zucked 1.0.0

Python library for parsing through facebook messenger data

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zucked: analyze your Facebook Messenger data

What is it?

zucked is a quick and easy way to veiw your facebook messenger data.

Main Features

• Easy viewing of your most used words on Facebook Messenger
• See another facebook users most sent words to you
• Check the amount of messages you have sent a person or group
• Search a word or phrase that you might have sent or recieved to see: The message the word or phrase was in, as well as who sent it and when the message was sent

Install

The source code is currently hosted on GitHub at: https://github.com/Jordan-Milne/zucked

The latest released version is available at the Python package index (PyPI)

pip install zucked

Tutorial

• Downlaod your Facebook Messenger data (How to)

  • When choosing file type make sure it is JSON and not HTML

• Create a python file in the same directory as the "inbox" folder from your download - not inside the inbox folder

Import

To import zucked, type:

import zucked as zd

read_messages

save read_messages as a variable, which we will be calling methods on later.

Example:

ms = zd.read_messages

top_words()

call top_words(facebook_name, stop_words=True, period='all time') on ms to return an ordered list of the most common words the user sent and how many times they sent it: This method works for top words you have sent and top words you have recieved from a certain user Example:

ms.top_words('Jordan Milne')

Returns:

[('I', 3), ('love', 2), ('data', 1)]

If you want stop words filtered out, (what are stop words) and just the top words for a certain year then the method would simply be:

ms.top_words('Jordan Milne', stop_words=False, period='2016')

top_convos

call top_convos(facebook_name) on ms to return an ordered list of the name(s) of the person or group and the amount of messages you sent to that person or group:

Example:

ms.top_convos('Jordan Milne')

Returns:

[('User 1', 33),
 ('User 2', 21)]

If you want the top conversations for a certain year then the method would simply be:

ms.top_words('Jordan Milne', period='2016')

search_messages()

call search_messages(facebook_name, value) on ms to return a list that has the full message containing the word or phrase input as value, who the message was sent to, and the date and time the message was sent.

Example:

ms.search_messages('Jordan Milne', 'hello')

Returns:

[{'Message': 'I'm sorry but, hello world',
  'Sent to': 'Auston Matthews',
  'Date': '2019-06-09 11:46:54'}]

This method works for messages you have sent and messages you have recieved from a certain user

format_top_words()

call format_top_words(facebook_name, input_list, year) on ms to return a nested dictionary with the year and counts of the list of words that the user inputs that is specifically formatted for plotting.

Example:

ms.format_top_words('Jordan Milne', ['Matthews','Tavares','Marner','Andersen'], '2019')

Returns:

{'2019': {'Matthews': 75, 'Andersen': 70, 'Marner': 17, 'Tavares': 10}}

Now for the fun part. If you want to see how often you used your top 5 all time words each year it can be done in a few simple steps.

Get all years into the same dictionary using this for loop:

example={}
for i in range(2010,2021):
    example.update(ms.format_top_words('Jordan Milne',['Matthews','Tavares','Marner','Andersen'], str(i)))

Now that we have the data collected and organized we are ready to plot:

import matplotlib.pyplot as plt

# get inner keys
inner_keys = list(example.values())[0].keys()

# x-axis is the outer keys
x_axis_values = list(example.keys())

# loop through inner_keys
plt.title("Facebook Messenger Word Count")
plt.xlabel("Year")
plt.ylabel("Count");
for x in inner_keys:

    # create a list of values for inner key
    y_axis_values = [v[x] for v in example.values()]

    # plot each inner key
    plt.plot(x_axis_values, y_axis_values, label=x)
    plt.legend()

The ouput should look like: