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@ -322,80 +322,77 @@ field skills
2. Read michelle_obama_speech.txt file and count number of lines and now of words
3. Read donald_speech.txt file and count number of lines and now of words
4. Read melina_trump_speech.txt file and count number of lines and now of words
2. Read the countries_data.json data file in data directory:
1. Create a function which find the ten most spoken languages
```py
print(most_spoken_languages(filename='./data/countries_data.json', 10))
[(91, 'English'),
(45, 'French'),
(25, 'Arabic'),
(24, 'Spanish'),
(9, 'Russian'),
(9, 'Portuguese'),
(8, 'Dutch'),
(7, 'German'),
(5, 'Chinese'),
(4, 'Swahili'),
(4, 'Serbian')
]
print(most_spoken_languages(filename='./data/countries_data.json', 3))
[(91, 'English'),
(45, 'French'),
(25, 'Arabic')
]
```
1. Create a function which create the ten most populated countries
```py
print(most_populated_countries(filename='./data/countries_data.json', 10))
[{'country': 'China', 'population': 1377422166},
{'country': 'India', 'population': 1295210000},
{'country': 'United States of America', 'population': 323947000},
{'country': 'Indonesia', 'population': 258705000},
{'country': 'Brazil', 'population': 206135893},
{'country': 'Pakistan', 'population': 194125062},
{'country': 'Nigeria', 'population': 186988000},
{'country': 'Bangladesh', 'population': 161006790},
{'country': 'Russian Federation', 'population': 146599183},
{'country': 'Japan', 'population': 126960000}]
print(most_populated_countries(filename='./data/countries_data.json', 3))
[{'country': 'China', 'population': 1377422166},
{'country': 'India', 'population': 1295210000},
{'country': 'United States of America', 'population': 323947000}]
```
1. Extract all incoming emails from the email_exchange_big.txt file.
2. Find the most common words in the English language. Call the name of your function find_most_common_words, it will take two parameters which are a string or a file and a positive integer. Your function will return an array of tuples in descending order. Check the output
2. Read the countries_data.json data file in data directory, create a function which find the ten most spoken languages
```py
print(find_most_common_words('sample.txt', 10))
[(10, 'the'),
(8, 'be'),
(6, 'to'),
(6, 'of'),
(5, 'and'),
(4, 'a'),
(4, 'in'),
(3, 'that'),
(2, 'have'),
(2, 'I')]
print(find_most_common_words('sample.txt', 5))
[(10, 'the'),
(8, 'be'),
(6, 'to'),
(6, 'of'),
(5, 'and')]
print(most_spoken_languages(filename='./data/countries_data.json', 10))
[(91, 'English'),
(45, 'French'),
(25, 'Arabic'),
(24, 'Spanish'),
(9, 'Russian'),
(9, 'Portuguese'),
(8, 'Dutch'),
(7, 'German'),
(5, 'Chinese'),
(4, 'Swahili'),
(4, 'Serbian')
]
print(most_spoken_languages(filename='./data/countries_data.json', 3))
[(91, 'English'),
(45, 'French'),
(25, 'Arabic')
]
```
3. Use the function you made at question number 3 to find out:
3. Read the countries_data.json data file in data directory,create a function which create the ten most populated countries
```py
print(most_populated_countries(filename='./data/countries_data.json', 10))
[{'country': 'China', 'population': 1377422166},
{'country': 'India', 'population': 1295210000},
{'country': 'United States of America', 'population': 323947000},
{'country': 'Indonesia', 'population': 258705000},
{'country': 'Brazil', 'population': 206135893},
{'country': 'Pakistan', 'population': 194125062},
{'country': 'Nigeria', 'population': 186988000},
{'country': 'Bangladesh', 'population': 161006790},
{'country': 'Russian Federation', 'population': 146599183},
{'country': 'Japan', 'population': 126960000}]
print(most_populated_countries(filename='./data/countries_data.json', 3))
[{'country': 'China', 'population': 1377422166},
{'country': 'India', 'population': 1295210000},
{'country': 'United States of America', 'population': 323947000}]
```
4. Extract all incoming emails from the email_exchange_big.txt file.
5. Find the most common words in the English language. Call the name of your function find_most_common_words, it will take two parameters which are a string or a file and a positive integer. Your function will return an array of tuples in descending order. Check the output
```py
print(find_most_common_words('sample.txt', 10))
[(10, 'the'),
(8, 'be'),
(6, 'to'),
(6, 'of'),
(5, 'and'),
(4, 'a'),
(4, 'in'),
(3, 'that'),
(2, 'have'),
(2, 'I')]
print(find_most_common_words('sample.txt', 5))
[(10, 'the'),
(8, 'be'),
(6, 'to'),
(6, 'of'),
(5, 'and')]
```
1. Use the function, find_most_frequent_words to find out:
1. The ten most frequent words used in [Obama's speech](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/obama_speech.txt)
2. The ten most frequent words used in [Michelle's speech](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/michelle_obama_speech.txt)
3. The ten most frequent words used in [Trump's speech](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/donald_speech.txt)
4. The ten most frequent words used in [Melina's speech](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/melina_trump_speech.txt)
4. Write a python application which checks similarity between two texts. It takes a file or a string as a parameter and it will evaluate the similarity of the two texts. For instance check the similarity between the transcripts of [Michelle's](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/michelle_obama_speech.txt) and [Melina's](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/melina_trump_speech.txt) speech. You may need a couple of functions, function to clean the text(clean_text), function to remove support words(remove_support_words) and finally to check the similarity(check_text_similarity). List of [stop words](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/stop_words.py) are in the data directory
5. Find the 10 most repeated words in the romeo_and_juliet.txt
6. Read the [hacker news csv](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/hacker_news.csv) file and find out:
2. Write a python application which checks similarity between two texts. It takes a file or a string as a parameter and it will evaluate the similarity of the two texts. For instance check the similarity between the transcripts of [Michelle's](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/michelle_obama_speech.txt) and [Melina's](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/melina_trump_speech.txt) speech. You may need a couple of functions, function to clean the text(clean_text), function to remove support words(remove_support_words) and finally to check the similarity(check_text_similarity). List of [stop words](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/stop_words.py) are in the data directory
3. Find the 10 most repeated words in the romeo_and_juliet.txt
4. Read the [hacker news csv](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/hacker_news.csv) file and find out:
1. Count the number of lines containing python or Python
2. Count the number lines containing JavaScript, javascript or Javascript
3. Count the number lines containing Java not JavaScript