PubChem View tutorial¶

This package, as far as I know, is the only one that implements the PubChem View for downloading, i.e. experimental data for different compounds from PubChem. The available parameters are available here.

In [1]:

from provesid import (
    PubChemView, 
    PropertyData, 
    PubChemViewError, 
    PubChemViewNotFoundError,
    get_experimental_property,
    get_all_experimental_properties,
    get_property_values_only,
    get_property_table
)

from provesid import ( PubChemView, PropertyData, PubChemViewError, PubChemViewNotFoundError, get_experimental_property, get_all_experimental_properties, get_property_values_only, get_property_table )

In [2]:

# Initialize PubChemView with default settings
pugview = PubChemView()

# Let's start with a simple example - getting experimental data for aspirin (CID: 2244)
aspirin_cid = 2244
print(f"Working with Aspirin (CID: {aspirin_cid})")

# Initialize PubChemView with default settings pugview = PubChemView() # Let's start with a simple example - getting experimental data for aspirin (CID: 2244) aspirin_cid = 2244 print(f"Working with Aspirin (CID: {aspirin_cid})")

Working with Aspirin (CID: 2244)

What is PubChemView?¶

PubChemView provides access to experimental and computed properties for chemical compounds from PubChem. Unlike the regular PubChem API that gives you basic compound information, PubChemView specializes in:

• Experimental Properties: Real laboratory measurements (melting points, boiling points, density, etc.)
• Physical Properties: Computed or literature values
• Biological Activities: Toxicity data, bioactivity information
• Environmental Data: Fate and transport properties

This makes it invaluable for toxicology, environmental science, and chemical safety assessments.

Basic Property Retrieval¶

Let's start by getting some basic experimental properties for aspirin.

In [3]:

# Get melting point data for aspirin
melting_point_data = pugview.extract_property_data(aspirin_cid, "Melting Point")

if melting_point_data:
    print("Melting Point Data for Aspirin:")
    for entry in melting_point_data[:3]:  # Show first 3 entries
        print(f"  Value: {entry.value}")
        print(f"  Unit: {entry.unit}")
        print(f"  Reference: {entry.reference}")
        print("  ---")
else:
    print("No melting point data found")

# Get melting point data for aspirin melting_point_data = pugview.extract_property_data(aspirin_cid, "Melting Point") if melting_point_data: print("Melting Point Data for Aspirin:") for entry in melting_point_data[:3]: # Show first 3 entries print(f" Value: {entry.value}") print(f" Unit: {entry.unit}") print(f" Reference: {entry.reference}") print(" ---") else: print("No melting point data found")

Melting Point Data for Aspirin:
  Value: 275 °F (NTP, 1992)
  Unit: None
  Reference: National Toxicology Program, Institute of Environmental Health Sciences, National Institutes of Health (NTP). 1992. National Toxicology Program Chemical Repository Database. Research Triangle Park, North Carolina.
  ---
  Value: 138-140
  Unit: None
  Reference: http://www.rsc.org/learn-chemistry/content/filerepository/CMP/00/000/045/Aspirin.pdf
  ---
  Value: 135 °C (rapid heating)
  Unit: °C
  Reference: O'Neil, M.J. (ed.). The Merck Index - An Encyclopedia of Chemicals, Drugs, and Biologicals. Whitehouse Station, NJ:  Merck and Co., Inc., 2006., p. 140
  ---

In [4]:

# Get multiple properties at once
properties_of_interest = ["Boiling Point", "Density", "Solubility"]

print("Multiple Properties for Aspirin:")
print("=" * 40)

for prop_name in properties_of_interest:
    try:
        data = pugview.extract_property_data(aspirin_cid, prop_name)
        if data:
            print(f"\n{prop_name}:")
            # Show first entry for each property
            first_entry = data[0]
            print(f"  Value: {first_entry.value}")
            print(f"  Unit: {first_entry.unit}")
            print(f"  Source: {first_entry.source}")
        else:
            print(f"\n{prop_name}: No data available")
    except Exception as e:
        print(f"\n{prop_name}: Error - {e}")

