Python Myfitnesspal¶

Getting Started¶

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¶

This library uses your local browser’s MyFitnessPal cookies for interacting with MyFitnessPal via the browser_cookie3 library. To control which user account this library uses for interacting with MyFitnessPal, just log in to the appropriate account in your browser and, with a bit of luck, python-myfitnesspal should be able to find the authentication credentials needed.

By default, this library will look for cookies set for the www.myfitnesspal.com and myfitnesspal.com domains in all browsers supported by browser_cookie3. You can control which cookiejar is used by passing a http.cookiejar.CookieJar object via the constructor’s cookiejar keyword parameter. See browser_cookie3’s readme for details around how you might select a cookiejar from a particular browser.

Note

Starting on August 25th, 2022, MyFitnessPal added a hidden captcha to their login flow. That change unfortunately prevents this library from logging-in directly, and as of version 2.0 of python-myfitnesspal, this library now relies on reading browser cookies directly for gathering login credentials.

See Issue #144 for details and context.

Using the 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 single command described below.

`day [$DATE]`¶

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

How-to Guides¶

Accessing your Diary¶

To access a single day’s information:

import myfitnesspal

client = myfitnesspal.Client()

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 milliliters 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}

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(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)

Accessing a Friend’s Diary¶

If a friend has their diary visibility set to public, you can grab their diary entries:

import myfitnesspal

client = myfitnesspal.Client()

friend_day = client.get_date(2020, 8, 23, username="username_of_my_friend")
>>> friend_day
<08/23/20 {'calories': 891.0, 'carbohydrates': 105.0, 'fat': 38.0, 'protein': 29.0, 'sodium': 0.0, 'sugar': 2.0}>

Accessing your Exercises¶

Exercises are accessed through the day.exercises command - giving an 2-item array of [<Cardiovascular>, <Strength>], which can be explored using get_as_list()

To get a list of cardiovascular exercises

import myfitnesspal

client = myfitnesspal.Client()

day = client.get_date(2019, 3, 12)

day.exercises[0].get_as_list()
# >> [{'name': 'Walking, 12.5 mins per km, mod. pace, walking dog', 'nutrition_information': {'minutes': 60, 'calories burned': 209}}, {'name': 'Running (jogging), 8 kph (7.5 min per km)', 'nutrition_information': {'minutes': 25, 'calories burned': 211}}]

And then access individual properties

day.exercises[0].get_as_list()[0]['name']

# >> 'Walking, 12.5 mins per km, mod. pace, walking dog'

day.exercises[0].get_as_list()[0]['nutrition_information']['minutes']
# >> 60

day.exercises[0].get_as_list()[0]['nutrition_information']['calories burned']
# >> 209

To get a list of strength exercises

import myfitnesspal

client = myfitnesspal.Client()

day = client.get_date(2019, 3, 12)

day.exercises[1].get_as_list()
# >> [{'name': 'Leg Press', 'nutrition_information': {'sets': 3, 'reps/set': 12, 'weight/set': 20}}, {'name': 'Seated Row, Floor, Machine', 'nutrition_information': {'sets': 3, 'reps/set': 12, 'weight/set': 20}}]

And then access individual properties

day.exercises[1].get_as_list()[0]['name']
# >> 'Leg Press'

day.exercises[1].get_as_list()[0]['nutrition_information']['sets']
# >> 3

day.exercises[1].get_as_list()[0]['nutrition_information']['reps/set']
# >> 12

day.exercises[1].get_as_list()[0]['nutrition_information']['weight/set']
# >> 20

Accessing your Measurements¶

To access measurements from the past 30 days:

import myfitnesspal

client = myfitnesspal.Client()

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.

Searching for Foods¶

To search for items:

import myfitnesspal

client = myfitnesspal.Client()

food_items = client.get_food_search_results("bacon cheeseburger")
food_items
# >> [<Bacon Cheeseburger -- Sodexo Campus>,
# <Junior Bacon Cheeseburger -- Wendy's>,
# <Bacon Cheeseburger -- Continental Café>,
# <Bacon Cheddar Cheeseburger -- Applebees>,
# <Bacon Cheeseburger - Plain -- Homemade>,
# <Jr. Bacon Cheeseburger -- Wendys>,
# ...

print("{} ({}), {}, cals={}, mfp_id={}".format(
    food_items[0].name,
    food_items[0].brand,
    food_items[0].serving,
    food_items[0].calories,
    food_items[0].mfp_id
))
# > Bacon Cheeseburger (Sodexo Campus), 1 Sandwich, cals = 420.0

