YDataProfiling

Code and Details for ydata-profiling

Published

April 13, 2026

tldr; qwen gets the assist on implementing ydata-profiling

qwen-3.5-35B-A3B follows up from the EDA post on YDataProfiling, a python package for exploratory data analysis.

System Instruction

TipThe System Instruction

You are a capable, thoughtful, and precise assistant. Always prioritize being truthful, nuanced, insightful, and efficient, tailoring your responses specifically to the user’s needs and preferences.

Research before answering the questions: use both reasoning and tool calls to synthesize a proper conclusion. The current month is April and the year is 2026.

library(reticulate)
reticulate::use_virtualenv("MyVenv")

Asking qwen

CautionPrompt

Write up a brief summary set of commands for using YDataProfiling in python. Construct some code to import this csv of data into python and examine it. The data is one cross-section of funds so time series capabilities will be of minimal interest here but should be documented nevertheless a link to the data is provided

Based on my research, here’s a complete summary of YDataProfiling commands and working code for your BondFunds.csv data:

YDataProfiling Command Summary

Installation

pip install ydata-profiling
# or with conda
conda install -c conda-forge ydata-profiling

Basic Usage Commands

Function Description Example
ProfileReport() Creates a profiling report from a DataFrame profile = ProfileReport(df, title="My Report")
.to_file() Exports report to HTML or JSON file profile.to_file("report.html")
.to_notebook_iframe() Displays inline in Jupyter Notebook profile.to_notebook_iframe()
compare() Compares two datasets side-by-side ProfileReport(df1).compare(ProfileReport(df2))

Key Configuration Options

ProfileReport(
    df,                           # pandas DataFrame to profile
    title="Report Title",         # Report heading
    tsmode=False,                 # Enable time-series mode
    sortby=None,                  # Column name for chronological order (tsmode=True)
    samples={"head": 10},         # Data samples shown in report
    correlations={"pearson": True}   # Correlation matrix options
)

Time Series Configuration

# Enable time-series analysis mode
profile = ProfileReport(
    df, 
    tsmode=True,                # Required for time-series profiling
    sortby="date_column_name"   # Column containing chronological data
)

Working Code: BondFunds.csv Analysis

import pandas as pd
from ydata_profiling import ProfileReport

# ============================================
# 1. Load the Data
# ============================================
url = "https://raw.githubusercontent.com/robertwwalker/DADMStuff/master/BondFunds.csv"
df_bondfunds = pd.read_csv(url)

print(f"Dataset shape: {df_bondfunds.shape}")
Dataset shape: (184, 9)
print(f"\nColumn names:\n{list(df_bondfunds.columns)}")

Column names:
['Fund Number', 'Type', 'Assets', 'Fees', 'Expense Ratio', 'Return 2009', '3-Year Return', '5-Year Return', 'Risk']
print(f"\nFirst few rows:")

First few rows:
print(df_bondfunds.head())
  Fund Number                     Type  ...  5-Year Return           Risk
0        FN-1  Intermediate Government  ...            5.5  Below average
1        FN-2  Intermediate Government  ...            6.1  Below average
2        FN-3  Intermediate Government  ...            5.6        Average
3        FN-4  Intermediate Government  ...            5.5        Average
4        FN-5  Intermediate Government  ...            5.4        Average

[5 rows x 9 columns]
# ============================================
# 2. Basic Profile Report (Cross-Section Focus)
# ============================================
profile_basic = ProfileReport(
    df_bondfunds, 
    title="Bond Funds - Cross-Section Analysis"
)

# Generate and save the HTML report
profile_basic.to_file("img/bond_funds_report.html")

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Summarize dataset:   0%|          | 0/14 [00:00<?, ?it/s, Describe variable: Fund Number]
Summarize dataset:   0%|          | 0/14 [00:00<?, ?it/s, Describe variable: Type]       
Summarize dataset:   0%|          | 0/14 [00:00<?, ?it/s, Describe variable: Assets]
Summarize dataset:   7%|7         | 1/14 [00:00<00:00, 49.32it/s, Describe variable: Fees]
Summarize dataset:   7%|7         | 1/14 [00:00<00:00, 43.34it/s, Describe variable: Expense Ratio]
Summarize dataset:  29%|##8       | 4/14 [00:00<00:00, 131.34it/s, Describe variable: Return 2009] 
Summarize dataset:  29%|##8       | 4/14 [00:00<00:00, 127.59it/s, Describe variable: 3-Year Return]
Summarize dataset:  29%|##8       | 4/14 [00:00<00:00, 119.18it/s, Describe variable: 5-Year Return]
Summarize dataset:  29%|##8       | 4/14 [00:00<00:00, 112.12it/s, Describe variable: Risk]         

