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Python Data Validation And Observability As Code With Pydantic

2025 05 30 PythonDataValidationAndObservabilityAsCodeWithPydantic800600

In this post, I use the Pydantic Python library to create data validation and observability processes for my Project Wolfie iTunes data.

Table of Contents

Introduction

Data validation is a crucial component of any data project. It ensures that data is accurate, consistent and reliable. It verifies that data meets set criteria and rules to maintain its quality, and stops erroneous or unreliable information from entering downstream systems. I’ve written about it, scripted it and talked about it.

Validation will be a crucial aspect of Project Wolfie. It is an ongoing process that should occur from data ingestion to exposure, and should be automated wherever possible. Thankfully, most data processes within Project Wolfie are (and will be) built using Python, which provides several libraries to simplify data validation. These include Pandera, Great Expectations and the focus of this post – Pydantic (specifically, version 2).

Firstly, I’ll explore the purpose and benefits of Pydantic. Next, I’ll import some iTunes data and use it to explore key Pydantic validation concepts. Finally, I’ll explore how Pydantic handles observability and test its findings. The complete code will be in a GitHub repo.

Let’s begin!

Introducing Pydantic

This section introduces Pydantic and examines some of its benefits.

About Pydantic

Pydantic is an open-source data validation Python library. It uses established Python notation and constructs to define data structures, types and constraints. These can then validate the provided data, generating clear error messages when issues occur.

Pydantic is a widely used tool for managing application settings, validating API requests and responses, and streamlining data transfer between Python objects and formats like JSON. By integrating both existing and custom elements, it offers a powerful and Pythonic method for ensuring data quality and consistency within projects. This makes data handling in Python more reliable and reduces the likelihood of errors through its intuitive definition and validation processes.

Pydantic Benefits

Pydantic’s benefits are thoroughly documented, and the ones I want to highlight here are:

Intuitive: Pydantic’s use of type hints, functions and classes fits well with my current Python skill level, so I can focus on learning Pydantic without also having to explore unfamiliar Python concepts.

Fast: Pydantic’s core validation logic is written in Rust, which enables rapid development, testing, and validation. This speed has contributed towards…

Well-Supported: Pydantic has extensive community use and support from organisations like Anthropic, Netflix and OpenAI, as well as popular Python libraries like Airflow, FastAPI and LangChain. It also has extensive AWS Lambda support via user-configurable artefacts and the community-managed Powertools for AWS Lambda (Python)‘s Parser utility.

Preparation

Before I can start using Pydantic, I need some data. This section examines the data I am using and how I prepare it for Pydantic.

iTunes Data

Firstly, let’s extract some data from iTunes. I create iTunes Export files using the iTunes > Export Playlist command. Apple has documented this, but WikiHow’s documentation is more illustrative. The export file type choices are…interesting. The one closest to matching my needs is the txt format, although the files are technically tab-separated files (TSVs).

iTunes Exports contain many metadata columns. I’m not including them all here (after all, this is a Pydantic post not an iTunes one), but I will be using the following subset (using my existing metadata definitions):

Metadata TypeColumn NameData TypePurpose
TechnicalAlbumStringTrack key as Camelot Notation*
TechnicalLocationStringTrack file path
TechnicalTrack NumberIntegerTrack BPM*
DescriptiveArtistStringTrack artist(s)
DescriptiveGenreStringTrack genre
DescriptiveNameStringTrack name and mix
DescriptiveWorkStringPublishing record label
DescriptiveYearIntegerTrack release year
InteractionMy RatingIntegerTrack personal rating

Note that the starred Album and Track Number columns have purposes that differ from the column names. The reasons for this are…not ideal.

  • Track Number contains BPM data as, although iTunes does have a BPM column, it isn’t included in the exports. And the exports can’t be customised! To include BPMs in an export, I had to repurpose an existing column.

Great. But that’s not as bad as…

  • Album contains musical keys, as iTunes doesn’t even have a key column, despite MP3s having a native Initial Key metadata field! Approaches to dealing with this vary – I chose to use another donor column. I’ll explain Camelot Notations later on.

That’s enough about the iTunes data for now – I’ll go into more detail in future Project Wolfie posts. Now let’s focus on getting this data into memory for Python.

Data Capture

Next, let’s get the iTunes data into memory. Starting with a familiar library…

pandas

I’ll be using pandas to ingest the iTunes data. This is a well-established and widely supported module. It also has its own data validation functions and will assist with issues like handling spaces in column names.

While iTunes files aren’t CSVs, the pandas read_csv function can still read their data into a DataFrame. It needs some help though – the delimiter parameter must be \t to identify the tabs’ delimiting status.

So let’s read the iTunes metadata into memory and…

Python
df = pd.read_csv(csv_path, delimiter='\t')

>> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

Oh. pandas can’t read the file. The error says it’s trying the utf-8 codec, so the export must be using something else. Fortunately, there’s another Python library that can help!

charset_normalizer

charset_normalizer is an open-source encoding detector. It determines the encoding of a file or text and records the result. It’s related to the older chardet library but is faster, has a more permissive MIT license and supports more encodings.

Here, I’m using charset_normalizer.detect in a detect_file_encoding function to detect the export’s codec:

Python
def detect_file_encoding(file_path: Path) -> str:
    with open(file_path, 'rb') as file:
        raw_data = file.read()
        
    detection_result = charset_normalizer.detect(raw_data)
    return detection_result['encoding'] or 'utf-8'

In which:

  • I define a detect_file_encoding function that expects a filepath and returns a string.
  • detect_file_encoding opens the file, reads the data and stores it as raw_data.
  • charset_normalizer detects raw_data‘s codec and stores this as detection_result.
  • detect_file_encoding returns either the successfully detected codec, or the common utf-8 codec if the attempt fails.

I can then pass the export’s filepath to the detect_file_encoding function, capture the results as encoding and pass this as a parameter to pandas.read_csv:

Python
encoding = detect_file_encoding(csv_path)
    
df = pd.read_csv(csv_path, encoding=encoding, delimiter='\t')

>> Loaded 4407 rows

There’s one more action to take before moving on. Some columns contain spaces. This will become a problem as spaces are not allowed in Python identifiers!

As the data is now in a pandas DataFrame, I can use pandas.DataFrame.rename to remove these spaces:

Python
df = df.rename(columns={
        'Track Number': 'TrackNumber',
        'My Rating': 'MyRating'
    })

The metadata is now ready for Pydantic.

Installing Pydantic

Finally, let’s install Pydantic. This process is fully documented. My preferred method is via pip install in a local virtual environment:

Python
pip install pydantic

And then importing Pydantic into my script:

Python
import pydantic

Now I can start using Pydantic.

Pydantic Data Models

In this section, I tell Pydantic about my data model and the types of data it should expect for validation.