To get details for a particular food:

import myfitnesspal

client = myfitnesspal.Client()

item = client.get_food_item_details("89755756637885")
item.servings
# > [<1.00 x Sandwich>]
item.saturated_fat
# > 10.0

Accessing Reports¶

To access report data from the past 30 days:

import myfitnesspal

client = myfitnesspal.Client()

client.get_report(report_name="Net Calories", report_category="Nutrition")
# >> OrderedDict([(datetime.date(2015, 5, 14), 1701.0), (datetime.date(2015, 5, 13), 1732.8), (datetime.date(2015, 5,12), 1721.8),
#                 (datetime.date(2015, 5, 11), 1701.6), (datetime.date(2015, 5, 10), 1272.4), (datetime.date(2015, 5, 9), 1720.2),
#                 (datetime.date(2015, 5, 8), 1071.0),  (datetime.date(2015, 5, 7), 1721.2),  (datetime.date(2015, 5, 6), 1270.8),
#                 (datetime.date(2015, 5, 5), 1701.8),  (datetime.date(2015, 5, 4), 1724.2),  (datetime.date(2015, 5, 3), 1722.2),
#                 (datetime.date(2015, 5, 2), 1701.0),  (datetime.date(2015, 5, 1), 1721.2),  (datetime.date(2015, 4, 30), 1721.6),
#                 (datetime.date(2015, 4, 29), 1072.4), (datetime.date(2015, 4, 28), 1272.2), (datetime.date(2015, 4, 27), 1723.2),
#                 (datetime.date(2015, 4, 26), 1791.8), (datetime.date(2015, 4, 25), 1720.8), (datetime.date(2015, 4, 24), 1721.2),
#                 (datetime.date(2015, 4, 23), 1721.6), (datetime.date(2015, 4, 22), 1723.2), (datetime.date(2015, 4, 21), 1724.2),
#                 (datetime.date(2015, 4, 20), 1273.6), (datetime.date(2015, 4, 19), 1721.8), (datetime.date(2015, 4, 18), 1720.4),
#                 (datetime.date(2015, 4, 17), 1629.8), (datetime.date(2015, 4, 16), 1270.4), (datetime.date(2015, 4, 15), 1270.8),
#                 (datetime.date(2015, 4, 14), 1721.6)])

import datetime

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

client.get_report("Net Calories", "Nutrition", may)
# >> OrderedDict([(datetime.date(2015, 5, 14), 172.8), (datetime.date(2015, 5, 13), 173.1), (datetime.date(2015, 5, 12), 127.7),
#                 (datetime.date(2015, 5, 11), 172.7), (datetime.date(2015, 5, 10), 172.8), (datetime.date(2015, 5, 9), 172.4),
#                 (datetime.date(2015, 5, 8), 172.6),  (datetime.date(2015, 5, 7), 172.7),  (datetime.date(2015, 5, 6), 172.6),
#                 (datetime.date(2015, 5, 5), 172.9),  (datetime.date(2015, 5, 4), 173.0),  (datetime.date(2015, 5, 3), 172.6),
#                 (datetime.date(2015, 5, 2), 172.6),  (datetime.date(2015, 5, 1), 172.7)])

To access report data within a date range:

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

client.get_report("Net Calories", "Nutrition", thisweek, lastweek)
# >> OrderedDict([(datetime.date(2015, 5, 11), 1721.6), (datetime.date(2015, 5, 10), 1722.4), (datetime.date(2015, 5,9), 1720.2),
#                 (datetime.date(2015, 5, 8), 1271.0),  (datetime.date(2015, 5, 7), 1721.2),  (datetime.date(2015, 5, 6), 1720.8),
#                 (datetime.date(2015, 5, 5), 1721.8),  (datetime.date(2015, 5, 4), 1274.2)])

Report data is returned as ordered dictionaries. The first argument specifies the report name, the second argument specifies the category name - both of which can be anything listed in the MyFitnessPal Reports page. When specifying a date range, the order of the date arguments does not matter.

Use on Windows Subsystem for Linux¶

When using python-myfitnesspal on Windows via Windows Subsystem for Linux you will encounter one extra complication when logging in due to the mechanism python-myfitnesspal uses for gathering authentication credentials.

You must log in to MyFitnessPal via a browser installed on your guest (Linux) installation instead of via one installed the conventional way.

