PYTHON Challenge
START
GOODLUCK!
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
What is the datatype of the variable in the following line? X= 12
Integer
Float
String
Boolean
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
WRONG!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
9 25.600 POINTS
4 800 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
How is a Set indicated in Python?
curly braces { }
square brackets [ ]
parentheses ( )
curly braces with { : }
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
What is the output of the following code?
dict = {"ID":["name","John Doe"], "age": 30, "country": "USA"}
print(dict["ID"])
'name':'John Doe'
['name', 'John Doe']
'name'
'John Doe'
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
Which method is used to convert a string column into a datetime column in pandas?
df.parse_datetime()
df.convert_to_datetime()
df.string_to_datetime()
df.to_datetime()
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you sort a DataFrame by values in a specific column in descending order?
df.sort_values(['column_n\ ame'], ascending=False)
df.sort_value(['column_n\ ame'], ascending=False)
df.sort_by(column='column_n\ame', ascending=False)
df.sort(column='column_nam\e', ascending=False)
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
9 25.600 POINTS
4 800 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you replace Null with a specific value in a pandas DataFrame?
df.fillna()
df.replace_null()
df.rep_nul()
df.fill_na()
6 3.200 POINTS
1 100 POINTS
7 6.400 POINTS
2 200 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you drop a column 'B' in pandas DataFrame 'df'?
df.drop('B', axis=0)
df.drop('B', axis=1)
del(df['B'])
A & C are correct
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
Which of the following methods used to draw a Histogram using matplotlib?
df['Column'].plot\(kind='histogram')
df['Column'].plot\(kind='hist')
df['Column'].plot\(type='hist')
df['Column'].plot\(kind='histogram')
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you handle missing values by filling them with the mean value of the column 'B' in pandas?
df.fillna(['B']'mean')
df.fill_with_mean('B')
df.mean_fillna('B')
df.fillna(df['B'].mean())
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
9 25.600 POINTS
4 800 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
What does the df.dropna(axis=1) method do in pandas?
Drops columns with missing values
Drops rows with missing values
10
Drops rows and columns with missing values
None Of The Above
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
9 25.600 POINTS
4 800 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
WELL DONE!
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
YOU WIN!
1.000.000 POINTS
BACK
Python Challenge 2
Sayed Ali
Created on July 10, 2023
Python challenge for data analytics
Start designing with a free template
Discover more than 1500 professional designs like these:
View
Piñata Challenge
View
Pixel Challenge
View
Corporate Challenge Game
View
Catch the Stars
View
Movie Minigames
View
Question Wheel
View
Character Clue Game Mobile
Explore all templates
Transcript
PYTHON Challenge
START
GOODLUCK!
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
What is the datatype of the variable in the following line? X= 12
Integer
Float
String
Boolean
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
WRONG!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
9 25.600 POINTS
4 800 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
How is a Set indicated in Python?
curly braces { }
square brackets [ ]
parentheses ( )
curly braces with { : }
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
What is the output of the following code?
dict = {"ID":["name","John Doe"], "age": 30, "country": "USA"}
print(dict["ID"])
'name':'John Doe'
['name', 'John Doe']
'name'
'John Doe'
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
Which method is used to convert a string column into a datetime column in pandas?
df.parse_datetime()
df.convert_to_datetime()
df.string_to_datetime()
df.to_datetime()
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you sort a DataFrame by values in a specific column in descending order?
df.sort_values(['column_n\ ame'], ascending=False)
df.sort_value(['column_n\ ame'], ascending=False)
df.sort_by(column='column_n\ame', ascending=False)
df.sort(column='column_nam\e', ascending=False)
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
9 25.600 POINTS
4 800 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you replace Null with a specific value in a pandas DataFrame?
df.fillna()
df.replace_null()
df.rep_nul()
df.fill_na()
6 3.200 POINTS
1 100 POINTS
7 6.400 POINTS
2 200 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you drop a column 'B' in pandas DataFrame 'df'?
df.drop('B', axis=0)
df.drop('B', axis=1)
del(df['B'])
A & C are correct
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
Which of the following methods used to draw a Histogram using matplotlib?
df['Column'].plot\(kind='histogram')
df['Column'].plot\(kind='hist')
df['Column'].plot\(type='hist')
df['Column'].plot\(kind='histogram')
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
How can you handle missing values by filling them with the mean value of the column 'B' in pandas?
df.fillna(['B']'mean')
df.fill_with_mean('B')
df.mean_fillna('B')
df.fillna(df['B'].mean())
1 100 POINTS
6 3.200 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
4 800 POINTS
9 25.600 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
RIGHT!
8 12.800 POINTS
3 400 POINTS
9 25.600 POINTS
4 800 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
What does the df.dropna(axis=1) method do in pandas?
Drops columns with missing values
Drops rows with missing values
10
Drops rows and columns with missing values
None Of The Above
6 3.200 POINTS
1 100 POINTS
2 200 POINTS
7 6.400 POINTS
WRONG!
3 400 POINTS
8 12.800 POINTS
9 25.600 POINTS
4 800 POINTS
10 1.000.000 POINTS
5 1.600 POINTS
START OVER?
WELL DONE!
1 100 POINTS
6 3.200 POINTS
7 6.400 POINTS
2 200 POINTS
8 12.800 POINTS
3 400 POINTS
4 800 POINTS
9 25.600 POINTS
5 1.600 POINTS
10 1.000.000 POINTS
YOU WIN!
1.000.000 POINTS
BACK