calories': 630,
#     'carbohydrates': 132,
#     'fat': 3,
#     'protein': 21,
#     'sodium': 0,
#     'sugar': 3}

Measurement Examples

To access measurements from the past 30 days:

import myfitnesspal

client = myfitnesspal.Client('my_username')

weight = client.get_measurements('Weight')
weight
# >> OrderedDict([(datetime.date(2015, 5, 14), 171.0), (datetime.date(2015, 5, 13), 173.8), (datetime.date(2015, 5,12), 171.8),
#                 (datetime.date(2015, 5, 11), 171.6), (datetime.date(2015, 5, 10), 172.4), (datetime.date(2015, 5, 9), 170.2),
#                 (datetime.date(2015, 5, 8), 171.0),  (datetime.date(2015, 5, 7), 171.2),  (datetime.date(2015, 5, 6), 170.8),
#                 (datetime.date(2015, 5, 5), 171.8),  (datetime.date(2015, 5, 4), 174.2),  (datetime.date(2015, 5, 3), 172.2),
#                 (datetime.date(2015, 5, 2), 171.0),  (datetime.date(2015, 5, 1), 171.2),  (datetime.date(2015, 4, 30), 171.6),
#                 (datetime.date(2015, 4, 29), 172.4), (datetime.date(2015, 4, 28), 172.2), (datetime.date(2015, 4, 27), 173.2),
#                 (datetime.date(2015, 4, 26), 171.8), (datetime.date(2015, 4, 25), 170.8), (datetime.date(2015, 4, 24), 171.2),
#                 (datetime.date(2015, 4, 23), 171.6), (datetime.date(2015, 4, 22), 173.2), (datetime.date(2015, 4, 21), 174.2),
#                 (datetime.date(2015, 4, 20), 173.6), (datetime.date(2015, 4, 19), 171.8), (datetime.date(2015, 4, 18), 170.4),
#                 (datetime.date(2015, 4, 17), 169.8), (datetime.date(2015, 4, 16), 170.4), (datetime.date(2015, 4, 15), 170.8),
#                 (datetime.date(2015, 4, 14), 171.6)])

To access measurements since a given date:

import datetime

may = datetime.date(2015, 5, 1)

body_fat = client.get_measurements('Body Fat', may)
body_fat
# >> OrderedDict([(datetime.date(2015, 5, 14), 12.8), (datetime.date(2015, 5, 13), 13.1), (datetime.date(2015, 5, 12), 12.7),
#                 (datetime.date(2015, 5, 11), 12.7), (datetime.date(2015, 5, 10), 12.8), (datetime.date(2015, 5, 9), 12.4),
#                 (datetime.date(2015, 5, 8), 12.6),  (datetime.date(2015, 5, 7), 12.7),  (datetime.date(2015, 5, 6), 12.6),
#                 (datetime.date(2015, 5, 5), 12.9),  (datetime.date(2015, 5, 4), 13.0),  (datetime.date(2015, 5, 3), 12.6),
#                 (datetime.date(2015, 5, 2), 12.6),  (datetime.date(2015, 5, 1), 12.7)])

To access measurements within a date range:

thisweek = datetime.date(2015, 5, 11)
lastweek = datetime.date(2015, 5, 4)

weight = client.get_measurements('Weight', thisweek, lastweek)
weight
# >> OrderedDict([(datetime.date(2015, 5, 11), 171.6), (datetime.date(2015, 5, 10), 172.4), (datetime.date(2015, 5,9), 170.2),
#                 (datetime.date(2015, 5, 8), 171.0),  (datetime.date(2015, 5, 7), 171.2),  (datetime.date(2015, 5, 6), 170.8),
#                 (datetime.date(2015, 5, 5), 171.8),  (datetime.date(2015, 5, 4), 174.2)])

Measurements are returned as ordered dictionaries. The first argument specifies the measurement name, which can be any name listed in the MyFitnessPal Check-In page. When specifying a date range, the order of the date arguments does not matter.

Command-line API

Although most people will probably be using Python-MyFitnessPal as a way of integrating their MyFitnessPal data into another application, Python-MyFitnessPal does provide a command-line API with a handful of commands described below.

store-password $USERNAME

Store a password for a given MyFitnessPal account in your system's keyring.

delete-password $USERNAME

Delete a password for a given MyFitnessPal account from your system keyring.

day $USERNAME [$DATE]

Display meals and totals for a given date. If no date is specified, totals will be printed for today.

Hints

Day objects act as dictionaries:

day.keys()
# >> ['Breakfast', 'Lunch', 'Dinner', 'Snack']
lunch = day['Lunch']
print lunch
# >> [<Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': 508, 'carbohydrates': 76, 'calories': 346, 'fat': 2, 'sugar': 0, 'protein': 12}>,
#     <Injera - Ethiopian Flatbread, 18 " diameter {'sodium': 204, 'carbohydrates': 30, 'calories': 139, 'fat': 1, 'sugar': 0, 'protein': 5}>]

Meal objects act as lists:

len(lunch)
# >> 2
miser_wat = lunch[0]
print miser_wat
# >> <Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': 508, 'carbohydrates': 76, 'calories': 346, 'fat': 2, 'sugar': 0, 'protein': 12}>

and Entry objects act as dictionaries:

print miser_wat['calories']
# >> 346

and, since the measurement units returned are not necessarily very intuitive, you can enable or disable unit awareness using the unit_aware keyword argument.

client = myfitnesspal.Client('my_username', unit_aware=True)
day = client.get_date(2013, 3, 2)
lunch = day['lunch']
print lunch
# >> [<Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': Weight(mg=508), 'carbohydrates': Weight(g=76), 'calories': Energy(Calorie=346), 'fat': Weight(g=2), 'sugar': Weight(g=0), 'protein': Weight(g=12)}>,
miser_wat = lunch[0]
print miser_wat['calories']
# >> Energy(Calorie=346)