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Wayfair Technical Analyst OA – OA代做 – Weather Department Statistics – Find Top Students – 面试代面 – VO 辅助

Wayfair的da岗位,以下是take-home accessment 105min

Wayfair Technical Analyst OA 限时105分钟,这里展示其中2道题

Weather Department Statistics

There is a database containing temperature and humidity statistics by state for various countries. The database aims to return a list of states with their associated country names, displaying the state's average humidity and average temperature during November 2018. The average temperature is categorized into a weather type:

  • COLD - If 0 ≤ average monthly temperature < 15.
  • WARM - If 15 ≤ average monthly temperature < 30.
  • HOT - If ≥ 30 average monthly temperature.

The output should be formatted as: state.name | country.name | average_monthly_humidity | weather_type, ordered descending by average humidity and ascending by state name in case of ties. Average humidity should be displayed with four decimal places.

Database Schema


  1. country
    • id: INTEGER, primary key, the country's id number.
    • name: STRING, the country's name.
  2. state
    • id: INTEGER, primary key, the state's id number.
    • name: STRING, the state's name.
    • country_id: INTEGER, the country's id number.
  3. state_weather_stats
    • state_id: INTEGER, foreign key referencing state.id.
    • record_date: DATE, the date when the stat was recorded.
    • temperature: INTEGER, recorded temperature value.
    • humidity: INTEGER, recorded humidity value.

Sample Output

  • Alberta, Canada | 47.0000 | WARM
  • Bheri, Nepal | 41.0000 | WARM
  • British Columbia, Canada | 37.0000 | HOT
  • Dhawalagiri, Nepal | 31.5000 | COLD
  • Bagmati, Nepal | 20.5000 | COLD

Find Top Students


Given a dataset of marks scored by students in three different subjects (math, reading, and writing), the task is to analyze and process the data using a pandas DataFrame to identify top-performing students based on specific criteria.

Task Description

  1. Data Cleaning and Preprocessing
    • Remove Incomplete Data: Drop all students who have marks missing in two or more subjects.
    • Impute Missing Data: For students with marks missing in fewer than two subjects, fill in the missing marks with the median score for the respective subject.
  2. ID Cleaning
    • Standardize IDs: Clean the student_id column by removing all non-numeric characters to simplify identification.
  3. Calculations
    • Weighted Average: Calculate the weighted average of marks using the following weights:
      • Math: 50%
      • Reading: 20%
      • Writing: 30%
    • Sort and Filter: Order the students by their weighted average in descending order and filter out students whose average is above 70.

Implementation Steps

  • Data Cleaning: Students with incomplete data (missing marks in two or more subjects) are excluded from further analysis.
  • Imputation: Use the median of the available scores in each subject to fill any missing scores for students with fewer missing marks.
  • ID Transformation: Convert student_id entries to purely numeric form by stripping non-numeric characters.
  • Score Calculation and Sorting: Compute the weighted average for each student and sort the DataFrame by these scores in descending order.
  • Result Extraction: Extract and return the student_id of students whose weighted average score exceeds 70.


Given the following DataFrame:


After cleaning and processing, the DataFrame showing top students might look like:



In the given example, only student_id sde123as (cleansed to 123) met all the criteria after imputation and computation of the weighted average. The student's final score surpassed the threshold of 70, qualifying them as a top student.

Function Description

  • Function: topStudents(df)
  • Parameters: A pandas DataFrame df containing the student marks.
  • Returns: A pandas DataFrame listing the student IDs of the top students.


  • The DataFrame df will have at least 1 row and no more than 1000 rows.
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