Introducing BaseModel

At the core of Pydantic is the BaseModel class – used for defining data models. Every Pydantic model inherits from it, and by doing so gains features like type enforcement, automatic data parsing and built-in validation.

By subclassing BaseModel, a schema for the data is defined using standard Python type hints. Pydantic uses these hints to validate and convert input data automatically.

Let’s explore BaseModel by creating a new Track class.

Creating A Track Class

Pydantic supports standard library types like string and integer. This reduces Pydantic’s learning curve and simplifies integration into existing Python processes.

Here are the very beginnings of my Track data model. I have a new Track class inheriting from Pydantic’s BaseModel, and a Name field with string data type:

Python
class Track(BaseModel):
    Name: str

Next, I add a Year field with integer data type:

Python
class Track(BaseModel):
    Name: str
    Year: int

And so on for each field I want to validate with Pydantic:

Python
class Track(BaseModel):
    Name: str
    Artist: str
    Album: str
    Work: str
    Genre: str
    TrackNumber: int
    Year: int
    MyRating: int
    Location: str

Now, if any field is missing or has the wrong type, Pydantic will raise a ValidationError. But there’s far more to Pydantic data types than this…

Defining Special Data Types

Where no standards exist or where validation rules are more complex to determine, Pydantic offers further type coverage. These include:

One of my Track fields will immediately benefit from this:

Python
class Track(BaseModel):
    Location: str

Currently, my Location field validation is highly permissive. It will accept any string. I can improve this using Pydantic’s FilePath data type:

Python
class Track(BaseModel):
    Location: FilePath

Now, Pydantic will check that the given location is a path that exists and links to a valid file. No custom code; no for loops – the FilePath type handles everything for me.

So I now have data type validation in my Pydantic data model. What else can I have?

Pydantic Built-In Validation

This section explores the native data validation features of Pydantic, including field annotation and constraints.

Introducing Field

In Pydantic models, data attributes are typically defined using Python type hints. The Field function enables further customisation like constraints, schema metadata and default values.

While type hints define what kind of data is allowed, Field defines how that data should behave, what happens if it’s missing and how it should be documented. It adds clarity to models and helps Pydantic enforce stricter rules.

Let’s run through some examples.

Custom Schema Metadata

One of the challenges in creating data pipelines is that the data fields can sometimes be unclear or difficult to explain. This can cause confusion and delay when building ETLs, examining repos and interacting with code.

Field helps here by adding custom fields to annotate data within Pydantic classes. Examples include description:

Python
class Track(BaseModel):
    Name: str = Field(
        description="Track's name and mix.")

And examples:

Python
class Track(BaseModel):
    Name: str = Field(
        description="Track's name and mix.",
        examples=["Track Title (Original Mix)", "Track Title (Extended Mix)"])

Using these throughout my Track class simplifies the code and reduces context switching:

Python
class Track(BaseModel):
    Name: str = Field(
        description="Track's name and mix.",
        examples=["Track Title (Original Mix)", "Track Title (Extended Mix)"])
    
    Artist: str = Field(
        description="The artist(s) of the track.",
        examples=["Above & Beyond", "Armin van Buuren"])
    
    Album: str = Field(
        description="Track's Camelot Notation indicating the key.",
        examples=["01A-Abm", "02B-GbM"])
    
    Work: str = Field(
        description="The record label that published the track.",
        examples=["Armada Music", "Anjunabeats"])
    
    Genre: str = Field(
        description="Track's musical genre.",
        examples=["Trance", "Progressive House"])
    
    TrackNumber: int = Field(
        description="Track's BPM (Beats Per Minute).",
        examples=[130, 140])
    
    Year: int = Field(
        description="Track's release year.",
        examples=[1998, 2004])
    
    MyRating: int = Field(
        description="Personal Rating.  Stars expressed as 0, 20, 40, 60, 80, or 100",
        examples=[60, 80])
    
    Location: FilePath = Field(
        description="Track's Location on the filesystem.",
        examples=[r"C:\Users\User\Music\iTunes\TranquilityBase-GettingAway-OriginalMix.mp3"])

This is especially useful for Album and TrackNumber given their unique properties.

Field Constraints

Field can also constrain the data that a class accepts. This includes string constraints:

  • max_length: Maximum length of the string.
  • min_length: Minimum length of the string.
  • pattern: A regular expression that the string must match.

and numeric constraints:

  • ge & le – greater than or equal to/less than or equal to
  • gt & lt – greater/less than
  • multiple_of – multiple of a given number

Constraints can also be combined as needed. For example, iTunes exports record MyRating values in increments of 20, where 1 star is 20 and 2 stars are 40, rising to the maximum 5 stars being 100.

I can express this within the Track class as:

Python
class Track(BaseModel):
    MyRating: int = Field(
        description="Personal Rating.  Stars expressed as 0, 20, 40, 60, 80, or 100",
        examples=[60, 80],
        ge=20,
        le=100,
        multiple_of=20)

Here, MyRating must be greater than or equal to 20 (ge=20), less than or equal to 100 (le=100), and must be a multiple of 20 (multiple_of=20).

I can also parameterise these constraints using variables instead of hard-coded values:

Python
ITUNES_RATING_RAW_LOWEST = 20
ITUNES_RATING_RAW_HIGHEST = 100

class Track(BaseModel):
    MyRating: int = Field(
        description="Personal Rating.  Stars expressed as 0, 20, 40, 60, 80, or 100",
        examples=[60, 80],
        ge=ITUNES_RATING_RAW_LOWEST,
        le=ITUNES_RATING_RAW_HIGHEST,
        multiple_of=20)

This property lets me use Pydantic with other Python libraries. Here, my Year validation checks for years greater than or equal to 1970 and less than or equal to the current year (using the datetime library):

Python
YEAR_EARLIEST = 1970
YEAR_CURRENT = datetime.datetime.now().year

class Track(BaseModel):
    Year: int = Field(
        description="Track's release year.",
        examples=[1998, 2004],
        ge=YEAR_EARLIEST,
        le=YEAR_CURRENT)

No track in the collection should exist beyond the current year – this constraint will now update itself as time passes.

Having applied other constraints, my Track class looks like this:

Python
class Track(BaseModel):
    """Pydantic model for validating iTunes track metadata."""
    
