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Asabeneh 2019-12-08 16:41:22 +02:00
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@ -330,7 +330,7 @@ field: skills
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, create a function which find the ten most spoken languages
```py
# You output should look like this
# Your output should look like this
print(most_spoken_languages(filename='./data/countries_data.json', 10))
[(91, 'English'),
(45, 'French'),
@ -344,7 +344,7 @@ field: skills
(4, 'Swahili'),
(4, 'Serbian')
]
# You output should look like this
# Your output should look like this
print(most_spoken_languages(filename='./data/countries_data.json', 3))
[(91, 'English'),
@ -354,7 +354,7 @@ field: skills
```
3. Read the countries_data.json data file in data directory,create a function which create the ten most populated countries
```py
# You output should look like this
# Your output should look like this
print(most_populated_countries(filename='./data/countries_data.json', 10))
[{'country': 'China', 'population': 1377422166},
@ -377,7 +377,7 @@ field: skills
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
# You output should look like this
# Your output should look like this
print(find_most_common_words('sample.txt', 10))
[(10, 'the'),