# Get multiple properties at once properties_of_interest = ["Boiling Point", "Density", "Solubility"] print("Multiple Properties for Aspirin:") print("=" * 40) for prop_name in properties_of_interest: try: data = pugview.extract_property_data(aspirin_cid, prop_name) if data: print(f"\n{prop_name}:") # Show first entry for each property first_entry = data[0] print(f" Value: {first_entry.value}") print(f" Unit: {first_entry.unit}") print(f" Source: {first_entry.source}") else: print(f"\n{prop_name}: No data available") except Exception as e: print(f"\n{prop_name}: Error - {e}")

Multiple Properties for Aspirin:
========================================

Boiling Point:
  Value: 284 °F at 760 mmHg (decomposes) (NTP, 1992)
  Unit: mmHg

Boiling Point: Error - 'PropertyData' object has no attribute 'source'

Density:
  Value: 1.4 (NTP, 1992) - Denser than water; will sink
  Unit: None

Density: Error - 'PropertyData' object has no attribute 'source'

Solubility:
  Value: less than 1 mg/mL at 73 °F (NTP, 1992)
  Unit: None

Solubility: Error - 'PropertyData' object has no attribute 'source'

Using Convenience Functions¶

The package provides several convenience functions that make common tasks easier.

In [5]:

# Example 1: Get just the values without metadata
print("=== Quick Property Values ===")
mp_values = get_property_values_only(aspirin_cid, "Melting Point")
print(f"Melting Point values: {mp_values[:3]}")  # First 3 values

# Example 2: Get all experimental properties at once
print("\n=== All Available Properties ===")
all_props = get_all_experimental_properties(aspirin_cid)
print(f"Available properties: {list(all_props.keys())[:10]}")  # First 10 properties

# Example 3: Get a property table (structured format)
print("\n=== Property Table ===")
prop_table = get_property_table(aspirin_cid, "Density")
if not prop_table.empty:
    print(f"Density table has {len(prop_table)} entries")

# Example 1: Get just the values without metadata print("=== Quick Property Values ===") mp_values = get_property_values_only(aspirin_cid, "Melting Point") print(f"Melting Point values: {mp_values[:3]}") # First 3 values # Example 2: Get all experimental properties at once print("\n=== All Available Properties ===") all_props = get_all_experimental_properties(aspirin_cid) print(f"Available properties: {list(all_props.keys())[:10]}") # First 10 properties # Example 3: Get a property table (structured format) print("\n=== Property Table ===") prop_table = get_property_table(aspirin_cid, "Density") if not prop_table.empty: print(f"Density table has {len(prop_table)} entries")

=== Quick Property Values ===
Melting Point values: ['275 °F (NTP, 1992)', '138-140', '135 °C (rapid heating)']

=== All Available Properties ===
Available properties: ['Physical Description', 'Color/Form', 'Odor', 'Boiling Point', 'Melting Point', 'Flash Point', 'Solubility', 'Density', 'Vapor Pressure', 'LogP']

=== Property Table ===
Density table has 5 entries

Working with Different Compounds¶

Let's explore data for various common chemicals to see the diversity of available information.

In [6]:

# Define some common compounds
compounds = {
    "Water": 962,
    "Ethanol": 702,
    "Caffeine": 2519,
    "Benzene": 241,
    "Acetone": 180
}

# Compare melting points across compounds
print("Melting Point Comparison:")
print("=" * 50)

melting_data = {}
for name, cid in compounds.items():
    try:
        mp_values = get_property_values_only(cid, "Melting Point")
        if mp_values:
            # Get the first numeric value
            first_value = mp_values[0] if mp_values else "No data"
            melting_data[name] = first_value
            print(f"{name:12}: {first_value}")
        else:
            print(f"{name:12}: No data available")
    except Exception as e:
        print(f"{name:12}: Error - {str(e)[:50]}...")