Contributing¶

I think I’ve found a bug¶

We’d love to hear about it – create a bug report on github and be sure to include:

A description of what you were doing when the error occurred. Were you trying to fetch your diary, exercises, or measurements? Were you trying to look up food? The more we know about what you were doing, the easier ti will be to find the problem.
A traceback, if at all possible. Without a traceback, it’s really hard to know what kind of problem you’re running into, and given that a lot of what this library does is interact with individuals’ accounts, reproducing the bug might not be possible, and a traceback will at least give hints about what’s going wrong.

I want to add a new feature¶

You’re a hero. New feature submissions are almost always welcome, but we recommend starting a discussion to make sure that this feature isn’t already under development by somebody else, and to possibly talk through how you’re planning to implement a feature to make sure it’ll sail through the Pull Request Review process as smoothly as possible.

There are a few things to keep in mind for your submission in order to help it sail through the review process smoothly including:

The build process will most likely inform you if you’ve missed something, but your submission should follow the project’s style and standards including:
- Proper typings for your methods and variables.
- Following of the standard black style.
Your Pull Request description should include a clear description of what problem your submission solves and why.
Your submission should include a little documentation if at all possible including:
- API Documentation covering your newly-added public methods and properties. This is mostly automated, luckily; just make sure you’ve added a docstring and proper types to your methods.
- Maybe a “How to” article in the docs to show folks how to use the feature your new submission adds.
If you want extra credit, some tests. We recognize that this is mostly an integration tool, and that testing such things is extremely tricky, but if you can think of a way of making your work testable, we’d all appreciate it.

Once you’ve developed your feature, post a Pull Request, and the community and collaborators will have a look at what you’ve put together and possibly post comments and/or suggestions for your consideration. You need at least one official maintainer’s approval before the Pull Request can be merged into the codebase.

I have a feature idea¶

Start a discussion about it. Maybe you can find somebody to help you bring it to fruition.

API¶

Client¶

class myfitnesspal.Client(cookiejar: Optional[CookieJar] = None, unit_aware: bool = False)¶

Provides access to MyFitnessPal APIs

property user_id: Optional[str]¶: The user_id of the logged-in account.

property user_metadata: UserMetadata¶: Metadata about of the logged-in account.

property access_token: Optional[str]¶: The access token for the logged-in account.

property effective_username: str¶

One’s actual username may be different from the one used for login

This method will return the actual username if it is available, but will fall back to the one provided if it is not.

get_date(year: int, month: int, day: int) → Day¶
get_date(date: date) → Day: Returns your meal diary for a particular date

get_measurements(measurement='Weight', lower_bound: Optional[date] = None, upper_bound: Optional[date] = None) → Dict[date, float]¶: Returns measurements of a given name between two dates.

set_measurements(measurement='Weight', value: Optional[float] = None, date: Optional[date] = None) → None¶: Sets measurement for today’s date.

get_report(report_name: str = 'Net Calories', report_category: str = 'Nutrition', lower_bound: Optional[date] = None, upper_bound: Optional[date] = None) → Dict[date, float]¶: Returns report data of a given name and category between two dates.

get_food_search_results(query: str) → List[FoodItem]¶: Search for foods matching a specified query.

get_food_item_details(mfp_id: int) → FoodItem¶: Get details about a specific food using its ID.

set_new_food(brand: str, description: str, calories: int, fat: float, carbs: float, protein: float, sodium: Optional[float] = None, potassium: Optional[float] = None, saturated_fat: Optional[float] = None, polyunsaturated_fat: Optional[float] = None, fiber: Optional[float] = None, monounsaturated_fat: Optional[float] = None, sugar: Optional[float] = None, trans_fat: Optional[float] = None, cholesterol: Optional[float] = None, vitamin_a: Optional[float] = None, calcium: Optional[float] = None, vitamin_c: Optional[float] = None, iron: Optional[float] = None, serving_size: str = '1 Serving', servingspercontainer: float = 1.0, sharepublic: bool = False) → None¶: Function to submit new foods / groceries to the MyFitnessPal database. Function will return True if successful.

set_new_goal(energy: float, energy_unit: str = 'calories', carbohydrates: Optional[float] = None, protein: Optional[float] = None, fat: Optional[float] = None, percent_carbohydrates: Optional[float] = None, percent_protein: Optional[float] = None, percent_fat: Optional[float] = None) → None¶

Updates your nutrition goals.