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Summarize dataset:  69%|######8   | 11/16 [00:00<00:00, 181.32it/s, Calculate auto correlation]
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Summarize dataset:  75%|#######5  | 12/16 [00:00<00:00, 82.85it/s, Get scatter matrix]        
Summarize dataset:  29%|##9       | 12/41 [00:00<00:00, 82.85it/s, scatter Assets, Assets]
Summarize dataset:  32%|###1      | 13/41 [00:00<00:00, 82.85it/s, scatter Expense Ratio, Assets]
Summarize dataset:  34%|###4      | 14/41 [00:00<00:00, 82.85it/s, scatter Return 2009, Assets]  
Summarize dataset:  37%|###6      | 15/41 [00:00<00:00, 82.85it/s, scatter 3-Year Return, Assets]
Summarize dataset:  39%|###9      | 16/41 [00:00<00:00, 82.85it/s, scatter 5-Year Return, Assets]
Summarize dataset:  41%|####1     | 17/41 [00:00<00:00, 82.85it/s, scatter Assets, Expense Ratio]
Summarize dataset:  44%|####3     | 18/41 [00:00<00:00, 82.85it/s, scatter Expense Ratio, Expense Ratio]
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Summarize dataset:  49%|####8     | 20/41 [00:00<00:00, 82.85it/s, scatter 3-Year Return, Expense Ratio]
Summarize dataset:  51%|#####1    | 21/41 [00:00<00:00, 26.71it/s, scatter 3-Year Return, Expense Ratio]
Summarize dataset:  51%|#####1    | 21/41 [00:00<00:00, 26.71it/s, scatter 5-Year Return, Expense Ratio]
Summarize dataset:  54%|#####3    | 22/41 [00:00<00:00, 26.71it/s, scatter Assets, Return 2009]         
Summarize dataset:  56%|#####6    | 23/41 [00:00<00:00, 26.71it/s, scatter Expense Ratio, Return 2009]
Summarize dataset:  59%|#####8    | 24/41 [00:00<00:00, 26.71it/s, scatter Return 2009, Return 2009]  
Summarize dataset:  61%|######    | 25/41 [00:00<00:00, 26.71it/s, scatter 3-Year Return, Return 2009]
Summarize dataset:  63%|######3   | 26/41 [00:00<00:00, 22.72it/s, scatter 3-Year Return, Return 2009]
Summarize dataset:  63%|######3   | 26/41 [00:00<00:00, 22.72it/s, scatter 5-Year Return, Return 2009]
Summarize dataset:  66%|######5   | 27/41 [00:01<00:00, 22.72it/s, scatter Assets, 3-Year Return]     
Summarize dataset:  68%|######8   | 28/41 [00:01<00:00, 22.72it/s, scatter Expense Ratio, 3-Year Return]
Summarize dataset:  71%|#######   | 29/41 [00:01<00:00, 22.72it/s, scatter Return 2009, 3-Year Return]  
Summarize dataset:  73%|#######3  | 30/41 [00:01<00:00, 21.25it/s, scatter Return 2009, 3-Year Return]
Summarize dataset:  73%|#######3  | 30/41 [00:01<00:00, 21.25it/s, scatter 3-Year Return, 3-Year Return]
Summarize dataset:  76%|#######5  | 31/41 [00:01<00:00, 21.25it/s, scatter 5-Year Return, 3-Year Return]
Summarize dataset:  78%|#######8  | 32/41 [00:01<00:00, 21.25it/s, scatter Assets, 5-Year Return]       
Summarize dataset:  80%|########  | 33/41 [00:01<00:00, 20.65it/s, scatter Assets, 5-Year Return]
Summarize dataset:  80%|########  | 33/41 [00:01<00:00, 20.65it/s, scatter Expense Ratio, 5-Year Return]
Summarize dataset:  83%|########2 | 34/41 [00:01<00:00, 20.65it/s, scatter Return 2009, 5-Year Return]  
Summarize dataset:  85%|########5 | 35/41 [00:01<00:00, 20.65it/s, scatter 3-Year Return, 5-Year Return]
Summarize dataset:  88%|########7 | 36/41 [00:01<00:00, 19.69it/s, scatter 3-Year Return, 5-Year Return]
Summarize dataset:  88%|########7 | 36/41 [00:01<00:00, 19.69it/s, scatter 5-Year Return, 5-Year Return]
Summarize dataset:  86%|########6 | 37/43 [00:01<00:00, 19.69it/s, Missing diagram bar]                 
Summarize dataset:  88%|########8 | 38/43 [00:01<00:00, 19.69it/s, Missing diagram matrix]
Summarize dataset:  91%|######### | 39/43 [00:01<00:00, 16.74it/s, Missing diagram matrix]
Summarize dataset:  91%|######### | 39/43 [00:01<00:00, 16.74it/s, Take sample]           
Summarize dataset:  93%|#########3| 40/43 [00:01<00:00, 16.74it/s, Detecting duplicates]
Summarize dataset:  95%|#########5| 41/43 [00:01<00:00, 16.74it/s, Get alerts]          
Summarize dataset:  98%|#########7| 42/43 [00:01<00:00, 16.74it/s, Get reproduction details]
Summarize dataset: 100%|##########| 43/43 [00:01<00:00, 16.74it/s, Completed]               
Summarize dataset: 100%|##########| 43/43 [00:01<00:00, 23.59it/s, Completed]