# Define some common compounds compounds = { "Water": 962, "Ethanol": 702, "Caffeine": 2519, "Benzene": 241, "Acetone": 180 } # Compare melting points across compounds print("Melting Point Comparison:") print("=" * 50) melting_data = {} for name, cid in compounds.items(): try: mp_values = get_property_values_only(cid, "Melting Point") if mp_values: # Get the first numeric value first_value = mp_values[0] if mp_values else "No data" melting_data[name] = first_value print(f"{name:12}: {first_value}") else: print(f"{name:12}: No data available") except Exception as e: print(f"{name:12}: Error - {str(e)[:50]}...")

Melting Point Comparison:
==================================================
Water       : 32 °F
Ethanol     : -173.4 °F (NTP, 1992)
Caffeine    : 460 °F (NTP, 1992)
Benzene     : 41.9 °F (NTP, 1992)
Acetone     : -137 °F (NTP, 1992)

Advanced Features¶

Property Discovery and Batch Processing¶

The PubChemView API offers advanced features for discovering available properties and processing multiple compounds efficiently.

In [7]:

# Discover available properties for a compound
print("=== Available Properties for Caffeine ===")
available_props = pugview.get_available_properties(2519)  # Caffeine
if available_props:
    print(f"Found {len(available_props)} properties:")
    for i, prop in enumerate(available_props[:10], 1):  # Show first 10
        print(f"  {i:2d}. {prop}")
    if len(available_props) > 10:
        print(f"     ... and {len(available_props) - 10} more")
else:
    print("No properties found")

# Get property summary for better overview
print(f"\n=== Property Summary for Caffeine ===")
try:
    summary = pugview.get_property_summary(2519, "Solubility")
    if summary:
        print(f"Property: {summary.get('property', 'Unknown')}")
        print(f"Total entries: {summary.get('count', 0)}")
        print(f"Unique values: {len(summary.get('unique_values', []))}")
        print(f"Sources: {summary.get('sources', [])[:3]}")  # First 3 sources
except Exception as e:
    print(f"Error getting summary: {e}")

# Discover available properties for a compound print("=== Available Properties for Caffeine ===") available_props = pugview.get_available_properties(2519) # Caffeine if available_props: print(f"Found {len(available_props)} properties:") for i, prop in enumerate(available_props[:10], 1): # Show first 10 print(f" {i:2d}. {prop}") if len(available_props) > 10: print(f" ... and {len(available_props) - 10} more") else: print("No properties found") # Get property summary for better overview print(f"\n=== Property Summary for Caffeine ===") try: summary = pugview.get_property_summary(2519, "Solubility") if summary: print(f"Property: {summary.get('property', 'Unknown')}") print(f"Total entries: {summary.get('count', 0)}") print(f"Unique values: {len(summary.get('unique_values', []))}") print(f"Sources: {summary.get('sources', [])[:3]}") # First 3 sources except Exception as e: print(f"Error getting summary: {e}")

=== Available Properties for Caffeine ===

Found 20 properties:
   1. Physical Description
   2. Color/Form
   3. Odor
   4. Taste
   5. Boiling Point
   6. Melting Point
   7. Solubility
   8. Density
   9. Vapor Pressure
  10. LogP
     ... and 10 more

=== Property Summary for Caffeine ===

Property: Solubility
Total entries: 9
Unique values: 0
Sources: []

Data Analysis and Visualization¶

Let's analyze the data we've collected and create some visualizations.

