Day in the Life of a Data Analyst - SurveyMonkey Data Transformation

In this introductory paragraph, the speaker explains the purpose of the video, which is to teach viewers how to manipulate data using Python and Pandas. The goal is to transform a SurveyMonkey dataset from a wide format to a long format, making it easier to visualize in tools like Power BI and Tableau. The speaker emphasizes the importance of data manipulation skills for data analysts and data scientists.

The speaker shares a personal anecdote about a client needing data visualization in Tableau, highlighting the frequent need for data transformation in the field. This segment underscores the practical importance of data manipulation skills and suggests that these skills are marketable on freelance platforms. The speaker cleans a dataset by removing identifying information and begins explaining the steps to understand and prepare the data.

The speaker details the process of identifying and retaining important demographic information in the dataset, such as division, position level, gender, tenure, and employment type. This part focuses on maintaining the integrity of demographic columns while transforming the rest of the data. The speaker also discusses the necessity of transforming question columns from a wide to a long format for better analysis.

In this section, the speaker demonstrates how to transpose and concatenate header rows in Excel. They emphasize the importance of preparing data in Excel before importing it into Python, showing practical steps to create a new sheet, transpose data, and rename columns to ensure clarity and usability. The speaker highlights the advantages of using underscores instead of spaces in column names.

The speaker outlines the process of saving the prepared Excel data and explains the importance of creating backups. They emphasize the importance of organized data management and demonstrate the steps to ensure that the raw data remains untouched. This part also includes tips on best practices for data preparation and setting up the environment for Python analysis.

The speaker transitions to Python, demonstrating how to set up a Python environment and import necessary libraries like Pandas and OS. They explain the importance of creating a clean workspace and using IPython notebooks for step-by-step code execution. The section covers importing Excel data into Pandas, copying dataframes, and initial data exploration using Pandas methods.

This part focuses on cleaning the dataset by dropping unnecessary columns such as start date, end date, and email addresses. The speaker explains how to use Pandas' drop method and the importance of reducing data size early in the process to improve efficiency. They demonstrate practical coding techniques for handling columns and lists in Python.

The speaker demonstrates how to use the Pandas melt method to unpivot the dataset, transforming it from wide to long format. They explain the concept of identifier variables and value variables, and how to apply these in the melt function. The section also covers practical tips for handling large numbers of columns efficiently in Python.

In this section, the speaker explains how to rename columns in the dataset for better clarity and usability. They demonstrate how to create a new dataframe by copying and modifying existing data. The speaker also introduces the concept of merging dataframes, showing how to combine data based on specific columns using Pandas' merge method.

The speaker delves deeper into merging dataframes, explaining different types of joins (left, right, inner) and their uses. They provide a detailed explanation of how to ensure data integrity during merges and how to handle potential issues such as duplicating columns. The section includes practical examples of merging dataframes using Pandas methods.

This part covers the process of grouping and aggregating data to calculate the number of respondents for each question. The speaker demonstrates how to use Pandas' groupby method and aggregate functions like count and nunique. They explain the importance of resetting the index after grouping to maintain a clean dataframe structure.

The speaker explains how to filter and clean the aggregated data, ensuring that only relevant rows are included in the final dataset. They demonstrate how to handle null values (NaNs) and use the dropna method to remove unwanted rows. This section emphasizes the importance of data cleaning for accurate analysis.

In this section, the speaker shows how to add new columns to the dataset based on aggregated data, such as the number of respondents per question. They explain how to use Pandas' rename method to avoid column name conflicts during merges. The speaker provides practical examples of renaming and merging dataframes to enhance the dataset.

The speaker demonstrates the final steps of the data transformation process, including the last merge to add aggregated data columns to the main dataframe. They explain how to fill null values with zero using the fillna method and ensure that the final dataset meets the client's requirements. This part highlights the importance of verifying the integrity and accuracy of the transformed data.

The speaker explains how to export the final transformed dataset to an Excel file using Pandas' to_excel method. They provide tips on handling file paths and ensuring that the exported file does not include unwanted index columns. The section concludes with a review of the entire transformation process and the importance of clear, organized code for data analysis.

In the concluding paragraph, the speaker summarizes the key points covered in the video, including data import, cleaning, transformation, merging, and aggregation. They emphasize the real-world applicability of these skills and encourage viewers to practice and apply them in their own projects. The speaker also invites feedback and suggestions from viewers to improve future tutorials.