This Function will update your nutrition goals and is able to deal with multiple situations based on the passed arguments. First matching situation will be applied and used to update the nutrition goals.

Passed arguments - Hints: energy and all absolute macro values - Energy value will be adjusted/calculated if energy from macro values is higher than provided energy value. energy and all percentage macro values - Energy will be adjusted and split into macros by provided percentage. energy - Energy will be adjusted and split into macros by percentage as before.

Optional arguments: energy_unit - Function is able to deal with calories and kilojoules. If not provided user preferences will be used.

Additional hints: Values will be adjusted and rounded by MFP if no premium subscription is applied!

get_recipes() → Dict[int, str]¶

Returns a dictionary with all saved recipes.

Recipe ID will be used as dictionary key, recipe title as dictionary value.

get_recipe(recipeid: int) → Recipe¶

Returns recipe details in a dictionary.

See https://schema.org/Recipe for details regarding this schema.

get_meals() → Dict[int, str]¶

Returns a dictionary with all saved meals.

Key: Meal ID Value: Meal Name

get_meal(meal_id: int, meal_title: str) → Recipe¶

Returns meal details.

See https://schema.org/Recipe for details regarding this schema.

Day¶

class myfitnesspal.day.Day(date: date, meals: Optional[List[Meal]] = None, goals: Optional[Dict[str, float]] = None, notes: Optional[Callable[[], str]] = None, water: Optional[Callable[[], float]] = None, exercises: Optional[Callable[[], List[Exercise]]] = None, complete: bool = False)¶

Stores meal entries for a particular day.

__getitem__(value: str) → Meal¶: Returns a meal matching the provided name.

keys() → List[str]¶: Returns a list of meal names.

property meals: List[Meal]¶: Returns a list of meals.

property entries: Generator[Entry, None, None]¶: Yields all entries from all meals.

property goals: Dict[str, float]¶: Returns goals.

property notes: str¶: Returns notes.

property water: float¶: Returns water.

property exercises: List[Exercise]¶: Returns list of exercises.

get_as_dict() → Dict[str, List[MealEntry]]¶: Returns a mapping of meal names to the list of entries for that meal.

Meal¶

class myfitnesspal.meal.Meal(name: str, entries: List[Entry])¶

Stores information about a particular meal.

__getitem__(value: int) → Entry¶: Returns a particular entry for thsi meal.

property entries: List[Entry]¶: Entries for this meal.

property name: str¶: Name of this meal.

property totals: Dict[str, float]¶: Nutrition totals for all entries for this meal.

Entry¶

class myfitnesspal.entry.Entry(name: str, nutrition: Dict[str, float])¶

Stores information about a single entry.

property name: str¶: Name of the item.

property totals: Dict[str, float]¶: Totals for the item.

get_as_dict() → MealEntry¶: Returns totals per-item as a dictionary.

property short_name: Optional[str]¶: Short name.

property unit: Optional[str]¶: Unit.

property quantity: Optional[str]¶: Quantity.

Exercise¶

class myfitnesspal.exercise.Exercise(name: str, entries: List[Entry])¶

Shows information about your exercise.

__getitem__(value: int) → Entry¶: Returns a particular entry.

property entries: List[Entry]¶: List of entries.

property name: str¶: Name of exercise.

get_as_list() → List[MealEntry]¶: Returns exercises as a list of dictionaries.

Food Item¶

class myfitnesspal.fooditem.FoodItem(mfp_id: int, name: str, brand: Optional[str], verified: bool, calories: float, details: Optional[FoodItemNutritionDict] = None, confirmations: Optional[int] = None, serving_sizes: Optional[List[ServingSizeDict]] = None, client: Optional[Client] = None)¶

Stores information about a particular food item.

property details: FoodItemNutritionDict¶: Nutritional details.

property mfp_id: int¶: Myfitnespal ID for this item.

property name: str¶: Name

property brand: Optional[str]¶: Brand

property verified: bool¶: Verified?

