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Amigos chat py



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in. Whatsapp claims that nearly 55 billion messages are sent each day. The average user spends minutes per week on Whatsapp, and is a member of plenty of groups. With this treasure house of data right under our very noses, it is but imperative that we embark on a mission to gain insights on the messages our phones are forced to bear witness to.

This article aims to serve as a step-by-step guide to build your own whatsapp conversation analyzer, and is divided into the following 3 main topics:. Before amigos chat py can get started, ensure that the following packages are installed in your Python environment I recommend using Jupyter since you can see intermediate outputs easily while following the steps in this tutorial :. First off, we require a whatsapp conversation to analyze. Important Note : When prompted by whatsapp, ensure that you do not export any media otherwise it might take ages to export.

Download the exported chat from your inbox. It should resemble the following:. Just like raw vegetables have to be cooked and garnished with a variety of spices to make them palatable to humans, so also this plain text file will have to be parsed and tokenized in a meaningful manner in order to be served stored in a Pandas dataframe:. Hereafter, whenever I wish to draw your attention to different tokens in a string sI will present to you 2 lines.

In our sample line of text, our main objective is to automatically break down the raw message into 4 tokens, and we will see how to go about this task in the next section:. Let us define a new method called startsWithDateTime :. The following diagram shows how regex matching detects the date and time in our message:.

The following diagram gives a brief overview of all the messages detected in the sample text file:.

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Coming back to our sample line, Before we ran the startsWithDateTime method, no tokens were detected in our raw sample message:. After we run the startsWithDateTime method, 2 tokens were detected in our processed sample message:. Now that we have identified lines that contain new messages with Date and Time components, let us move to the next part of the message everything after the hypen :. Once again, we will require some more regular expression matching. Our objective is to detect the author of this message.

Keeping these rules in mind, let us now define a method called startsWithAuthor which finds strings that match at least one of the aforementioned rules:. The following diagram shows how regex matching detects the author in our message:. The following diagram gives a brief overview of all the authors detected in the sample text file:. Before we ran the startsWithAuthor method, 2 token s had been detected in our processed sample message:. After we run the amigos chat py method, 4 tokens are detected in our processed sample message. Note: You might be wondering how the Message token appeared out of thin air.

Well, once we have detected the DateTime and Author tokens, what we are left with is the remaining portion of the string which is the de facto Message token. Now that we have been able to identify the DateTimeAuthor and Message tokens in a single message, it is time to split each line based on the separator tokens like commas, hyphens -colons : and spacesso that the required tokens can be extracted and saved in a dataframe. amigos, chat, hacer pareja, conocer gente, amistad. hm, n

This time, let me invert things by highlighting the separator tokens instead of the DateTimeAuthor and Message tokens:. Let us define a new method called getDataPoint for the task of splitting string based on the separator tokens to extract the tokens of interest:. Sample output values are shown in comments beside each line. Note: Figuring out when the value of Author token can be None is left as an exercise to the reader. We have come to the last stage of data parsing, for which we will have to read the entire whatsapp text file, identify and extract tokens from each line and capture all data in tabular format within a list:.

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Initialize a pandas dataframe using the following code:. Finally, we have reached one of the most exciting parts of our journey — Data Exploration. It is time for us to unearth the interesting stories that all this data is trying to tell us. Firstly, let us take a look at what pandas has to say about our data frame df :. This command shows the of entries countunique entries, most frequently occurring entries top and frequency of the most frequently occurring entries freq for each column in the data frame.

The output might look like this:. Who are the most garrulous amigos chat py Remember how, a few sections earlier, I had given you an exercise to figure out that the author of certain messages can be None? Let us find all those messages which have no authors, using the following code:. Do you see any pattern in the messages here?

These messages represent any pictures, videos or audio messages. Let us find all media messages and analyze the of media amigos chat py sent by the top 10 authors who send media messages in the group, using the following code:. Do you spot any differences between the authors who send the most messages overall, and the authors who send the most media messages? This step could be categorized as data cleaning. Feel free to skip this step if you want to gain insights on the entire non-text data as well. It might be interesting to count the of letters and words used by each author in each message.

This step could be categorized as data augmentation. Now, let us describe the cleaned and augmented data frame. One important point to note here is the distinction made between columns containing continuous values vs. Try running the describe command on the entire data frame without specifying any columns. What do you observe?

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Let us take a step back and look at the overall picture. How many words and letters have been sent since the beginning of time which in this case, happens to be since the moment the group was conceived? How many words have been sent in total by each author, since the beginning of time? Looks like most messages contain only 1 word. I wonder what that word is!

Does it make sense to count the total of letters sent by each author since the beginning of time as well? So, here goes:. Looks like most messages contain only 1 or 2 letters.

Build your own whatsapp chat analyzer

Very interesting! Are these letters from the English language or some other symbols? Do you know the date on which the most of messages were sent in the history of your group?

Well, fear no more, for you will find out in just a second:. Was this the date when Thanos struck, rendering everyone panic-stricken and bursting with questions?

What date did you get? Do you remember anything ificant happening on this date? Do you lie awake at night wondering at what time of the day your group is most active? The truth will be revealed:. Looks like the group is mostly active at around PM at night. Be sure to message at this time to get a quicker response. What is the most suitable hour of the day at which to message to increase your chances of getting a response from someone? Now, you just have to run code similar to the ones for obtaining the top dates and times:.

Looks like messaging between 6 PM and 7 PM has the highest chances of eliciting responses from group members. You are more knowledgeable about your whatsapp conversations now!

What insights did you gain about your conversations? Did you find any of them useful? Feel free to add your thoughts in the comments section so that I can improve this guide. Or are they? Should I discard punctuations? Why do most messages contain only 1 word? Stay tuned for an article in the not too distant future, where I will try to dig deeper into some of these questions. Until then, Adios Amigos and Happy Exploring! Greeting from Me…yeah ME!

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