    Name: str = Field(
        description="Track's name and mix type.",
        examples=["Track Title (Original Mix)", "Track Title (Extended Mix)"])
    
    Artist: str = Field(
        description="The artist(s) of the track.",
        examples=["Above & Beyond", "Armin van Buuren"])
    
    Album: str = Field(
        description="Track's Camelot Notation indicating the key.",
        examples=["01A-Abm", "02B-GbM"])
    
    Work: str = Field(
        description="The record label that published the track.",
        examples=["Armada Music", "Anjunabeats"])
    
    Genre: str = Field(
        description="Track's musical genre.",
        examples=["Trance", "Progressive House"])
    
    TrackNumber: int = Field(
        description="Track's BPM (Beats Per Minute).",
        examples=[130, 140],
        ge=BPM_LOWEST,
        le=BPM_HIGHEST)
    
    Year: int = Field(
        description="Track's release year.",
        examples=[1998, 2004],
        ge=YEAR_EARLIEST,
        le=YEAR_CURRENT)
    
    MyRating: int = Field(
        description="Personal Rating. Stars expressed as 0, 20, 40, 60, 80, or 100",
        examples=[60, 80],
        ge=ITUNES_RATING_RAW_LOWEST,
        le=ITUNES_RATING_RAW_HIGHEST,
        multiple_of=20)
    
    Location: FilePath = Field(
        description="Track's Location on the filesystem.",
        examples=[r"C:\Users\User\Music\iTunes\AboveAndBeyond-AloneTonight-OriginalMix.mp3"])

This is already very helpful. Next, let’s examine my custom requirements.

Pydantic Custom Validation

This section discusses how to create custom data validation using Pydantic. I will outline what the requirements are, and then examine how these validations are defined and implemented.

Introducing Decorators

In Python, decorators modify or enhance the behaviour of functions or methods without changing their actual code. Decorators are usually written using the @ symbol followed by the decorator name, just above the function definition:

Python
@my_decorator
def my_function():
    ...

For example, consider this logger_decorator function:

Python
def logger_decorator(func):
    def wrapper():
        print(f"Running {func.__name__}...")
        func()  # Execute the supplied function
        print("Done!")
    return wrapper

This function takes another function (func) as an argument, printing a message before and after execution. If the logger_decorator function is then used as a decorator when running this greet function:

Python
@logger_decorator
def greet():
    print("Hello, world!")

greet()

Python will add the logging behaviour of logger_decorator without modifying greet:

Python
Running greet...
Hello, world!
Done!

Introducing Field Validators

In addition to the built-in data validation capabilities of Pydantic, custom validators with more specific rules can be defined for individual fields using Field Validators. These use the field_validator() decorator, and are declared as class methods within a class inheriting from Pydantic’s BaseModel.

Here’s a basic example using my Track model:

Python
class Track(BaseModel):
    Name: str = Field(
        description="Track's name and mix.",
        examples=["Track Title (Original Mix)", "Track Title (Extended Mix)"]
    )

    @field_validator("Name")
    @classmethod
    def validate_name(cls, value):
        # custom validation logic here
        return value

Where:

  • @field_validator("Name") tells Pydantic to use the function to validate the Name field.
  • @classmethod lets the validator access the Track class (cls).
  • The validator executes the validate_name function with the field value (in this case Name) as input, performs the checks and must either:
    • return the validated value, or
    • raise a ValueError or TypeError if validation fails.

Let’s see this in action.

Null Checks

Firstly, let’s perform a common data validation check by identifying empty fields. I have two variants of this – one for strings and another for numbers.

The first – validate_non_empty_string – uses pandas.isna to catch missing values and strip() to catch empty strings. This field validator applies to the Artist, Work and Genre columns:

Python
    @field_validator("Artist", "Work", "Genre")
    @classmethod
    def validate_non_empty_string(cls, value, info):
        """Validate that a string field is not empty."""
        if pd.isna(value) or str(value).strip() == "":
            raise ValueError(f"{info.field_name} must not be null or empty")
        return value

The second – validate_non_null_numeric – checks the TrackNumber, Year and MyRating numeric columns for empty values using pandas.isna:

Python
    @field_validator("TrackNumber", "Year", "MyRating", mode="before")
    @classmethod
    def validate_non_null_numeric(cls, value, info):
        """Validate that a numeric field is not null."""
        if pd.isna(value):
            raise ValueError(f"{info.field_name} must not be null")
        return value

Also, it uses Pydantic’s before validator (mode="before"), ensuring the data validation happens before Pydantic coerces types. This catches edge cases like "" or "NaN" before they become None or float("nan") values.

Character Check

Now let’s create a validator for something a little more challenging to define. All tracks in my collection follow a Track Name (Mix) schema. This can take many forms:

  • Original track: Getting Away (Original Mix)
  • Remixed track: Shapes (Oliver Smith Remix)
  • Updated remixed track: Distant Planet (Menno de Jong Interpretation) (2020 Remaster)
  • …and many more variants.

But generally, there should be at least one instance of text enclosed by parentheses. However, some tracks have no remixer and are released with just a title:

  • Getting Away
  • Shapes
  • Distant Planet

This not only looks untidy (eww!), but also breaks some of my downstream automation that expects the Track Name (Mix) schema. So any track without a remixer gets (Original Mix) added to the Name field upon download:

  • Getting Away (Original Mix)
  • Shapes (Original Mix)
  • Distant Planet (Original Mix)

Expressing this is possible with RegEx, but I can make a more straightforward and more understandable check with a field validator:

Python
    @field_validator("Name")
    @classmethod
    def validate_name(cls, value):
        if pd.isna(value) or str(value).strip() == "":
            raise ValueError("Name must not be null or empty")
        
        value_str = str(value)
        if '(' not in value_str:
            raise ValueError("Name must contain an opening parenthesis '('")
        if ')' not in value_str:
            raise ValueError("Name must contain a closing parenthesis ')'")
        return value

This validator checks that the value isn’t empty and then performs additional checks for parentheses. This could be one check, but having it as two checks improves log readability (insert foreshadowing – Ed). I could also have added Name to the validate_non_empty_string validation, but this way I have all my Name checks in the same place.

Parameterised Checks

Like constraints, field validators can also be parameterised. Let’s examine Album.

As iTunes exports can’t be customised, I use Album for a track’s Camelot Notation. These are based on the Camelot Wheel – MixedInKey‘s representation of the Circle Of Fifths. DJs generally favour Camelot Notation as it is simpler than traditional music notation for human understanding and application sorting.

Importantly, there are only twenty-four possible notations:

For example:

  • 1A (A-Flat Minor)
  • 6A (G Minor)
  • 6B (B-Flat Major)
  • 10A (B Minor)

So let’s capture these values in a CAMELOT_NOTATIONS list:

Python
CAMELOT_NOTATIONS = {
    '01A-Abm', '01B-BM', '02A-Ebm', '02B-GbM', '03A-Bbm', '03B-DbM',
    '04A-Fm', '04B-AbM', '05A-Cm', '05B-EbM', '06A-Gm', '06B-BbM',
    '07A-Dm', '07B-FM', '08A-Am', '08B-CM', '09A-Em', '09B-GM',
    '10A-Bm', '10B-DM', '11A-Gbm', '11B-AM', '12A-Dbm', '12B-EM'
}

(Note the leading zeros. Without them, iTunes sorts the Album column as (10, 11, 12, 1, 2, 3…) – you can imagine how I felt about that – Ed)

Next, I pass the CAMELOT_NOTATIONS list to an Album field validator that checks if the given value is in the list:

Python
    @field_validator("Album")
    @classmethod
    def validate_album(cls, value):
        if pd.isna(value) or str(value).strip() == "":
            raise ValueError("Album must not be null or empty")
        
        if str(value) not in CAMELOT_NOTATIONS:
            raise ValueError(f"Album must be a valid Camelot notation: {value} is not in the valid list")
        return value

Pydantic now fails any value not found in the CAMELOT_NOTATIONS list.

