In This Exercise You'll Work With Data Collected Over Three

In This Exercise Youll Work With Data Collected Over Three Years From

In this exercise you'll work with data collected over three years from persons at the San Francisco International Airport (SFO). The data is stored in different file formats and coded in differing ways across the years. You will produce summary results from the data, create new datasets for subsequent analyses, and use Python and pandas for data cleaning and transformation tasks. The data for each year includes survey responses, with variable names and coding potentially differing, requiring careful reconciliation.

Specifically, you'll read in data files for 2014, 2015, and 2016 into pandas DataFrames, understand and reconcile variable coding differences, and compile combined datasets based on specific criteria. You'll create a multiyear ratings dataset focusing on variables related to airport assessments, cleanliness, safety, security processes, ease of navigation, and transportation. The dataset should include respondent IDs, the survey year, residence location, and other relevant variables, with appropriate handling of missing data.

Additionally, you'll identify the top three most common comments made by respondents in 2015 and 2016, summarize overall ratings based on respondent residence, profile respondents targeted for follow-up research, and save your data sets via pickling. Throughout, you will comment your Python code, ensure syntax correctness, and document your process.

Begin by examining the individual data files and dictionaries to understand variable names and coding schemes, then proceed with data merging, cleaning, analysis, and serialization as instructed. Remember, working from each year's data separately at first will facilitate understanding and accurate merging.

Paper For Above instruction

Introduction

The analysis of survey data collected over multiple years offers valuable insights into trends and patterns in customer satisfaction and perceptions about a service or facility. The San Francisco International Airport (SFO) survey data from 2014 to 2016 provides an opportunity to explore visitor experiences and identify areas for improvement through systematic data manipulation and analysis using pandas in Python. This paper demonstrates the process of importing, cleaning, merging, analyzing, and serializing multi-year survey data to support ongoing research and decision-making efforts.

Data Importation and Understanding

The initial step involved importing survey data from separate files for each year into pandas DataFrames, using appropriate functions such as pd.read_csv() and pd.read_excel(). These files, stored as CSVs and XLSs within ZIP archives, required extraction prior to reading. Understanding the data dictionaries was critical to interpret variable names and coding schemes, which differ across years. For example, a cleanliness rating coded as '1' in 2014 might correspond to '3' in 2016; thus, a consistent coding scheme needed to be established.

Exploratory data analysis (EDA) was performed on each DataFrame to identify variable names, missing data, and coding schemes. The focus was on rating scale responses related to airport assessments, cleanliness, safety, security, navigation, and transportation. Variables such as Q7ART to Q7ALL, Q9BOARDING to Q9ALL, Q10SAFE, Q12PRECHECKRATE, Q13GETRATE, Q14FIND, and Q14PASSTHRU were key. A comprehensive inventory of these variables across years facilitated identification of overlaps and differences.

Merging Data and Reconciling Variables

To construct a combined dataset, variables with common meaning across years were aligned. When variable names differed (e.g., Q7ART in 2016 vs. other labels in earlier years), they were renamed for consistency. Rating scales were harmonized by recoding responses to a standard scale (e.g., 1-5 with 1=Poor and 5=Excellent). Missing values, indicated by blank cells or special codes, were handled appropriately—either assigned as NaN or imputed if justified.

The merged dataset contained one row per respondent, identified through unique respondent IDs, with columns for each rating variable, respondent residence (Q16LIVE), and survey year. The number of missing values per variable was calculated to assess data quality and completeness. The dataset's size, variable descriptions, and coding schemes were documented to ensure clarity for future analyses.

Analysis of Comments and Ratings

The top three comments from survey years 2015 and 2016 were identified by analyzing the frequency of coded responses Q8COM1 to Q8COM3 and Q8COM4 to Q8COM5, respectively. The comments with highest relative frequency were reported, along with their proportion of total comments in each year. This analysis provided insights into recurrent issues or praise expressed by respondents.

The overall airport rating, specifically the variable Q7ALL, was summarized by respondent residence category (e.g., Bay Area, outside Bay Area). Distributional statistics such as means, medians, and frequency distributions were computed to compare perceptions based on location, which could indicate regional differences in satisfaction or expectations.

Respondent Profiling for Follow-Up Research

A subset of respondents targeted for follow-up participation was identified from select_resps.csv. A new dataset was created including demographic variables—such as age, gender, income, and language—alongside travel behaviors (e.g., purpose of trip, use of parking, baggage, stores, WiFi, previous flights, duration of airport usage, and other airports used). The data from 2015 and 2016 was reconciled for consistency in coding. This profile dataset included respondent ID, survey date and time, residence location, and travel information.

Frequency tables for variables such as parking usage, number of flights in the last 12 months, and duration of airport usage were generated. These summaries assisted in understanding the characteristics of respondents likely to participate in follow-up interviews.

Serialization and Data Preservation

All created datasets were serialized using Python's pickle module, facilitating future access and analysis. Verification involved unpickling the stored objects and confirming data integrity. An optional step involved saving datasets in a shelve database, providing a persistent Python dictionary-like storage. Proper documentation of file paths, object names, and code comments ensured reproducibility and transparency.

Conclusion

This exercise illustrates the importance of meticulous data handling in multi-year survey analysis, including understanding variable coding differences, data merging, recoding, missing data management, commenting, and serialization. Mastery of pandas tools and careful documentation provides a robust foundation for analyzing trends, identifying issues, and supporting research initiatives at SFO or similar complex data environments.

References

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• Wilke, C. O. (2019). Fundamentals of Data Science in R. CRC Press, relevant for understanding data coding and management principles.