diff --git a/readme19-21.md b/readme19-21.md
index 877937b..63adf12 100644
--- a/readme19-21.md
+++ b/readme19-21.md
@@ -325,6 +325,7 @@ field skills
 2. Read the countries_data.json data file in data directory, 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'),
@@ -338,6 +339,7 @@ field skills
     (4, 'Serbian')
     ]
     print(most_spoken_languages(filename='./data/countries_data.json', 3))
+
     [(91, 'English'),
     (45, 'French'),
     (25, 'Arabic')
@@ -346,6 +348,7 @@ field skills
 3. Read the countries_data.json data file in data directory,create a function which create the ten most populated countries
     ```py
     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},
@@ -358,6 +361,7 @@ field skills
     {'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}]
@@ -365,6 +369,7 @@ 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
+
     print(find_most_common_words('sample.txt', 10))
 
     [(10, 'the'),
@@ -377,6 +382,7 @@ field skills
     (3, 'that'),
     (2, 'have'),
     (2, 'I')]
+
     print(find_most_common_words('sample.txt', 5))
 
     [(10, 'the'),
@@ -385,14 +391,14 @@ field skills
     (6, 'of'),
     (5, 'and')]
 ```
-1. Use the function, find_most_frequent_words to find out:
+6. 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)
-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:
+7. 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
+8. Find the 10 most repeated words in the romeo_and_juliet.txt
+9. 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
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