Now I have my validation needs fully covered. What observability does Pydantic give me over these data validation checks?

Pydantic Observability

In this section, I assess and adjust the default Pydantic observability abilities to ensure my data validation is accurately recorded.

Default Output

Pydantic automatically generates data validation error messages if validation fails. These detailed messages provide a structured overview of the issues encountered, including:

  • The index of the failing input (e.g., a DataFrame row number).
  • The model class where the error occurred.
  • The field name that failed validation.
  • A human-readable explanation of the issue.
  • The offending input value and its type.
  • A direct link to relevant documentation for further guidance.

Here’s an example of Pydantic’s output when a string field receives a NaN value:

Python
Row 2353: 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type

In this example:

  • Row 2353 indicates the problematic input row.
  • Track is the Pydantic model where validation failed.
  • Work is the failing field.
  • Pydantic detects that the input is nan (a float) and not a valid string.
  • Pydantic provides a URL to the string_type documentation.

Here’s another example, this time for a MyRating error:

Python
Row 3040: 1 validation error for Track
MyRating
  Value error, MyRating must not be null [type=value_error, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

In this case, a field validator raised a ValueError because MyRating must not be null.

Pydantic’s error reporting is clear and actionable, making it suitable for debugging and systemic data validation tasks. However, for larger datasets or more user-friendly outputs (such as reports or UI feedback), further customisation is helpful, such as…

Terminal Output Customisation

As good as Pydantic’s default output is, it’s not that human-readable. For example, in this Terminal output I have no idea which tracks are on rows 2353, 2495 and 3040:

Plaintext
Row 2353: 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
    
Row 2495: 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
    
Row 3040: 1 validation error for Track
MyRating
  Value error, MyRating must not be null [type=value_error, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

While I can find this out, it would be better to know at a glance. Fortunately, I can improve this when capturing the errors by appending the artist and name to each row of the errors object:

Python
except (ValidationError, ValueError) as e:
            artist = row['Artist'] if not pd.isna(row['Artist']) else "Unknown Artist"
            name = row['Name'] if not pd.isna(row['Name']) else "Unknown Name"
            errors.append((index, artist, name, str(e)))

Now, Artist and Name are added to each row:

Plaintext
Row 2353: Ben Stone - Mercure (Extended Mix): 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
    
Row 2495: DJ Hell - My Definition Of House Music (Resistance D Remix): 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type
    
Row 3040: York - Reachers Of Civilisation (In Search Of Sunrise Mix): 1 validation error for Track
MyRating
  Value error, MyRating must not be null [type=value_error, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

This makes it far easier to find the problematic files in my collection. As long as there aren’t many findings…

Creating An Error File

There are three main problems with Pydantic printing all data validation errors in the Terminal:

  • They don’t persist outside of the Terminal session.
  • The Terminal isn’t that easy to read when it’s full of text.
  • The Terminal may run out of space if there are a large number of errors.

So let’s capture the errors in a file instead. This write_error_report function generates a text-based error report from validation failures, saving it in a logs subfolder adjacent to the input file:

Python
def write_error_report(
    csv_path: Path, 
    field_error_details: Dict[str, List[str]],  
    sorted_fields: List[str]
) -> Path:

    timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
    logs_dir = csv_path.parent / "logs"
    logs_dir.mkdir(exist_ok=True)
    
    error_output_path = logs_dir / f"{timestamp}-PydanticErrors-{csv_path.stem}.txt"
    
    with open(error_output_path, 'w', encoding='utf-8') as f:
        f.write(f"Validation Error Report - {timestamp}\n")
        f.write("=" * 80 + "\n")

        for field in sorted_fields:
            messages = field_error_details.get(field, [])
            if messages:
                f.write(f"\n{field} Errors ({len(messages)}):\n")
                f.write("-" * 80 + "\n")
                for message in messages:
                    f.write(message + "\n\n")
    
    return error_output_path

Firstly, it constructs a timestamped filename using the original file’s stem (e.g., 20250529-142304-PydanticErrors-data.txt) and the logs subfolder, creating the latter if it doesn’t exist:

Python
    timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
    logs_dir = csv_path.parent / "logs"
    logs_dir.mkdir(exist_ok=True)
    
    error_output_path = logs_dir / f"{timestamp}-PydanticErrors-{csv_path.stem}.txt"

Next, Python orders the errors by the sorted_fields input, displays error counts per field and formats each error message with clear section dividers. A structured report listing all validation errors by field is saved in the logs subfolder:

Python
    with open(error_output_path, 'w', encoding='utf-8') as f:
        f.write(f"Validation Error Report - {timestamp}\n")
        f.write("=" * 80 + "\n")

        for field in sorted_fields:
            messages = field_error_details.get(field, [])
            if messages:
                f.write(f"\n{field} Errors ({len(messages)}):\n")
                f.write("-" * 80 + "\n")
                for message in messages:
                    f.write(message + "\n\n")

Finally, the filesystem path of the generated report is returned:

Python
    return error_output_path

When executed, the Terminal tells me the error file path:

Plaintext
Detailed error log written to: 20250513-133743-PydanticErrors-iTunes-Elec-Dance-Club-Main.txt

And stores the findings in a local txt file, grouped by error type for simpler readability:

Plaintext
Validation Error Report - 20250513-133743
================================================================================

MyRating Errors (5):
--------------------------------------------------------------------------------
Row 3040: York - Reachers Of Civilisation (In Search Of Sunrise Mix): 1 validation error for Track
MyRating
  Value error, MyRating must not be null [type=value_error, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

Work Errors (22):
--------------------------------------------------------------------------------
Row 223: Dave Angel - Artech (Original Mix): 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type

Adding A Terminal Summary

Finally, I created a Terminal summary of Pydantic’s findings:

Python
print("\nValidation Summary:\n")
sorted_fields = sorted(Track.model_fields.keys())

for field in sorted_fields:
  count = error_analysis['counts'].get(field, 0)
  print(f"{field} findings: {count}")

This shows feedback after each execution:

Plaintext
Validation Summary:

Album findings: 0
Artist findings: 0
Genre findings: 0
Location findings: 0
MyRating findings: 5
Name findings: 1
TrackNumber findings: 0
Work findings: 22
Year findings: 0

Now, let’s ensure everything works properly!

Testing Pydantic

In this section, I test that my Pydantic data validation and observability processes are working correctly using iTunes export files and pytest unit tests.

