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Find Missing Number in a consecutive arranging array

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Data Analysis vs. Data Analytics: What's the Difference?

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Scrabble

In this project, you will process some data from a group of friends playing scrabble. You will use dictionaries to organize players, words, and points.

There are many ways you can extend this project on your own if you finish and want to get more practice!

N.B: You should go through each task to complete this project.
Post your solution via @PYTHONETHBOT
Don't post your solution in the group
Follow instructions accordingly

Tasks
letters = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]

points = [1, 3, 3, 2, 1, 4, 2, 4, 1, 8, 5, 1, 3, 4, 1, 3, 10, 1, 1, 1, 1, 4, 4, 8, 4, 10]

1. I have provided you with two lists, letters and points. You would like to combine these two into a dictionary that would map a letter to its point value.

Using a list comprehension and zip, create a dictionary called letter_to_points that has the elements of letters as the keys and the elements of points as the values.

2. Our letters list did not take into account blank tiles. Add an element to the letter_to_points dictionary that has a key of " " and a point value of 0.

3. We want to create a function that will take in a word and return how many points that word is worth.
Define a function called score_word that takes in a parameter word.

4. Inside score_word, create a variable called point_total and set it to 0.

5. After defining point_total, create a for loop that goes through the letters in word and adds the point value of each letter to point_total.

You should get the point value from the letter_to_points dictionary. If the letter you are checking for is not in letter_to_points, add 0 to the point_total.

6. After the for loop is finished, return point_total.

7. Let’s test this function! Create a variable called asibeh_points and set it equal to the value returned by the score_word() function with an input of "ASIBEH".

8. We expect the word ASIBEH to earn 11 points:

(A + S + I + B + E + H)

Let’s print out asibeh_points to make sure we got it right.

9. Create a dictionary called player_to_words that maps players to a list of the words they have played. This table represents the data to transcribe into your dictionary:
player1 wordNerd Lexi Con Prof Reader
BLUE EARTH ERASER ZAP
TENNIS EYES BELLY COMA
EXIT MACHINE HUSKY PERIOD

10. Create an empty dictionary called player_to_points.

11. Iterate through the items in player_to_words. Call each player player and each list of words words.

Within your loop, create a variable called player_points and set it to 0.

12. Within the loop, create another loop that goes through each word in words and adds the value of score_word() with word as an input.

13. After the inner loop ends, set the current player value to be a key of player_to_points, with a value of player_points.

14. player_to_points should now contain the mapping of players to how many points they’ve scored. Print this out to see the current standings for this game!

If you’ve calculated correctly, wordNerd should be winning by 1 point.

====GOOD LUCK=====

10 Python Tricks You Should Know

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Python POP QUIZ: What is the output of the following code?

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Detecting and removing outliers using boxplot

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How to Detect and Remove Outliers using Interquantile Range in Python

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Dear Learners,
Today, we will release the first episode of how to setup MongoDb in Vs Code for better code management.
Stay Tuned.

MongoDB Basics and Setup: Your First Steps with MongoDB and VS Code : https://www.youtube.com/watch?v=nm-7rM5mnYE

Next we will share you how to create collections and read data. Stay tuned!!

In this topic modeling project-based tutorial, I have gone through the following steps:

1. Loads the documents(Generating sample documents)
2. Preprocesses the text by removing stop words and stemming words.
3. Creates a TF-IDF vector representation of the documents.
4. Performs LDA topic modeling with the specified number of topics.
5. Extracts the document-topic weight matrix.
6. Prepares the data for CSV format, including document IDs and topic weights.
7. Saves the results to the specified CSV file. https://youtu.be/uJCB2hRCB60

A technique used to extract features from the text. It counts how many times a word appears in a document (corpus), and then transforms that information into a dataset.
https://youtu.be/tn-Tvi8CHmg

Benefits of Python for Database Programming
- Python is a popular scripting language to connect to the database and analyzes the data.
- Python ecosystem: - NumPy, pandas, matplotlib, SciPy
- Ease of use
- Python supports relational database systems
- Python database API's to connect to the database
- Detailed documentation: The python is easily available

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