property serving: Optional[str]¶: Serving

property calories: float¶: Calories

property calcium: float¶: Calcium

property carbohydrates: float¶: Carbohydrates

property cholesterol: float¶: Cholesterol

property fat: float¶: Fat

property fiber: float¶: Fiber

property iron: float¶: Iron

property monounsaturated_fat: float¶: Monounsaturated Fat

property polyunsaturated_fat: float¶: Polyunsaturated Fat

property potassium: float¶: Potassium

property protein: float¶: Protein

property saturated_fat: float¶: Saturated Fat

property sodium: float¶: Sodium

property sugar: float¶: Sugar

property trans_fat: float¶: Trans Fat

property vitamin_a: float¶: Vitamin A

property vitamin_c: float¶: Vitamin C

property confirmations: int¶: Confirmations

property servings: List[FoodItemServing]¶: Servings

Serving¶

class myfitnesspal.fooditemserving.FoodItemServing(serving_id: str, nutrition_multiplier: float, value: float, unit: str, index: int)¶

property serving_id: str¶: Serving ID

property nutrition_multiplier: float¶: Nutrition Multiplier

property value: float¶: Value

property unit: str¶: Unit

property index: int¶: Index

Note¶

class myfitnesspal.note.Note(note_data: NoteDataDict)¶

Stores information about a note

property type: Optional[str]¶: Type

property date: Optional[date]¶: Date

as_dict()¶: Returns data as a dictionary.