Recent File Test

The first test used a recent export from the end of April 2025. Here is the Terminal output:

Plaintext
Processing file: iTunes-Elec-Dance-Club-Main-2025-04-28.txt
Reading iTunes-Elec-Dance-Club-Main-2025-04-28.txt with detected encoding UTF-16
Loaded 4407 rows
Validated 4379 rows
Found 28 errors!

Validation Summary for iTunes-Elec-Dance-Club-Main-2025-04-28.txt:
Album errors: 0
Artist errors: 0
Genre errors: 0
Location errors: 0
MyRating errors: 5
Name errors: 1
TrackNumber errors: 0
Work errors: 22
Year errors: 0

Detailed error log written to: 20250521-164324-PydanticErrors-iTunes-Elec-Dance-Club-Main-2025-04-28.txt

Good first impressions – the 4407 row count matches the export file, the summary is shown in the Terminal and an error log is created. So what’s in the log?

Firstly, five tracks have no MyRating values. For example:

Plaintext
MyRating Errors (5):
--------------------------------------------------------------------------------
Row 558: Reel People Feat Angela Johnson - Can't Stop (Michael Gray Instrumental Remix): 1 validation error for Track
MyRating
  Value error, MyRating must not be null [type=value_error, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

This is correct, as this export was created when I added some new tracks to my collection.

Next, one track has a Name issue:

Plaintext
Name Errors (1):
--------------------------------------------------------------------------------
Row 1292: The Prodigy - Firestarter (Original Mix}: 1 validation error for Track
Name
  Value error, Name must contain a closing parenthesis ')' [type=value_error, input_value='Firestarter (Original Mix}', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

This one confused me at first, until I looked at the error more closely and realised the closing parenthesis is wrong! } is used instead of )! This is why my validate_name field validator has separate checks for each character – it makes it easier to understand the results!

Finally, twenty-two tracks are missing record label metadata in Work:

Plaintext
Work Errors (22):
--------------------------------------------------------------------------------
Row 223: Dave Angel - Artech (Original Mix): 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type

This means some tracks are missing full metadata. This won’t break any downstream processes as I have no reliance on this field. That said, it’s good to know about this in case my future needs change.

Older File Test

The next test uses an older file from March 2025. Let’s see what the Terminal says this time…

Plaintext
Processing file: iTunes-AllTunesMaster-2025-03-01.txt      
Reading iTunes-AllTunesMaster-2025-03-01.txt with detected encoding UTF-16
Loaded 4381 rows
Validated 0 rows
Found 4381 errors!

Validation Summary for iTunes-AllTunesMaster-2025-03-01.txt:

Album errors: 0
Artist errors: 0
Genre errors: 0
Location errors: 4381
MyRating errors: 0
Name errors: 1
TrackNumber errors: 2
Work errors: 17
Year errors: 0
Detailed error log written to: 20250521-164322-PydanticErrors-iTunes-AllTunesMaster-2025-03-01.txt

There are fewer rows here – 4381 vs 4407. This is correct, as my collection was smaller in March. But no rows were validated successfully!

I don’t have to go far to find out why:

Plaintext
Location Errors (4381):
--------------------------------------------------------------------------------
Row 0: Ariel - A9 (Original Mix): 1 validation error for Track
Location
  Path does not point to a file [type=path_not_file, input_value='C:\\Users\\User\\Folder...riel-A9-OriginalMix.mp3', input_type=str]

All the location checks failed. But this is actually a successful test!

In the time between these two exports, I reorganised my music collection. As a result, the file paths in this export no longer exist. Remember – the Location field uses the FilePath data type, which checks that the given paths exist and link to valid files. And these don’t!

The Name results are the same as the first test. This has been around for a while apparently…

Plaintext
Name Errors (1):
--------------------------------------------------------------------------------
Row 1292: The Prodigy - Firestarter (Original Mix}: 1 validation error for Track
Name
  Value error, Name must contain a closing parenthesis ')' [type=value_error, input_value='Firestarter (Original Mix}', input_type=str]
    For further information visit https://errors.pydantic.dev/2.11/v/value_error

There are also TrackNumber errors in this export:

Plaintext
TrackNumber Errors (2):
--------------------------------------------------------------------------------
Row 485: Andrew Bayer Feat Alison May - Brick (Original Mix): 2 validation errors for Track
TrackNumber
  Input should be greater than or equal to 100 [type=greater_than_equal, input_value=90, input_type=int]
    For further information visit https://errors.pydantic.dev/2.11/v/greater_than_equal

Two tracks have BPM values lower than the set range. Both files were moved during my reorganisation, but were included in this export at the time and therefore fail this validation check.

Finally, the Work errors are the same as the first test (although more have crept in since!):

Plaintext
Work Errors (17):
--------------------------------------------------------------------------------
Row 223: Dave Angel - Artech (Original Mix): 1 validation error for Track
Work
  Input should be a valid string [type=string_type, input_value=nan, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type

Ultimately, both tests match expectations!

Unit Tests With Amazon Q

Finally, I wanted to include some unit tests for this project. Unit testing is always a good idea, especially in this context where I can verify function outputs and error generation without needing to create numerous test files.

I figured this was a good opportunity to test Amazon Q Developer and see what it came up with. I gave it a fairly basic prompt, using the @workspace context to allow Q access to my project’s entire workspace as context for its responses:

Plaintext
@workspace write unit tests for this script using pytest

I tend to use pytest for my Python testing, as I find it simpler and more flexible than Python’s standard unittest library.

Q promptly provided several reasonable tests in response. This initiated a half-hour exchange between us focused on calibrating the existing tests and creating new ones. To be fair to Q, my initial prompt was quite basic and could have been much more detailed.

Amongst Q’s tests was this one testing an empty Artist field:

Python
    @patch('pathlib.Path.exists')
    def test_empty_artist(self, mock_exists):
        """Test that an empty artist fails validation."""
        # Mock file existence check
        mock_exists.return_value = True
        
        invalid_track_data = {
            "Name": "Test Track (Original Mix)",
            "Artist": "",  # Empty artist
            "Album": "01A-Abm",
            "Work": "Test Label",
            "Genre": "Trance",
            "TrackNumber": 130,
            "Year": 2020,
            "MyRating": 80,
            "Location": "C:\\Music\\test_track.mp3"
        }

This one, checking an invalid Camelot Notation:

Python
@patch('pathlib.Path.exists')
    def test_invalid_album_not_camelot(self, mock_exists):
        """Test that an invalid Camelot notation fails validation."""
        # Mock file existence check
        mock_exists.return_value = True
        
        invalid_track_data = {
            "Name": "Test Track (Original Mix)",
            "Artist": "Test Artist",
            "Album": "Invalid Key",  # Not a valid Camelot notation
            "Work": "Test Label",
            "Genre": "Trance",
            "TrackNumber": 130,
            "Year": 2020,
            "MyRating": 80,
            "Location": "C:\\Music\\test_track.mp3"
        }
        
        with pytest.raises(ValueError, match="Album must be a valid Camelot notation"):
            Track(**invalid_track_data)