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# Display in Jupyter Notebook (optional)
# profile_basic.to_notebook_iframe()




# ============================================
# Summary Statistics for BondFunds Dataset
# ============================================
print("\n" + "="*60)

============================================================
print("BOND FUNDS DATASET SUMMARY")
BOND FUNDS DATASET SUMMARY
print("="*60)
============================================================
print(f"\nTotal Funds: {len(df_bondfunds)}")

Total Funds: 184
print(f"Columns Analyzed: {df_bondfunds.shape[1]}")
Columns Analyzed: 9
# Numeric columns summary
numeric_cols = df_bondfunds.select_dtypes(include=['number']).columns.tolist()
for col in numeric_cols[:5]:  # First 5 numeric columns
    print(f"\n{col}:")
    print(f"  Min: {df_bondfunds[col].min():.2f}")
    print(f"  Max: {df_bondfunds[col].max():.2f}")
    print(f"  Mean: {df_bondfunds[col].mean():.2f}")

Assets:
  Min: 12.40
  Max: 18603.50
  Mean: 910.65

Expense Ratio:
  Min: 0.12
  Max: 1.94
  Mean: 0.71

Return 2009:
  Min: -8.80
  Max: 32.00
  Mean: 7.16

3-Year Return:
  Min: -13.80
  Max: 9.40
  Mean: 4.66

5-Year Return:
  Min: -7.30
  Max: 6.80
  Mean: 3.99
# Categorical columns summary
categorical_cols = df_bondfunds.select_dtypes(include=['object']).columns.tolist()
<string>:3: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.
See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.
for col in categorical_cols[:3]:
    print(f"\n{col} - Unique values: {df_bondfunds[col].nunique()}")

Fund Number - Unique values: 184

Type - Unique values: 2

Fees - Unique values: 2

Output Files Generated

File Purpose
bond_funds_report.html Interactive HTML report with visualizations
bond_funds_metrics.json Machine-readable metrics for programmatic access

Key Observations for BondFunds Dataset

Based on the CSV structure, this dataset contains:

  • Fund identifiers (FN-1 through FN-43+)
  • Fund types (Intermediate Government)
  • Assets (in millions USD)
  • Fee information (No/Yes binary)
  • Expense ratios (decimal values)
  • Returns across multiple time periods (2009, 3-Year, 5-Year)
  • Risk ratings (Below/Average/Above average)

The HTML report will provide:

  1. Variable type classification
  2. Missing value analysis
  3. Correlation matrices
  4. Distribution plots for numeric variables
  5. Value counts for categorical variables
  6. Data quality warnings

The full conversation including the thinking steps is here.