In [8]:

import pandas as pd
try:
    import matplotlib.pyplot as plt
except Exception:
    plt = None

# Create a comprehensive property comparison
property_data = []

for compound_name, cid in compounds.items():
    try:
        # Get multiple properties for analysis
        properties_to_check = ["Melting Point", "Boiling Point", "Density"]
        
        compound_props = {"Compound": compound_name, "CID": cid}
        
        for prop in properties_to_check:
            try:
                values = get_property_values_only(cid, prop)
                if values:
                    # Try to extract numeric value
                    first_val = str(values[0])
                    # Simple extraction of first number
                    import re
                    numbers = re.findall(r'-?\d+\.?\d*', first_val)
                    if numbers:
                        compound_props[prop] = float(numbers[0])
                    else:
                        compound_props[prop] = None
                else:
                    compound_props[prop] = None
            except:
                compound_props[prop] = None
        
        property_data.append(compound_props)
        
    except Exception as e:
        print(f"Error processing {compound_name}: {e}")

# Create DataFrame for analysis
df = pd.DataFrame(property_data)
print("Property Data Summary:")
print(df.to_string(index=False))

import pandas as pd try: import matplotlib.pyplot as plt except Exception: plt = None # Create a comprehensive property comparison property_data = [] for compound_name, cid in compounds.items(): try: # Get multiple properties for analysis properties_to_check = ["Melting Point", "Boiling Point", "Density"] compound_props = {"Compound": compound_name, "CID": cid} for prop in properties_to_check: try: values = get_property_values_only(cid, prop) if values: # Try to extract numeric value first_val = str(values[0]) # Simple extraction of first number import re numbers = re.findall(r'-?\d+\.?\d*', first_val) if numbers: compound_props[prop] = float(numbers[0]) else: compound_props[prop] = None else: compound_props[prop] = None except: compound_props[prop] = None property_data.append(compound_props) except Exception as e: print(f"Error processing {compound_name}: {e}") # Create DataFrame for analysis df = pd.DataFrame(property_data) print("Property Data Summary:") print(df.to_string(index=False))

Property Data Summary:
Compound  CID  Melting Point  Boiling Point  Density
   Water  962           32.0          212.0    1.000
 Ethanol  702         -173.4          173.3    0.790
Caffeine 2519          460.0          352.0    1.230
 Benzene  241           41.9          176.2    0.879
 Acetone  180         -137.0          133.0    0.791

In [9]:

# Create visualizations
if plt is None:
    print("matplotlib is not installed; skipping plotting section.")
else:
    fig, axes = plt.subplots(1, 2, figsize=(15, 5))

# Plot 1: Melting Points
    mp_data = df[df['Melting Point'].notna()]
    if not mp_data.empty:
        axes[0].bar(mp_data['Compound'], mp_data['Melting Point'])
        axes[0].set_title('Melting Points of Compounds')
        axes[0].set_ylabel('Temperature (°C)')
        axes[0].tick_params(axis='x', rotation=45)
    else:
        axes[0].text(0.5, 0.5, 'No melting point data available', 
                    ha='center', va='center', transform=axes[0].transAxes)
        axes[0].set_title('Melting Points (No Data)')

# Plot 2: Density vs Melting Point correlation
    valid_data = df.dropna(subset=['Density', 'Melting Point'])
    if len(valid_data) > 1:
        axes[1].scatter(valid_data['Density'], valid_data['Melting Point'])
        for i, row in valid_data.iterrows():
            axes[1].annotate(row['Compound'], 
                            (row['Density'], row['Melting Point']),
                            xytext=(5, 5), textcoords='offset points')
        axes[1].set_xlabel('Density (g/cm³)')
        axes[1].set_ylabel('Melting Point (°C)')
        axes[1].set_title('Density vs Melting Point')
    else:
        axes[1].text(0.5, 0.5, 'Insufficient data for correlation', 
                    ha='center', va='center', transform=axes[1].transAxes)
        axes[1].set_title('Density vs Melting Point (Insufficient Data)')

    plt.tight_layout()
    plt.show()