And this one, checking what happens with an incomplete DataFrame:

Python
    @patch('wolfie_exportvalidator_itunes.detect_file_encoding')
    @patch('pandas.read_csv')
    def test_load_itunes_data_missing_columns(self, mock_read_csv, mock_detect_encoding):
        """Test loading iTunes data with missing columns."""
        # Setup mocks
        mock_detect_encoding.return_value = 'utf-8'
        mock_df = pd.DataFrame({
            'Name': ['Test Track (Original Mix)'],
            'Artist': ['Test Artist'],
            # Missing required columns
        })
        mock_read_csv.return_value = mock_df
        
        # Call function and verify it raises an error
        with pytest.raises(ValueError, match="Missing expected columns"):
            load_itunes_data(Path('dummy_path.txt'))

I’ll include the whole test suite in my GitHub repo. Let’s conclude with pytest‘s output:

Plaintext
collected 41 items                                                                                                                                        

tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_valid_track PASSED                                                                [  2%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_valid_track_boundary_values PASSED                                                [  4%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_name_no_parentheses PASSED                                                [  7%]
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_empty_name PASSED                                                                 [  9%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_empty_artist PASSED                                                               [ 12%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_empty_work PASSED                                                                 [ 14%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_empty_genre PASSED                                                                [ 17%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_album_not_camelot PASSED                                                  [ 19%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_valid_camelot_notations PASSED                                                    [ 21%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_bpm_range_high PASSED                                                     [ 24%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_bpm_range_low PASSED                                                      [ 26%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_year_range_early PASSED                                                   [ 29%]
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_year_range_future PASSED                                                  [ 31%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_rating_not_multiple PASSED                                                [ 34%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_rating_too_low PASSED                                                     [ 36%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_invalid_rating_too_high PASSED                                                    [ 39%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_null_track_number PASSED                                                          [ 41%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_null_year PASSED                                                                  [ 43%] 
tests/test_wolfie_exportvalidator_itunes.py::TestTrackModel::test_null_rating PASSED                                                                [ 46%]
tests/test_wolfie_exportvalidator_itunes.py::TestFileOperations::test_detect_file_encoding PASSED                                                   [ 48%] 
tests/test_wolfie_exportvalidator_itunes.py::TestFileOperations::test_detect_file_encoding_latin1 PASSED                                            [ 51%] 
tests/test_wolfie_exportvalidator_itunes.py::TestFileOperations::test_detect_file_encoding_no_result PASSED                                         [ 53%] 
tests/test_wolfie_exportvalidator_itunes.py::TestFileOperations::test_load_itunes_data_success PASSED                                               [ 56%]
tests/test_wolfie_exportvalidator_itunes.py::TestFileOperations::test_load_itunes_data_missing_columns PASSED                                       [ 58%] 
tests/test_wolfie_exportvalidator_itunes.py::TestFileOperations::test_load_itunes_data_empty_dataframe PASSED                                       [ 60%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_validate_tracks_all_valid PASSED                                                  [ 63%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_validate_tracks_with_errors PASSED                                                [ 65%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_duplicate_location PASSED                                                         [ 68%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_analyze_errors PASSED                                                             [ 70%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_analyze_errors_with_general_error PASSED                                          [ 73%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_write_error_report PASSED                                                         [ 75%]
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_process_file_with_errors PASSED                                                   [ 78%] 
tests/test_wolfie_exportvalidator_itunes.py::TestValidation::test_process_file_no_errors PASSED                                                     [ 80%]
tests/test_wolfie_exportvalidator_itunes.py::TestParamsData::test_bpm_range_valid PASSED                                                            [ 82%] 
tests/test_wolfie_exportvalidator_itunes.py::TestParamsData::test_year_range_valid PASSED                                                           [ 85%] 
tests/test_wolfie_exportvalidator_itunes.py::TestParamsData::test_rating_range_valid PASSED                                                         [ 87%] 
tests/test_wolfie_exportvalidator_itunes.py::TestParamsData::test_camelot_notations_valid PASSED                                                    [ 90%] 
tests/test_wolfie_exportvalidator_itunes.py::TestMain::test_main_with_files PASSED                                                                  [ 92%] 
tests/test_wolfie_exportvalidator_itunes.py::TestMain::test_main_no_files PASSED                                                                    [ 95%] 
tests/test_wolfie_exportvalidator_itunes.py::TestMain::test_main_with_exception PASSED                                                              [ 97%]
tests/test_wolfie_exportvalidator_itunes.py::TestMain::test_main_with_critical_exception PASSED                                                     [100%] 

=================================================================== 41 passed in 0.20s ===================================================================

I had a very positive experience overall! Working with Amazon Q allowed me to write the tests more quickly than I could have done on my own. We would have been even faster if I had put more thought into my initial prompt. Additionally, since Q Developer offers a generous free tier, it didn’t cost me anything.

GitHub Repo

I have committed my Pydantic data validation script, test suite and documentation in the repo below:

GitHub-BannerSmall

Note that the parameters are decoupled from the Pydantic script. This will allow me to reuse some parameters across future validation scripts and has enabled me to exclude the system parameters from the repository.

Summery

In this post, I used the Pydantic Python library to create data validation and observability processes for my Project Wolfie iTunes data.

I found Pydantic very impressive! Its simplicity, functionality and interoperability make it an attractive addition to Python data pipelines, and its strong community support keeps Pydantic relevant and current. Additionally, Pydantic’s presence in FastAPI, PydanticAI and a managed AWS Lambda layer enables rapid integration and seamless deployment. I see many applications for it within Project Wolfie.

There’s lots more to Pydantic – this Pixegami video is a great walkthrough of Pydantic in action:

If this post has been useful then the button below has links for contact, socials, projects and sessions:

SharkLinkButton 1

Thanks for reading ~~^~~

Categories
Data & Analytics

Creating A Basic iTunes ETL With Python And AWS Data Wrangler

In this post I will use Python and AWS Data Wrangler to create a basic iTunes ETL that extracts data from an iTunes export file into a Pandas DataFrame.

Table of Contents

Introduction

For many years I have enjoyed various forms of dance music. Starting with my first compilation CDs in 2000, I’ve since amassed a large collection of records, CDs and virtual media ranging from the late 80s to modern times.

I started using iTunes as my main media player in 2010. Since then I have built up a large database of iTunes metadata that includes various counts, ratings and timestamps.

Currently I use this data for a series of iTunes Smart Playlists. To derive further meaning from the data and to practise my Python skills, I want to extract this data from iTunes and analyse it using the various data tools at my disposal.

To get the ball rolling I’m going to build a basic iTunes ETL, which I will continue to develop over the coming months.

