A histogram shows how often numeric values fall within contiguous intervals, or bins. It turns a column of numbers into a shape you can inspect for concentration, spread, gaps, and unusual values. section 2.6.1
Use one when a practical question depends on a distribution: Are review scores balanced? Do customers usually receive answers within hours, or do some wait months? The shape can expose data-entry errors, bias, or a pattern that affects later statistical tests and models. section 2.6.1 section 2.6.3
Review response times
Calculate the difference between review-answer and review-creation timestamps, then plot that numeric column as a histogram. Start with seconds if needed, but inspect the summary and convert to hours or days when seconds obscure the business meaning. A long spread, large outliers, or distinct clusters deserves investigation against the raw records. section 2.6.3
A useful histogram workflow is:
- Choose a numeric measure and a question it can answer. section 2.6.1
- Plot the values, inspect the bin pattern, and compare it with an expected distribution when appropriate. section 2.6.1
- Check units, summary statistics, and suspicious cases rather than trusting the visual alone; pair the histogram with a box plot when useful. section 2.6.3
import matplotlib.pyplot as plt
# Histogram
df_order_reviews['time_diff'].plot(kind='hist', bins=50)
plt.xlabel('Time Difference (Seconds)')
plt.ylabel('Frequency')
plt.title('Distribution of Time Difference Between Review Creation and
Answer')
plt.show()
# Box plot
df_order_reviews['time_diff'].plot(kind='box', vert=False)
plt.xlabel('Time Difference (Seconds)')
plt.title('Box Plot of Time Difference Between Review Creation and
Answer')
plt.show()
A Python example combines a histogram with a box plot for the same time-difference column, making distribution and extreme values easier to inspect together. section 2.6.3
