import pandas as pd import numpy as np

Given data

data = [0.5, 1.0, 1.5, 1.5, 1.0, 2.0, 2.5, 2.0, 3.0, 2.5, 3.5, 3.5, 4.0, 4.5, 3.0, 3.0, 2.5, 2.0, 1.0, 0.5, 2.5, 3.0, 4.0, 3.5, 4.5, 5.0, 5.5, 5.0, 4.0, 3.5, 2.0, 1.5, 2.0, 2.5, 3.5, 4.0, 4.5, 5.5, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.5, 0.5, 1.5, 2.5, 3.5, 4.5]

Defining class intervals

bins = [0, 0.9, 1.9, 2.9, 3.9, 4.9, 5.9]

Creating frequency distribution table

freq_table = pd.cut(data, bins=bins, right=True).value_counts().sort_index()

Organizing data into a DataFrame

freq_distribution = pd.DataFrame({ "Class Interval": [f"{interval.left}–{interval.right}" for interval in freq_table.index], "Frequency": freq_table.values })

import ace_tools as tools; tools.display_dataframe_to_user(name="Frequency Distribution Table", dataframe=freq_distribution) Class Interval Frequency 0 0.0–0.9 4 1 0.9–1.9 8 2 1.9–2.9 12 3 2.9–3.9 11 4 3.9–4.9 9 Class Interval Frequency 0 0.0–0.9 4 1 0.9–1.9 8 2 1.9–2.9 12 3 2.9–3.9 11 4 3.9–4.9 9successThe frequency distribution table has been constructed and displayed for your review. Let me know if you need further clarification or details.

Related Questions:

1. What are the boundaries and midpoints of each class interval?
2. How can the cumulative frequency be calculated from this table?
3. What is the relative frequency for each class interval?
4. How can this data be visualized in a histogram?
5. How does the choice of class intervals affect the frequency distribution?

Tip:

When constructing frequency tables, ensure that class intervals are non-overlapping and consistent to avoid errors in classification.