# Create visualizations if plt is None: print("matplotlib is not installed; skipping plotting section.") else: fig, axes = plt.subplots(1, 2, figsize=(15, 5)) # Plot 1: Melting Points mp_data = df[df['Melting Point'].notna()] if not mp_data.empty: axes[0].bar(mp_data['Compound'], mp_data['Melting Point']) axes[0].set_title('Melting Points of Compounds') axes[0].set_ylabel('Temperature (°C)') axes[0].tick_params(axis='x', rotation=45) else: axes[0].text(0.5, 0.5, 'No melting point data available', ha='center', va='center', transform=axes[0].transAxes) axes[0].set_title('Melting Points (No Data)') # Plot 2: Density vs Melting Point correlation valid_data = df.dropna(subset=['Density', 'Melting Point']) if len(valid_data) > 1: axes[1].scatter(valid_data['Density'], valid_data['Melting Point']) for i, row in valid_data.iterrows(): axes[1].annotate(row['Compound'], (row['Density'], row['Melting Point']), xytext=(5, 5), textcoords='offset points') axes[1].set_xlabel('Density (g/cm³)') axes[1].set_ylabel('Melting Point (°C)') axes[1].set_title('Density vs Melting Point') else: axes[1].text(0.5, 0.5, 'Insufficient data for correlation', ha='center', va='center', transform=axes[1].transAxes) axes[1].set_title('Density vs Melting Point (Insufficient Data)') plt.tight_layout() plt.show()

matplotlib is not installed; skipping plotting section.

Error Handling and Best Practices¶

When working with experimental data, it's important to handle missing data and errors gracefully.

In [10]:

# Test with various scenarios to demonstrate error handling
test_cases = [
    (2244, "Melting Point"),          # Should work
    (2244, "NonExistentProperty"),    # Should fail gracefully
    (999999999, "Melting Point"),     # Invalid CID
]

print("Error Handling Examples:")
print("=" * 50)

if "get_property_safely" not in globals():
    def get_property_safely(cid, property_name, max_values=5):
        result = {
            'cid': cid,
            'property': property_name,
            'success': False,
            'data': [],
            'error': None,
            'count': 0,
        }
        try:
            data = get_experimental_property(cid, property_name)
            if data:
                result['success'] = True
                result['data'] = data[:max_values]
                result['count'] = len(data)
            else:
                result['error'] = "No data available"
        except Exception as e:
            result['error'] = str(e)
        return result

for cid, prop_name in test_cases:
    result = get_property_safely(cid, prop_name)
    
    print(f"\nCID {cid}, Property: {prop_name}")
    print(f"Success: {result['success']}")
    
    if result['success']:
        print(f"Found {result['count']} entries")
        if result['data']:
            first_entry = result['data'][0]
            print(f"First value: {first_entry.value} {first_entry.unit}")
    else:
        print(f"Error: {result['error']}")

# Test with various scenarios to demonstrate error handling test_cases = [ (2244, "Melting Point"), # Should work (2244, "NonExistentProperty"), # Should fail gracefully (999999999, "Melting Point"), # Invalid CID ] print("Error Handling Examples:") print("=" * 50) if "get_property_safely" not in globals(): def get_property_safely(cid, property_name, max_values=5): result = { 'cid': cid, 'property': property_name, 'success': False, 'data': [], 'error': None, 'count': 0, } try: data = get_experimental_property(cid, property_name) if data: result['success'] = True result['data'] = data[:max_values] result['count'] = len(data) else: result['error'] = "No data available" except Exception as e: result['error'] = str(e) return result for cid, prop_name in test_cases: result = get_property_safely(cid, prop_name) print(f"\nCID {cid}, Property: {prop_name}") print(f"Success: {result['success']}") if result['success']: print(f"Found {result['count']} entries") if result['data']: first_entry = result['data'][0] print(f"First value: {first_entry.value} {first_entry.unit}") else: print(f"Error: {result['error']}")

Error Handling Examples:
==================================================

CID 2244, Property: Melting Point
Success: True
Found 7 entries
First value: 275 °F (NTP, 1992) None

CID 2244, Property: NonExistentProperty
Success: False
Error: No data available

CID 999999999, Property: Melting Point
Success: False
Error: No data available

Practical Applications¶

Building a Chemical Property Database¶

Let's create a practical example of building a small chemical property database for risk assessment.