Exceptions¶

exception myfitnesspal.exceptions.MyfitnesspalError¶

exception myfitnesspal.exceptions.MyfitnesspalRequestFailed¶

exception myfitnesspal.exceptions.MyfitnesspalLoginError¶

Types¶

class myfitnesspal.types.CommandDefinition(**kwargs)¶

function: Callable¶

description: str¶

is_alias: bool¶

aliases: List[str]¶

class myfitnesspal.types.GoalDisplayDict(**kwargs)¶

id: str¶

display_type: str¶

nutrients: List[str]¶

class myfitnesspal.types.UnitPreferenceDict(**kwargs)¶

energy: str¶

weight: str¶

distance: str¶

height: str¶

water: str¶

class myfitnesspal.types.DiaryPreferencesDict(**kwargs)¶

default_foot_view: str¶

meal_names: List[str]¶

tracked_nutrients: List[str]¶

class myfitnesspal.types.UnitValueContainer(**kwargs)¶

unit: str¶

value: float¶

class myfitnesspal.types.GoalPreferencesDict(**kwargs)¶

workouts_per_week: int¶

weekly_workout_duration: int¶

weekly_exercise_energy: UnitValueContainer¶

weight_change_goal: UnitValueContainer¶

weight_goal: UnitValueContainer¶

diary_goal_display: str¶

home_goal_display: str¶

macro_goal_format: str¶

class myfitnesspal.types.LocationPreferencesDict(**kwargs)¶

time_zone: str¶

country_code: str¶

locale: str¶

postal_code: str¶

state: str¶

city: str¶

class myfitnesspal.types.AdminFlagDict(**kwargs)¶

status: str¶

has_changed_username: bool¶

forgot_password_or_username: bool¶

warnings: int¶

strikes: int¶

revoked_privileges: List¶

class myfitnesspal.types.AccountDict(**kwargs)¶

created_at: str¶

updated_at: str¶

last_login: str¶

valid_email: bool¶

registration_source: str¶

roles: List[str]¶

admin_flags: AdminFlagDict¶

class myfitnesspal.types.SystemDataDict(**kwargs)¶

login_streak: int¶

unseen_notifications: int¶

class myfitnesspal.types.UserProfile(**kwargs)¶

type: str¶

starting_weight_date: str¶

starting_weight: UnitValueContainer¶

main_image_url: str¶

main_image_id: Optional[Any]¶

birthdate: str¶

height: UnitValueContainer¶

first_name: Optional[str]¶

last_name: Optional[str]¶

sex: typing_extensions.Literal[M, F]¶

activity_factor: str¶

headline: Optional[str]¶

about: Optional[str]¶

why: Optional[str]¶

inspirations: List¶

class myfitnesspal.types.UserMetadata(**kwargs)¶

id: str¶

username: str¶

email: str¶

goal_displays: List[GoalDisplayDict]¶

unit_preferences: UnitPreferenceDict¶

diary_preferences: DiaryPreferencesDict¶

goal_preferences: GoalPreferencesDict¶

location_preferences: LocationPreferencesDict¶

account: AccountDict¶

system_data: SystemDataDict¶

step_sources: List¶

profiles: List[UserProfile]¶

class myfitnesspal.types.AuthData(**kwargs)¶

token_type: str¶

access_token: str¶

expires_in: int¶

refresh_token: str¶

user_id: str¶

class myfitnesspal.types.MealEntry(**kwargs)¶

name: str¶

nutrition_information: Dict[str, float]¶

class myfitnesspal.types.NoteDataDict(**kwargs)¶

body: str¶

type: str¶

date: str¶

class myfitnesspal.types.FoodItemNutritionDict(**kwargs)¶

calcium: float¶

carbohydrates: float¶

cholesterol: float¶

fat: float¶

fiber: float¶

iron: float¶

monounsaturated_fat: float¶

polyunsaturated_fat: float¶

potassium: float¶

protein: float¶

saturated_fat: float¶

sodium: float¶

sugar: float¶

trans_fat: float¶

vitamin_a: float¶

vitamin_c: float¶

class myfitnesspal.types.ServingSizeDict(**kwargs)¶

id: str¶

nutrition_multiplier: float¶

value: float¶

unit: str¶

index: int¶

class myfitnesspal.types.FoodItemDetailsResponse(**kwargs)¶

description: str¶

brand_name: Optional[str]¶

verified: bool¶

nutrition: FoodItemNutritionDict¶

calories: float¶

confirmations: int¶

serving_sizes: List[ServingSizeDict]¶

class myfitnesspal.types.NutritionInformation(**kwargs)¶

calories: str¶

carbohydrateContent: str¶

fiberContent: str¶

sugarContent: str¶

sodiumContent: str¶

proteinContent: str¶

fatContent: str¶

saturatedFatContent: str¶

monunsaturatedFatContent: str¶

polyunsaturatedFatContent: str¶

unsaturatedFatContent: str¶

transFatContent: str¶

class myfitnesspal.types.Recipe(**kwargs)¶

author: str¶

org_url: str¶

name: str¶

recipeYield: str¶

recipeIngredient: List[str]¶

nutrition: NutritionInformation¶

recipeInstructions: str¶

tags: List[str]¶

Upgrading from 1.x to 2.x¶

Between the 1.x and 2.x versions of this library, the mechanism used for authentication changed, and this change has an impact on how you instantiate the myfitnesspal.Client object.

For more information about why this change was necessary, see Issue #144.

Version 1.x (Obsolete)¶

Before getting started, you would store a password in your system keyring for the user account you would like to use (in this example: ‘myusername’).

In your code, you would then instantiate your myfitnespal.Client like this:

import myfitnesspal

client = myfitnesspal.Client('myusername')

Version 2.x (Current)¶

Before getting started, now you should open a web browser on the same computer you will be using this library from, go to https://myfitnesspal.com/, and log in to MyFitnessPal using the user account you would like to use.

In your code, you can then instantiate your myfitnespal.Client like this:

import myfitnesspal

client = myfitnesspal.Client()

Note that the instantiation no longer accepts a username, and instead reads the log in information directly from your browser.

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 or start a discussion.

Python Myfitnesspal¶

Getting Started¶

Installation¶

Authentication¶

Using the Command-Line API¶

`day [$DATE]`¶

How-to Guides¶

Accessing your Diary¶

Hints¶

Accessing a Friend’s Diary¶

Accessing your Exercises¶

Accessing your Measurements¶

Searching for Foods¶

Accessing Reports¶

Use on Windows Subsystem for Linux¶

Contributing¶

I think I’ve found a bug¶

I want to add a new feature¶

I have a feature idea¶

API¶

Client¶

Day¶

Meal¶

Entry¶

Exercise¶

Food Item¶

Serving¶

Note¶

Exceptions¶

Types¶

Upgrading from 1.x to 2.x¶

Version 1.x (Obsolete)¶

Version 2.x (Current)¶

Indices and tables¶

Python Myfitnesspal

Navigation

Related Topics

Python Myfitnesspal¶

Getting Started¶

Installation¶

Authentication¶

Using the Command-Line API¶

day [$DATE]¶

How-to Guides¶

Accessing your Diary¶

Hints¶

Accessing a Friend’s Diary¶

Accessing your Exercises¶

Accessing your Measurements¶

Searching for Foods¶

Accessing Reports¶

Use on Windows Subsystem for Linux¶

Contributing¶

I think I’ve found a bug¶

I want to add a new feature¶

I have a feature idea¶

API¶

Client¶

Day¶

Meal¶

Entry¶

Exercise¶

Food Item¶

Serving¶

Note¶

Exceptions¶

Types¶

Upgrading from 1.x to 2.x¶

Version 1.x (Obsolete)¶

Version 2.x (Current)¶

Indices and tables¶

`day [$DATE]`¶