word-cloud-superhuman.py

// Setup: pip3 install pandas matplotlib wordcloud 

import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import re


def replace_substring(test_str, s1, s2):
    # Replacing all occurrences of substring s1 with s2
    test_str = re.sub(s1, s2, test_str)
    return test_str

# Helpers


def filter_words(text):
    """Filter words that are noisy, such as 'code' """
    words = ['code', 'feature', 'base', 'change', 'one',
             'make', 'please', 'use', 'Cody', ' s ', 'ability']
    for word in words:
        text = replace_substring(text, word, '')
    return text

# Data processing


df = pd.read_csv("~/data/superhuman-survey/data/survey-export-2023-05-30.csv")
df.columns = ['timestamp', 'email', 'job_title', 'cody_distribution',
              'what_if', 'people_type', 'main_benefit', 'improve']

for i, col in enumerate(['job_title', 'cody_distribution', 'what_if', 'people_type', 'main_benefit', 'improve']):

    mask = (df['what_if'] == 'Very disappointed') | (
        df['what_if'] == 'Somewhat disappointed')
    # mask = (df['what_if'] == 'Not disappointed')

    text = " ".join(filter_words(feedback) for feedback in df[mask][col])

    word_cloud = WordCloud(
        collocations=False, background_color='white').generate(text)
    plt.subplot(3, 2, i+1)
    plt.title(col)
    plt.imshow(word_cloud, interpolation='bilinear')

plt.show()