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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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