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6 Foods With More Vitamin K Than Broccoli


A serving of broccoli provides more than half of the Daily Value of vitamin K.Credit: SherSor / Getty Images
A serving of broccoli provides more than half of the Daily Value of vitamin K.
Credit: SherSor / Getty Images
  • A cup of broccoli has 77.5 micrograms of vitamin K.
  • You can get a lot of vitamin K from broccoli, but many other foods contain even more of the nutrient.
  • Some of the richest sources of vitamin K include Swiss chard, collard greens, and beet greens, all of which have more vitamin K per serving than broccoli.

Your body needs vitamin K for blood clotting, strong bones, and a healthy heart. Broccoli is rich in vitamin K, with 1 cup of the vegetable containing 77.5 micrograms of the nutrient. That's about 64% of the Daily Value. But many other foods have more vitamin K per serving than broccoli.

1. Swiss Chard

Credit: alvarez / Getty Images
Credit: alvarez / Getty Images
  • Vitamin K content: 598 micrograms (mcg), 498% of the Daily Value (DV)
  • Serving size: 2 cups, raw
  • Other health benefits: Swiss chard is packed with vitamins A and C. One serving gives you about 24% of the DV for both vitamins. These vitamins help support the immune system. Vitamin A also helps keep your eyes, heart, and lungs healthy. Vitamin C helps your body absorb iron and make collagen. Swiss chard also provides about 14% of the DV for magnesium. Your body needs magnesium for brain, bone, muscle, and heart health, as well as blood sugar regulation.
  • How to use it:  Swiss chard is very versatile. You can use its leaves to wrap meals or make omelets, soups, stews, or even chips.

2. Collard Greens

Credit: bhofack2 / Getty Images
Credit: bhofack2 / Getty Images
  • Vitamin K content: 314 mcg, 261% of the DV
  • Serving size: 2 cups, raw
  • Other health benefits: A serving of collard greens gives you about 20% of the DV for vitamin A, 28% for vitamin C, and 11% for vitamin E—all antioxidant nutrients. Antioxidants help protect against cell damage and support the immune system. Collard greens are also high in fiber. A serving provides about 10% of the daily fiber recommendation. They also provide about 23% of the DV for folate. Your body needs folate to repair DNA and make red blood cells that carry oxygen. It’s especially important during pregnancy for the baby’s brain development.
  • How to use it: Steaming or cooking collard greens reduces their bitterness. They’re great to add to soups, stews, and stir-fries. You can also use collard greens as a substitute for vine leaves when making Mediterranean-style dolma.

3. Beet Greens 

Credit: Kinga Krzeminska / Getty Images
Credit: Kinga Krzeminska / Getty Images
  • Vitamin K content: 304 mcg, 253% of the DV
  • Serving size: 2 cups, raw
  • Other health benefits: Beet greens may help support the body’s antioxidant system. One study showed that drinking juice made from beet greens and stalks supports the body’s antioxidant defenses. They also provide over 26% of the DV for vitamin A and 24% for vitamin C. Beet greens are also rich in iron compared to most green leafy vegetables. One serving provides about 10% of the DV. Iron is essential for producing hemoglobin, a protein in red blood cells that carries oxygen.
  • How to use it: Sautéed or boiled beet greens pair great with nuts, beans, grains, and cheeses.

4. Spinach

Credit: Minh Hoang Cong / Getty Images
Credit: Minh Hoang Cong / Getty Images
  • Vitamin K content: 290 mcg, 241% of the DV
  • Serving size: 2 cups, raw
  • Other health benefits: Spinach contains nitrates, compounds that support healthy blood pressure. A study showed that eating a diet high in nitrates (from spinach) improved blood pressure in people without chronic conditions. Spinach is also rich in vitamins A, E, folate, and magnesium.
  • How to use it: Simmer spinach with chickpeas and spices like curry, then serve it with naan or rice.