In [11]:

# Robust property retrieval function
def get_property_safely(cid, property_name, max_values=5):
    """
    Safely retrieve property data with comprehensive error handling.
    
    Args:
        cid: Compound ID
        property_name: Name of the property to retrieve
        max_values: Maximum number of values to return
    
    Returns:
        dict: Contains status, data, and any error messages
    """
    result = {
        'cid': cid,
        'property': property_name,
        'success': False,
        'data': [],
        'error': None,
        'count': 0
    }
    
    try:
        # Try to get the property data
        data = get_experimental_property(cid, property_name)
        
        if data:
            result['success'] = True
            result['data'] = data[:max_values]  # Limit number of entries
            result['count'] = len(data)
        else:
            result['error'] = "No data available"
            
    except PubChemViewNotFoundError:
        result['error'] = "Property not found"
    except PubChemViewError as e:
        result['error'] = f"PubChemView error: {str(e)}"
    except Exception as e:
        result['error'] = f"Unexpected error: {str(e)}"
    
    return result

print("Utility function loaded: get_property_safely()")

# Robust property retrieval function def get_property_safely(cid, property_name, max_values=5): """ Safely retrieve property data with comprehensive error handling. Args: cid: Compound ID property_name: Name of the property to retrieve max_values: Maximum number of values to return Returns: dict: Contains status, data, and any error messages """ result = { 'cid': cid, 'property': property_name, 'success': False, 'data': [], 'error': None, 'count': 0 } try: # Try to get the property data data = get_experimental_property(cid, property_name) if data: result['success'] = True result['data'] = data[:max_values] # Limit number of entries result['count'] = len(data) else: result['error'] = "No data available" except PubChemViewNotFoundError: result['error'] = "Property not found" except PubChemViewError as e: result['error'] = f"PubChemView error: {str(e)}" except Exception as e: result['error'] = f"Unexpected error: {str(e)}" return result print("Utility function loaded: get_property_safely()")

Utility function loaded: get_property_safely()

In [12]:

# Create a chemical safety database
safety_relevant_properties = [
    "Flash Point",
    "Auto-Ignition Temperature", 
    "Vapor Pressure",
    "LC50",
    "LD50"
]

# Industrial solvents for safety assessment
industrial_chemicals = {
    "Acetone": 180,
    "Ethanol": 702,
    "Benzene": 241,
    "Toluene": 1140,
    "Methanol": 887
}

print("Chemical Safety Database")
print("=" * 60)

safety_database = []

for chemical_name, cid in industrial_chemicals.items():
    chemical_data = {
        'name': chemical_name,
        'cid': cid,
        'properties': {}
    }
    
    print(f"\nProcessing {chemical_name} (CID: {cid})...")
    
    for prop in safety_relevant_properties:
        result = get_property_safely(cid, prop, max_values=1)
        
        if result['success'] and result['data']:
            value_info = result['data'][0]
            chemical_data['properties'][prop] = {
                'value': value_info.value,
                'unit': value_info.unit,
                'reference': value_info.reference
            }
            print(f"  ✓ {prop}: {value_info.value} {value_info.unit}")
        else:
            chemical_data['properties'][prop] = None
            print(f"  ✗ {prop}: {result['error']}")
    
    safety_database.append(chemical_data)

print(f"\n\nDatabase created with {len(safety_database)} chemicals")
print(f"Properties tracked: {', '.join(safety_relevant_properties)}")