Let’s start by looking at the iTunes export process.

iTunes Export Files

I use iTunes 12.6.4.3. This isn’t by choice – iTunes 12.6.4.3 is the last version with a built-in App Store, allowing my battered old iPhone 3GS to live on in its second life as an iPod Touch:

Still works!

I mention this as newer versions of iTunes may be different, or may not offer an export feature at all. Why do I persist with this ageing setup? That…is a post for another time.

Every week I sync my Not-iPhone via iTunes, and then create an export of my master playlist:

iTunes doesn’t have many export options, and exports playlists as tab-delimited txt files by default:

To give myself an easier time for this post, I manually made the following changes to a recent iTunes export file:

  • Imported the txt file into Microsoft Excel.
  • Removed columns I didn’t want.
  • Saved the altered file as a csv.
  • Uploaded the csv to Amazon S3.

This Franken-File will be what I use to build my basic iTunes ETL. I understand there are ways of dealing with txt files in Python – I’ll be exploring this in future posts.

Setup

Before starting to write any code, I have done the following:

Advisory

During this post, I will make several decisions that will be revisited in the coming months as my skills improve. I have taken steps to protect my AWS credentials (more on that shortly) but at this stage my basic iTunes ETL Python script is a work in progress and should not be used in a Production environment.

Creating Secure Variables

My first job is to create the variables I’m going to need. As these variables can compromise my AWS account in the wrong hands, I want to create them as securely as possible.

The topic of security is something I will be returning to in future posts. For now, I’m using a similar method to PowerShell’s Dot Sourcing in last month’s post.

Python’s import statement can import other Python scripts in the same way as modules. With this in mind, I create a new ETL_ITU_Play_Variables.py file for my variables.

Importing ETL_ITU_Play_Variables into my main script will allow Python to locate the variables and call them successfully:

import ETL_ITU_Play_Variables

aws_accesskey = ETL_ITU_Play_Variables.AWS_ACCESSKEY
aws_secret = ETL_ITU_Play_Variables.AWS_SECRET

Next I create a gitignore file and add ETL_ITU_Play_Variables.py to it. I can now use these variables in my local environment, safe in the knowledge that Git will not track ETL_ITU_Play_Variables and will not include it in any commits.

With that taken care of, I need two sets of variables.

Creating Authentication Variables

AWS authenticates every request before completing it. As none of my AWS resources are public, I need to provide credentials that have the necessary IAM permissions.

There are various ways to provide these credentials – in this case I’m using an AWS Access Key / Secret Key combination with a variable for each string:

aws_accesskey = 'accesskey123456789'
aws_secretkey = 'secretkey123456789'

As additional security, these keys belong to a new IAM user that only has permission to read S3 objects in the appropriate bucket.

I now need a way to pass these keys to AWS. I use the AWS SDK for Python (Boto3) for this, creating a session variable using boto3.session.Session

session = boto3.session.Session
(
aws_access_key_id = aws_accesskey,
aws_secret_access_key = aws_secret
)

Creating S3 Variables

Next I create the S3 variables I need. I use s3_bucket for the bucket name and s3_prefix for the iTunes export csv‘s bucket prefix.

s3_bucket = 'example-my-bucket'
s3_prefix = 'Example/MyPath/'

I then use these variables to create s3_path for AWS Data Wrangler to use:

s3_path = f"s3://{s3_bucket}/{s3_prefix}"

Making The ETL

With my variables in place, I can start working on my basic iTunes ETL! AWS is now accepting my requests, so let’s start configuring AWS Data Wrangler.

Creating The DataFrame

AWS Data Wrangler is essentially Pandas on AWS, and the two tools share many commands. This DataEng Uncomplicated AWS Data Wrangler Overview does a great job of explaining the fundamentals:

I read the iTunes Export csv‘s contents by using awswrangler.s3.read_csv with the following parameters:

  • path: My s3_path variable.
  • path_suffix: The files I want to read, in this case .csv.
  • boto3_session: My session variable.

This reads all the csv files in the S3 path, which is fine for now.

df = wr.s3.read_csv(path = s3_path,
                    path_suffix = ".csv",
                    boto3_session = session
                    )

I can then print the columns in a DataFrame:

print (f'Dataframe columns are {df.columns}')

Dataframe columns are Index(['Name', 'Artist', 'Album', 'Genre', 'Time', 'Track Number', 'Year', 'Date Modified', 'Date Added', 'Bit Rate', 'Plays', 'Last Played', 'Skips', 'Last Skipped', 'My Rating', 'Location'], dtype='object')

Deleting Unnecessary Columns

Having seen the list of columns, there are some I don’t need. I can get rid of them using pandas.DataFrame.drop:

df = df.drop(columns=
    [
        'Time',
        'Bit Rate',
        'Skips',
        'Last Skipped',
        'Location'
    ]
)

Now, when I print the list of columns, the removed columns are no longer included:

print (f'Dataframe columns are now {df.columns}')

Dataframe columns are now Index(['Name', 'Artist', 'Album', 'Genre', 'Track Number', 'Year', 'Date Modified', 'Date Added', 'Plays', 'Last Played', 'My Rating'], dtype='object')

Renaming Columns

Next, I want to rename the columns. I use pandas.DataFrame.rename to map the current column names to the new ones:

df = df.rename(columns=
    {
        'Name' : 'name',
        'Artist' : 'artist',
        'Album' : 'album',
        'Genre' : 'genre',
        'Track Number' : 'tracknumber',
        'Year' : 'year',
        'Date Modified' : 'datemodified',
        'Date Added' : 'dateadded',
        'Plays' : 'plays',
        'Last Played' : 'lastplayed',
        'My Rating' : 'myrating'
    }
)

The columns are now changed to:

print (f'Dataframe columns are now named {df.columns}')

Dataframe columns are now named Index(['name', 'artist', 'album', 'genre', 'tracknumber', 'year', 'datemodified', 'dateadded', 'plays', 'lastplayed', 'myrating'], dtype='object')

Reformatting DateTime Columns

I now want to make sure that the dates in my DataFrame are stored in ISO 8601 format, as this will make them earlier to work with and report against.

When I print the dateadded column as an example, the dates are not currently in this format:

print (f'Dataframe Date Added column is {df.dateadded}')

1       05/04/2021 13:29
2       26/01/2019 18:25
3       30/12/2016 17:34
4       12/12/2015 00:43

I can resolve this using the dayfirst and yearfirst arguments of pandas.to_datetime:

df['dateadded'] = pd.to_datetime(df['dateadded'],yearfirst=False,dayfirst=True)

This tells Pandas how to interpret the dates. In the case of 05/04/2021, dayfirst=True tells Pandas this is 5th April 2021, as opposed to 4th May 2021.

Pandas then parses the rest of my dates in the same way, giving me the formatting I want:

1      2021-04-05 13:29:00
2      2019-01-26 18:25:00
3      2016-12-30 17:34:00
4      2015-12-12 00:43:00

I repeat this for the datemodified and lastplayed columns.