5. Mustard Greens

Credit: VU PHAM VAN / Getty Images
Credit: VU PHAM VAN / Getty Images
  • Vitamin K content: 288 mcg, 240% of the DV
  • Serving size: 2 cups, raw
  • Other health benefits: Mustard greens contain sulfur compounds. These compounds might support the body’s natural detoxification. Sulfur compounds may also help fight damaged cells and infections. Mustard greens are rich in fiber, with one serving providing over 12% of the daily recommended amount. They also provide 86% of the DV for vitamin C, 16% of the DV for vitamin E, and 10% of the DV for iron.
  • How to use it: You can add mustard greens to salads, but they can be a little bitter. Cook mustard greens with olive oil and spices like pepper flakes and garlic, then add vinegar or lemon juice to balance the bitterness.

6. Natto

Credit: daniaphoto / Getty Images
Credit: daniaphoto / Getty Images
  • Vitamin K content: 283 mcg, 235% of the DV
  • Serving size: 1 ounce
  • Other health benefits: Natto is a fermented soybean product. The type of vitamin K found in natto stays longer in the blood. This means it may be more effective in supporting bone health. Natto also contains an enzyme called nattokinase. Some studies suggest that nattokinase may improve blood flow, blood pressure, and blood lipids. Natto is also rich in many nutrients, including protein and fiber, as well as iron, potassium, magnesium, and zinc.
  • How to use it: You can serve natto with rice, salads, soups, or noodles.



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Python Serialization | A Complete Guide on Python Serialization


Python Serialization – Table of Content

Serialization in Python

Serialization in python is a process to serialize data in a species that is user-friendly, human-readable, and easily inspected. There are two very common python serialization libraries that serialize data objects in python. They are ‘HDF5’ and ‘Pickle’ which take dictionaries as well as Tensorflow models for storage purposes and transmission.

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Why Python Serialization?

The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. It can also be used to transmit data on a server network and the user can access it on any system later on.

The process of serialization is also very helpful for projects related to data science. For instance, the process of dataset preprocessing can be very time-consuming, hence preprocessing is done just once that too before saving the data on the disk. It is preferred that the user performs preprocessing each time he uses it. It also eliminates memory limitation problems for big data too which is heavy for loading in the memory as a single piece. So when the data is split into smaller chunks, the user is able to load every single chunk for preprocessing, and he can then save the outputs to the disk, removing all the data chunks from the memory.

Python Serialization: Text Based

The process of textual serialization means serializing the data in some specific format that is easy to understand, human-readable as well as easily inspected. Formats which are text-based are mainly language agnostic and they can be formed with the help of any language related to programming.

JSON is a standard format that is used to exchange data between servers and web clients. JSON is known to serialize the objects in a plain text file format and allow for easy visual identification to the user. JSON stores the objects in the form of key-value pairs, just like a dictionary in Python. JSON is a built-in library in python which makes it a breeze for the user to work with JSON. 

It is very easy to perform JSON serialization just like creating a JSON file and dumping the object. This is done with the help of the dump() method. This method has two arguments which are:  

  • The object user is serializing
  • File which will store the serialized object.

Python JSON has two main functions which it works with:

  • dump(): This function helps to convert a Python object into JSON format
  • Loads(): This function helps to convert the JSON string back into a Python object.

The table below will show the conversion of the python data type into a JSON type:

dict-object
List, tuple- array
str- String
True- true
Int, float- Number
False- false
None- null

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YAML

YAML is not a Markup Language but it is actually a parent set of JSON made in a way to be more comprehensible to the user. The most important and distinguishing feature of YAML is the capacity to create references for other objects in the same file. Another most important advantage is that it is possible to write comments in python. This feature has proved very useful to work with the configuration files also.