# Create a chemical safety database safety_relevant_properties = [ "Flash Point", "Auto-Ignition Temperature", "Vapor Pressure", "LC50", "LD50" ] # Industrial solvents for safety assessment industrial_chemicals = { "Acetone": 180, "Ethanol": 702, "Benzene": 241, "Toluene": 1140, "Methanol": 887 } print("Chemical Safety Database") print("=" * 60) safety_database = [] for chemical_name, cid in industrial_chemicals.items(): chemical_data = { 'name': chemical_name, 'cid': cid, 'properties': {} } print(f"\nProcessing {chemical_name} (CID: {cid})...") for prop in safety_relevant_properties: result = get_property_safely(cid, prop, max_values=1) if result['success'] and result['data']: value_info = result['data'][0] chemical_data['properties'][prop] = { 'value': value_info.value, 'unit': value_info.unit, 'reference': value_info.reference } print(f" ✓ {prop}: {value_info.value} {value_info.unit}") else: chemical_data['properties'][prop] = None print(f" ✗ {prop}: {result['error']}") safety_database.append(chemical_data) print(f"\n\nDatabase created with {len(safety_database)} chemicals") print(f"Properties tracked: {', '.join(safety_relevant_properties)}")

Chemical Safety Database
============================================================

Processing Acetone (CID: 180)...
  ✓ Flash Point: 0 °F (NTP, 1992) None
  ✗ Auto-Ignition Temperature: No data available
  ✓ Vapor Pressure: 180 mmHg at 68 °F ; 270 mmHg at 86 °F (NTP, 1992) mmHg
  ✗ LC50: No data available
  ✗ LD50: No data available

Processing Ethanol (CID: 702)...
  ✓ Flash Point: 55 °F (NTP, 1992) None
  ✗ Auto-Ignition Temperature: No data available
  ✓ Vapor Pressure: 40 mmHg at 66 °F ; 50 mmHg at 77 °F (NTP, 1992) mmHg
  ✗ LC50: No data available
  ✗ LD50: No data available

Processing Benzene (CID: 241)...
  ✓ Flash Point: 12 °F (NTP, 1992) None
  ✗ Auto-Ignition Temperature: No data available
  ✓ Vapor Pressure: 60 mmHg at 59 °F ; 76 mmHg at 68 °F (NTP, 1992) mmHg
  ✗ LC50: No data available
  ✗ LD50: No data available

Processing Toluene (CID: 1140)...
  ✓ Flash Point: 40 °F (NTP, 1992) None
  ✗ Auto-Ignition Temperature: No data available
  ✓ Vapor Pressure: 10 mmHg at 43.5 °F ; 20 mmHg at 65.1 °F; 40 mmHg at 89.2 °F (NTP, 1992) mmHg
  ✗ LC50: No data available
  ✗ LD50: No data available

Processing Methanol (CID: 887)...
  ✓ Flash Point: 52 °F (NTP, 1992) None
  ✗ Auto-Ignition Temperature: No data available
  ✓ Vapor Pressure: 100 mmHg at 70.2 °F ; 237.87 mmHg at 100 °F (NTP, 1992) mmHg
  ✗ LC50: No data available
  ✗ LD50: No data available


Database created with 5 chemicals
Properties tracked: Flash Point, Auto-Ignition Temperature, Vapor Pressure, LC50, LD50

In [13]:

# Export the database to CSV for further analysis
import json

# Create a flattened version for CSV export
export_data = []
for chemical in safety_database:
    row = {
        'Chemical': chemical['name'],
        'CID': chemical['cid']
    }
    
    # Add property columns
    for prop in safety_relevant_properties:
        if chemical['properties'][prop]:
            row[f'{prop}_Value'] = chemical['properties'][prop]['value']
            row[f'{prop}_Unit'] = chemical['properties'][prop]['unit']
            row[f'{prop}_Reference'] = chemical['properties'][prop]['reference']
        else:
            row[f'{prop}_Value'] = None
            row[f'{prop}_Unit'] = None
            row[f'{prop}_Reference'] = None
    
    export_data.append(row)

# Convert to DataFrame and display summary
export_df = pd.DataFrame(export_data)
print("Database Summary:")
print("-" * 40)
print(f"Total chemicals: {len(export_df)}")
print(f"Total columns: {len(export_df.columns)}")

# Show data availability summary
for prop in safety_relevant_properties:
    available = export_df[f'{prop}_Value'].notna().sum()
    print(f"{prop}: {available}/{len(export_df)} compounds have data")

