# Data Analytics Assignment Help | Sample Practice Paper For Beginners

Here we have provide some important data analytics questions. You need to solve it or if you need help to solve this paper then we are ready to help you. Here you can get help with an affordable price.

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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