Python Serialization: Binary Formats

It is not possible for binary formats in serialization to be human-readable; however they are faster in general and also require much lesser space than text-based counterparts. Let us see some very popular binary formats below:

Pickle

It is a very popular format for python serialization. It is used to serialize almost all the Python object types. Pickle is considered to be an original serialization format used for Python, hence when a user plans to serialize objects in python that he expects to share and he must use with many other languages used for programming, he has to be mindful of the issues such as cross-compatibility. Similarly, pickle works in the same way for various Python versions. The user cannot unpickle a file present in the XXX version, which he picked in the python ZZZ version. So by doing such unnecessary changes, the execution of malicious code gets tough.

Let us see an example below and understand how pickling is performed in python:


import pickle
 
class example_class:
    x_number = 10
    x_string = "Welcome to the tutorial"
    x_list = [10, 20, 30]
    x_dict = {"Heya": "x", "How": 5, "you": [10, 20, 30]}
    x_tuple = (2, 3)
 
my_object = example_class()
 
my_pickled_object = pickle.dumps(my_object)  
print(f"This would be pickled object:\n{my_pickled_object}\n")
 
my_object.a_dict = None
 
my_unpickled_object = pickle.loads(my_pickled_object) 
print(
    f"The dictionary of unpickled object is:\n{my_unpickled_object.a_dict}\n")
 
 Output
This would be pickled object:
b'\x80\x04\x95!\x00\x00\x00\x00\x00\x00\x00\x8c\x08__main__\x94\x8c\rexample_class\x94\x93\x94)\x81\x94.'
 
Traceback (most recent call last):
  File "", line 19, in 
AttributeError: 'example_class' object has no attribute 'a_dict'
> 

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Module Interface for Pickling and Unpickling

The data format is always Python-specific for the pickle module. That is why it is always important to write the essentially required code when the user is performing the process of serialization or deserialization. dumps() is the Python function that is used to serialize an object hierarchy whereas loads() is the function that is used to de-serialize the same.

Pickle Protocols

Protocols in pickle act like the convention measures to deconstruct and construct the python objects. There are in total of 5 protocols that a user can use in pickling. Whenever a user uses a higher protocol version, he will need the latest version of Python to obtain the highly compatible as well as readable pickle.

Protocol version 0: This version is readable by humans. It is compatible to use with data and interfaces from the older python versions.
Protocol version 1: It is known to be an old binary format. Just like protocol version 0, it is also compatible with older python versions.
Protocol version 2: It came into effect during the release of python version 2.3. This version is well known for providing new styles in picking.
Protocol version 3: This version was discovered during the release of python version 3.0. It is famous for supporting byte objects however the major drawback with this version is it gets unpicked by python version 2.0
Protocol version 4: This version was discovered during the release of python version 3.4. This is able to support large objects and various different objects can be picked too. It is also famous for supporting data optimization.

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Numpy

It is a very popular python library used by the user to work with large and multidimensional arrays as well as matrices. It stands for numerical python. They are open source and free to use but slow to process. NumPy arrays can be stored in one continuous place in the memory; however this same is not possible for lists. Processes can therefore access as well as manipulate the arrays very efficiently.

Let us see an example below and understand how the Numpy library is used in python:


import numpy as np
arr = np.array( [[ 10, 20, 30],
[ 40, 20, 50]] )
 
print("The type of array is: ", type(arr))
 
print("The no of dimensions are: ", arr.ndim)
 
print("The shape of the array is: ", arr.shape)
 
print("The size of the array is: ", arr.size)
 
print("Array stores elements of the type: ", arr.dtype)
 
 Output
The type of array is:  <class 'numpy.ndarray'>
The no of dimensions are:  2
The shape of the array is:  (2, 3)
The size of the array is:  6
Array stores elements of the type:  int64
> 

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Conclusion

Serialization is a process that aims at simplifying the data storage methods for a data scientist. Serialization in Python is one of the most important features that ease the data conversion interface of the data. In this article, we have talked about why we need serialization. The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. 

We have also discussed JSON and YAML in python. Then we talked about binary formats of python serialization which are pickle and NumPy. In this sub-topic, we will also have a glance at module instances of pickling and unpickling along with pickle protocols. Now we will be discussing some frequently asked questions by the developers and will give solutions for them.

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