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Task Related to Uber Drive Dataset.
Dataset Download from here
Q1:
Load the necessary libraries. Import and load the dataset with a name uber_drives .
Q 2:
Show the last 10 records of the dataset. (2 point)
Q 3:
Show the first 10 records of the dataset. (2 points)
Q 4:
Show the dimension(number of rows and columns) of the dataset. (2 points)
Q 5:
Show the size (Total number of elements) of the dataset. (2 points)
Q 6:
Display the information about all the variables of the data set. (2 points)
Hint: Information includes - Total number of columns,variable data-types, number of non-null values in a variable, and usage
Q 7:
Check for missing values. (2 points) - Note: Output should be boolean only.
Q 8:
How many missing values are present? (2 points)
Hint: Find out the total number of missing values across all the variables
Q 9:
Get the summary of the original data. (2 points).
Hint: Summary includes- Count,Mean, Std, Min, 25%,50%,75% and max
Note:Outcome will contain only numerical column.
Q 10:
Drop the missing values and store data in a new dataframe (name it"df") (2-points)
Note: Dataframe "df" will not contain any missing value
Q 11:
Check the information of the dataframe(df). (2 points)
Hint: Information includes - Total number of columns,variable data-types, number of non-null values in a variable, and usage
Q 12:
Get the unique start destinations. (2 points)
Note: This question is based on the dataframe with no 'NA' values
Hint- You need to print the unique destination place names in this and not the count.
Q 13:
What is the total number of unique start destinations? (2 points)
Note: Use the original dataframe without dropping 'NA' values
Q 14:
What is the total number of unique stop destinations. (2 points)
Note: Use the original dataframe without dropping 'NA' values.
Q 15:
Display all the Uber trips that has the starting point of San Francisco. (2 points)
Q 16:
Note: Use the original dataframe without dropping the 'NA' values.
Hint: You need to display the rows which has starting point of San Francisco. Try using loc function
Q 17:
What is the most popular starting point for the Uber drivers? (2 points)
Note: Use the original dataframe without dropping the 'NA' values.
Hint:Popular means the place that is visited the most
Q 18:
What is the most popular dropping point for the Uber drivers? (2 points)
Note: Use the original dataframe without dropping the 'NA' values.
Hint: Popular means the place that is visited the most
Q 19:
List the most frequent route taken by Uber drivers. (3 points)
Note: This question is based on the new dataframe with no 'na' values.
Hint-Print the most frequent route taken by Uber drivers (Route= combination of START & END points present in the Data set). One may use Groupby function
Q 20:
Display all types of purposes for the trip in an array. (3 points)
Q 21:Note: This question is based on the new dataframe with no 'NA' values
Plot a bar graph of Purpose vs Miles(Distance). (3 points)
Note: Use the original dataframe without dropping the 'NA' values.
Hint:You have to plot total/sum miles per purpose
Q 22:
Display a dataframe of Purpose and the distance travelled for that particular Purpose. (3 points)
Note: Use the original dataframe without dropping "NA" values
Q 23:
Plot number of trips vs Category of trips. (3 points)
Note: Use the original dataframe without dropping the 'NA' values.
Hint : You can make a countplot or barplot.
Q 24:
What is proportion of miles that are covered as Business trips and what is the proportion of miles that are covered as Personal trips? (3 points)
Note:Use the original dataframe without dropping the 'NA' values. The proportion calculation is with respect to the 'miles' variable.
Hint: Proportion of miles covered as business trips= (Total Miles clocked as Business Trips)/ (Total Miles)
Proportion of miles covered as personal trips= (Total Miles clocked as Personal Trips)/ (Total Miles)
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