# Display the first few rows
print(f"\nFirst few rows of the database:")
print(export_df[['Chemical', 'CID'] + [f'{p}_Value' for p in safety_relevant_properties[:3]]].head())

# Export the database to CSV for further analysis import json # Create a flattened version for CSV export export_data = [] for chemical in safety_database: row = { 'Chemical': chemical['name'], 'CID': chemical['cid'] } # Add property columns for prop in safety_relevant_properties: if chemical['properties'][prop]: row[f'{prop}_Value'] = chemical['properties'][prop]['value'] row[f'{prop}_Unit'] = chemical['properties'][prop]['unit'] row[f'{prop}_Reference'] = chemical['properties'][prop]['reference'] else: row[f'{prop}_Value'] = None row[f'{prop}_Unit'] = None row[f'{prop}_Reference'] = None export_data.append(row) # Convert to DataFrame and display summary export_df = pd.DataFrame(export_data) print("Database Summary:") print("-" * 40) print(f"Total chemicals: {len(export_df)}") print(f"Total columns: {len(export_df.columns)}") # Show data availability summary for prop in safety_relevant_properties: available = export_df[f'{prop}_Value'].notna().sum() print(f"{prop}: {available}/{len(export_df)} compounds have data") # Display the first few rows print(f"\nFirst few rows of the database:") print(export_df[['Chemical', 'CID'] + [f'{p}_Value' for p in safety_relevant_properties[:3]]].head())

Database Summary:
----------------------------------------
Total chemicals: 5
Total columns: 17
Flash Point: 5/5 compounds have data
Auto-Ignition Temperature: 0/5 compounds have data
Vapor Pressure: 5/5 compounds have data
LC50: 0/5 compounds have data
LD50: 0/5 compounds have data

First few rows of the database:
   Chemical   CID  Flash Point_Value Auto-Ignition Temperature_Value  \
0   Acetone   180   0 °F (NTP, 1992)                            None   
1   Ethanol   702  55 °F (NTP, 1992)                            None   
2   Benzene   241  12 °F (NTP, 1992)                            None   
3   Toluene  1140  40 °F (NTP, 1992)                            None   
4  Methanol   887  52 °F (NTP, 1992)                            None   

                                Vapor Pressure_Value  
0  180 mmHg at 68 °F ; 270 mmHg at 86 °F (NTP, 1992)  
1    40 mmHg at 66 °F ; 50 mmHg at 77 °F (NTP, 1992)  
2    60 mmHg at 59 °F ; 76 mmHg at 68 °F (NTP, 1992)  
3  10 mmHg at 43.5 °F ; 20 mmHg at 65.1 °F; 40 mm...  
4  100 mmHg at 70.2 °F ; 237.87 mmHg at 100 °F (N...  

Conclusion and Next Steps¶

What You've Learned¶

In this tutorial, you've learned how to:

1. Initialize and use PubChemView to access experimental chemical data
2. Retrieve specific properties like melting points, boiling points, and safety data
3. Use convenience functions for streamlined data access
4. Handle errors gracefully when data is missing or unavailable
5. Build practical databases for chemical safety and risk assessment
6. Analyze and visualize chemical property data
7. Export data for further analysis in other tools

Key Features of PubChemView¶

• Comprehensive Coverage: Access to thousands of experimental properties
• Robust Error Handling: Graceful handling of missing data and API errors
• Multiple Data Formats: Support for various property types and units
• Batch Processing: Efficient handling of multiple compounds
• Data Quality: Access to source information and multiple measurements

Additional Resources¶

• PubChem Documentation: https://pubchem.ncbi.nlm.nih.gov/docs/
• Available Properties: https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72
• API Reference: Check the package documentation for complete method descriptions

Best Practices¶

1. Always handle errors - Not all compounds have all properties
2. Validate data quality - Check sources and multiple measurements
3. Respect rate limits - The API has usage limitations
4. Cache results - Store frequently used data to avoid repeated requests
5. Verify units - Different sources may use different unit systems

Happy chemical data analysis! 🧪📊