CodeCow/30-Days-Of-Python
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@ -214,9 +214,24 @@ person = {
"skills": ["JavaScrip", "React", "Python"]
}
# let's convert it to json
person_json = json.dumps(person)
person_json = json.dumps(person, indent=4) # indent could be 2, 4, 8. It beautify the json
print(type(person_json))
print(person_json)
```
```sh
# when you it is printed it does not have the quote but actually it is a string
# JSON does not have type, it is a string type.
<class 'str'>
{
"name": "Asabeneh",
"country": "Finland",
"city": "Helsinki",
"skills": [
"JavaScrip",
"React",
"Python"
]
}
```
### Saving as JSON file
We can also save our data as a json file. Let's save it as a json file using the following steps.
@ -245,18 +260,24 @@ CSV stands for comma separated values. CSV is a simple file format used to store
```
**Example:**
```py
import csv
with open('./files/csv_example.csv') as f:
csv_reader = csv.reader(f, delimiter=',')
csv_reader = csv.reader(f, delimiter=',') # w use, reader method to read csv
line_count = 0
for row in csv_reader:
if line_count == 0:
print(f'Column names are {", ".join(row)}')
print(f'Column names are :{", ".join(row)}')
line_count += 1
else:
print(
f'\t{row[0]} is a teachers. He lives in {row[1]}, {row[2]}.')
line_count += 1
print(f'Processed {line_count} lines.')
print(f'Number of lines: {line_count}')
```
```sh
Column names are :name, country, city, skills
Asabeneh is a teachers. He lives in Finland, Helsinki.
Number of lines: 2
```
### File with xlsx Extension
To read excel we need to install *xlrd* package. We will cover this after we cover package installing using pip.
@ -296,40 +317,85 @@ field skills
```
## 💻 Exercises: Day 19
1. Read the countries data file in data directory:
1. Writ a function which count number of lines and number of words from a text. All the files are in data the folder:
1. Read obama_speech.txt file and count number of lines and now of words
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
2. Create a function which create the ten most populated countries
2. Extract all incoming emails from the email_exchange_big.txt file.
3. 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))
```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')
]
[(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))
print(most_spoken_languages(filename='./data/countries_data.json', 3))
[(91, 'English'),
(45, 'French'),
(25, 'Arabic')
]
```
[(10, 'the'),
(8, 'be'),
(6, 'to'),
(6, 'of'),
(5, 'and')]
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}]
```
4. Use the function you made at question number 3 to find out:
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
```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')]
```
3. Use the function you made at question number 3 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)
5. 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
6. Find the 10 most repeated words in the romeo_and_juliet.txt
7. Read the [hacker news csv](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/data/hacker_news.csv) file and find out:
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:
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