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Code-along | 2024-01-23 | Analyzing Survey Data with SQL & Python | Richie Cotton

This code-along analyses data from a survey about the growth of Finnish companies. The data reports the perceptions of top managers on growth, innovativeness, and the ability for renewal.

Where is the data from?

  • Suominen & Pihlajamaa, 2022
  • The dataset

What will I learn today?

  • How to summarize and visualize questions with a numeric response using a histogram.
  • How to determine whether there is a difference between two groups of numeric responses using a Mann-Whitney U test.
  • How to summarize and visualize questions with a categorical response using a bar plot.

Task 0: Setup

For this analysis we need the plotly.express package for drawing histograms and bar plots.

We'll also need the mannwhitneyu function from the scipy.stats package to perform the Mann-Whitney U test.

Instructions

Import the following packages.

  • Import plotly.express using the alias px.
  • From scipy.stats import the mannwhitneyu function.
# Import plotly.express using the alias px
import plotly.express as px 

# From scipy.stats import the mannwhitneyu function
from scipy.stats import mannwhitneyu 

Task 1: Import the Survey Dataset

The survey data is contained in a CSV file named "What_does_it_take_to_generate_new_growth_Survey_data.csv".

Data dictionary

The dataset contains the following columns.

  • Growth_Firm: Is the company (firm) currently classified as a growth company under OECD definitions?
  • question_2_row_1_transformed: The responses to question 2, part 1 (with some pre-applied transformation).
  • question_2_row_2_transformed: The responses to question 2, part 2 (with some pre-applied transformation).
  • question_3_row_1: The responses to question 3, part 1.
  • ...
  • question_7_row_1: The responses to question 7, part 1.

The details of each question are fully described in survey_questions.csv, and we'll cover the details of the specific questions that we look at as we come to them in the tasks here.

Instructions

Use SQL to import the survey data.

  • Select everything from survey_data.csv.
    • This uses European style CSV settings, so you can't use the default CSV reading settings.
    • Set the column delimiter to a semi-colon.
    • Set the decimal separator to a comma.
    • Set the null string to a space.
  • Assign to a DataFrame named survey.
Code hints

  • Workspace lets you import from a CSV file into a SQL query by calling DuckDB's read_csv_auto() in the FROM clause.

  • delim sets the column delimiter.

  • decimal_separator sets the decimal separator.

  • nullstr sets the value used for NULLs (missing values).

Unknown integration
DataFrameavailable as
survey
variable
-- Select everything from survey_data.csv
SELECT * FROM read_csv_auto('survey_data.csv',delim=";", decimal_separator=",",nullstr=" ")
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

The dataset doesn't contain the actual questions that were asked. To find out what the questions are, we can look up the column titles in the data dictionary contained in survey_questions.csv.

Instructions

Use SQL to import the data dictionary for the survey questions.

  • Select everything from survey_questions.csv.
    • This uses the default read CSV settings.
Code hints

  • If you are importing a file from CSV with the default read_csv_auto() settings, then Workspace lets you simply type the file name in the FROM clause.

Unknown integration
DataFrameavailable as
dictionary
variable
-- Select everything from survey_data.csv
SELECT * FROM 'survey_questions.csv'
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.



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