Creating Date Columns From DateTime Columns

I now want to create some new columns in my DataFrame.

The first of these new columns will mirror the values in the existing date columns. However, these columns will not contain the full timestamp – they will only contain the date instead. This will make it easier to aggregate my data.

To do this, I use pandas.Series.dt.date to create three new columns in the DataFrame:

df['datemodifieddate'] = df['datemodified'].dt.date
df['dateaddeddate'] = df['dateadded'].dt.date
df['lastplayeddate'] = df['lastplayed'].dt.date

The new columns retain the original date values and remove the unneeded time values:

print (f'Dataframe Date Added Date column is {df.dateaddeddate}')

1       2021-04-05
2       2019-01-26
3       2016-12-30
4       2015-12-12

Creating Simplified Rating Columns

I now want to add another column to the DataFrame to simplify reporting against a track’s rating. Ratings in iTunes export files appear in multiples of twenty:

  • 1 star = 20
  • 2 stars = 40
  • 3 stars = 60
  • 4 stars = 80
  • 5 stars = 100

In my current DataFrame, printing myrating produces this:

print (f'Dataframe My Rating is {df.myrating}')

1        40.0
2        40.0
3        60.0
4        80.0

This produces a disconnect between the data in the DataFrame and the data in the iTunes GUI. I would prefer to keep things simple by having a column where the rating value mirrors the iTunes GUI.

This can be added to my DataFrame by using a function. I define an itunes_rating function that will return an integer based on the value that is passed to it:

def itunes_rating(r):
    """Converts ratings in export file to familiar format"""
    if r == 20:
        return 1
    elif r == 40:
        return 2
    elif r == 60:
        return 3
    elif r == 80:
        return 4
    elif r == 100:
        return 5
    else:
        return 0

I then create a new myratingdigit column in my DataFrame by passing each value in the myrating column to the itunes_rating function and capturing the result:

df['myratingdigit'] = df['myrating'].apply(itunes_rating)

And when I print the new column, the results are as expected:

print (f'Dataframe My Rating Digit is {df.myratingdigit}')

1       2
2       2
3       3
4       4

Setting Data Types

Finally, I want to make sure the DataFrame is using the correct data types for each column. Pandas will usually infer data types correctly but doesn’t always get it right.

I can use pandas.DataFrame.dtypes to see the current data types in my DataFrame. At the moment they are:

name                        object
artist                      object
album                       object
genre                       object
tracknumber                  int64
year                         int64
datemodified        datetime64[ns]
dateadded           datetime64[ns]
plays                      float64
lastplayed          datetime64[ns]
myrating                   float64
datemodifieddate            object
dateaddeddate               object
lastplayeddate              object
myratingdigit                int64

Most of these are correct but some need changing. For example, plays will never have decimal places so should be int, and columns like datemodifieddate should be datetime64.

Pandas has several options for this, which are laid out in this helpful Stack Overflow thread. Here, I use astype to assign data types to my dataframe:

df = df.astype(
    {
        'name' : str,
        'artist' : str,
        'album' : str,
        'genre' : str,
        'tracknumber' : int,
        'year' : int,
        'datemodified' : datetime64,
        'dateadded' : datetime64,
        'plays' : int,
        'lastplayed' : datetime64,
        'myrating' : int,
        'datemodifieddate' : datetime64,
        'dateaddeddate' : datetime64,
        'lastplayeddate' : datetime64,
        'myratingint' : int
    }
)

Pandas uses NumPy datetime64 dtypes for working with time series data, so I import it at the top of my script:

from numpy import datetime64

Fixing A Casting Exception

Unfortunately, while testing the newly assigned dtypes I started getting an error:

Exception has occurred: IntCastingNaNError
Cannot convert non-finite values (NA or inf) to integer

This error means that at least one of the columns I’m trying to cast as int contains an empty value. An infinite value is possible, but unlikely due to the various integrity checks iTunes performs on its library.

To find the empty values, I create a second DataFrame using the data in the first, using pandas.DataFrame.isna and pandas.DataFrame.any to find any NA values:

df1 = df[df.isna().any(axis=1)]

Included within the resulting DataFrame were the following tracks:

3571	7 Hours (Original Mix)	Dan Stone	07A-Dm	...	2019-01-26	NaT	1

3575	8th Wonder (Espen & Stian Remix)	8 Wonders	04A-Fm	...	2019-01-26	NaT	1

Checking iTunes shows that these tracks have no plays:

iTunes represents no plays as an empty string as opposed to a zero. This is then extracted into the DataFrame as NA, causing the IntCastingNaN error.

To fix this, I use pandas.DataFrame.fillna to replace the empty fields with zero. Although only the plays column is generating the error, I apply fillna to all the columns being cast as int to prevent any future problems for the ETL:

df['tracknumber'] = df['tracknumber'].fillna(0)
df['year'] = df['year'].fillna(0)
df['plays'] = df['plays'].fillna(0)
df['myrating'] = df['myrating'].fillna(0)

The myratingint column doesn’t need this approach, since my itunes_rating function always returns zero if no conditions are met.

This time, printing the data types shows an acceptable list:

name                        object
artist                      object
album                       object
genre                       object
tracknumber                  int64
year                         int64
datemodified        datetime64[ns]
dateadded           datetime64[ns]
plays                        int64
lastplayed          datetime64[ns]
myrating                     int64
datemodifieddate    datetime64[ns]
dateaddeddate       datetime64[ns]
lastplayeddate      datetime64[ns]
myratingdigit                int64

Exporting The DataFrame As A CSV

This is as far as I’m going to take the DataFrame in this post. As a final check, I want to extract the DataFrame in some form to confirm its suitability for future work I have planned.

The quickest way to do this is with pandas.DataFrame.to_csv. This writes the entire DataFrame to a csv file. When I run:

df.to_csv('ETL-ITU.csv')

A ETL-ITU.csv file is created in the terminal’s working directory that can be viewed and sandboxed as needed.

Scripts

My gitignore file commit from 2022-07-17 can be viewed here:

Basic_iTunes_Python_ETL .gitignore on GitHub

My ETL_ITU_Play.py file commit from 2022-07-17 can be viewed here:

ETL_ITU_Play.py on GitHub

A requirements.txt file has also been created to aid installation. The file commit from 2022-07-20 can be viewed here:

Basic_iTunes_Python_ETL requirements.txt on GitHub

Summary

In this post I used Python and AWS Data Wrangler to create a basic iTunes ETL that extracts data from an iTunes export file into a Pandas DataFrame. I have used various Python modules to extract and transform the data, and the data is now ready to be loaded to a staging area of my choosing.

Expect to see further posts on this in the coming months. This basic iTunes ETL probably won’t stay basic for long!

If this post has been useful, please feel free to follow me on the following platforms for future updates:

Thanks for reading ~~^~~