### Install sas7bdat library

Source: https://github.com/jaredhobbs/sas7bdat/blob/master/README.md

Command to install the sas7bdat package using pip.

```bash
pip install sas7bdat
```

--------------------------------

### SAS7BDAT Constructor with Customization Options

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Illustrates the advanced configuration of the SAS7BDAT constructor. This example shows how to specify parameters like logging level, encoding, error handling, whitespace stripping, and custom date/time format strings for precise control over file parsing.

```python
from sas7bdat import SAS7BDAT
import logging

# Full constructor with all options
reader = SAS7BDAT(
    'data.sas7bdat',
    log_level=logging.WARNING,              # Logging level (default: INFO)
    skip_header=True,                       # Skip column names row
    encoding='utf8',                        # Text encoding (default: utf8)
    encoding_errors='ignore',               # Encoding error handling
    align_correction=True,                  # Memory alignment correction
    strip_whitespace_from_strings=True,     # Strip whitespace from strings
    extra_time_format_strings=['HHMM'],     # Additional time formats
    extra_date_format_strings=['YYQ'],      # Additional date formats
    extra_date_time_format_strings=['DT'],  # Additional datetime formats
    fh=None                                 # Optional file handle (opened in 'rb' mode)
)
```

--------------------------------

### Convert SAS7BDAT to Pandas DataFrame

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Directly convert SAS7BDAT files into pandas DataFrames, enabling immediate data analysis. Includes examples for viewing the head of the DataFrame, checking data types, and performing basic analysis like describe() and groupby().

```python
from sas7bdat import SAS7BDAT

# Convert to DataFrame
with SAS7BDAT('data.sas7bdat') as reader:
    df = reader.to_data_frame()

print(df.head())
print(df.dtypes)
print(f"Shape: {df.shape}")

# Example with data analysis
with SAS7BDAT('sales.sas7bdat') as reader:
    df = reader.to_data_frame()
    print(df.describe())
    print(df.groupby('region').sum())
```

--------------------------------

### Error Handling for SAS7BDAT Parsing

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Provides strategies for handling potential `ParseError` and `IOError` exceptions during file parsing. Includes an example of retrying file parsing with alignment correction disabled if the initial attempt fails.

```python
from sas7bdat import SAS7BDAT, ParseError
import logging

# Handle potential errors
try:
    with SAS7BDAT('data.sas7bdat', log_level=logging.ERROR) as reader:
        for row in reader:
            process(row)
except ParseError as e:
    print(f"Parse error: {e}")
except IOError as e:
    print(f"File error: {e}")

# Try with alignment correction disabled for problematic files
try:
    with SAS7BDAT('problematic.sas7bdat', align_correction=True) as reader:
        data = list(reader)
except Exception:
    # Retry with alignment correction disabled
    with SAS7BDAT('problematic.sas7bdat', align_correction=False) as reader:
        data = list(reader)
```

--------------------------------

### Iterating SAS7BDAT Rows with Type Conversion and Header Skipping

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Shows how to iterate through rows of a SAS7BDAT file, demonstrating automatic conversion of data types to Python natives (float, str, datetime.date, etc.). It includes examples of both reading with and skipping the header row, useful for data-only processing.

```python
from sas7bdat import SAS7BDAT
import datetime

# Read with header row
with SAS7BDAT('tests/data/mixedvalues.sas7bdat') as reader:
    for i, row in enumerate(reader):
        if i == 0:
            print(f"Columns: {row}")
        else:
            print(f"Row {i}: {row}")
# Output: Columns: ['intvalue', 'floatvalue', 'charvalue', 'timevalue', 'datevalue']
# Output: Row 1: [1.0, 0.1, 'abc', datetime.time(0, 0), datetime.date(1980, 1, 1)]
# Output: Row 2: [2.0, 0.2, 'def', datetime.time(2, 22), datetime.date(1990, 12, 31)]
# Output: Row 3: [3.0, 0.3, 'GHI', datetime.time(23, 59), datetime.date(2004, 2, 29)]

# Skip header row for data-only iteration
with SAS7BDAT('tests/data/intvalues.sas7bdat', skip_header=True) as reader:
    for row in reader:
        value = row[0]
        print(f"Value: {value}, Type: {type(value).__name__}")
# Output: Value: 1.0, Type: float
# Output: Value: 2.0, Type: float
```

--------------------------------

### Automatic Handling of Compressed SAS7BDAT Files

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

The library automatically detects and decompresses RLE (Run Length Encoding) and RDC (Ross Data Compression) compressed SAS7BDAT files without explicit user intervention. Examples show how to check the detected compression type.

```python
from sas7bdat import SAS7BDAT

# Compressed files are handled automatically
with SAS7BDAT('compressed_rle.sas7bdat') as reader:
    print(f"Compression: {reader.properties.compression}")
    for row in reader:
        print(row)
# Output: Compression: b'SASYZCRL'  (RLE compression)

# RDC compressed files
with SAS7BDAT('compressed_rdc.sas7bdat') as reader:
    print(f"Compression: {reader.properties.compression}")
    data = list(reader)
# Output: Compression: b'SASYZCR2'  (RDC compression)
```

--------------------------------

### Basic SAS7BDAT File Reading with Context Manager

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Demonstrates the fundamental usage of the SAS7BDAT class to open and read a SAS7BDAT file. It utilizes a context manager for safe file handling and iterates through rows, printing each row. The first row is treated as a header.

```python
from sas7bdat import SAS7BDAT

# Basic usage with context manager
with SAS7BDAT('data.sas7bdat') as reader:
    for row in reader:
        print(row)
# Output: ['col1', 'col2', 'col3']  # First row is header
# Output: [1.0, 'value', datetime.date(2020, 1, 15)]
# Output: [2.0, 'another', datetime.date(2020, 2, 20)]
```

--------------------------------

### Command-Line Conversion Tool Usage

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Demonstrates the usage of the `sas7bdat_to_csv` command-line utility for converting SAS7BDAT files to CSV. Covers basic conversion, specifying output files, directing output to stdout, batch processing, displaying header info, custom delimiters, debug mode, and options to disable alignment correction or whitespace stripping.

```bash
# Basic conversion (outputs to input_file.csv)
sas7bdat_to_csv input_file.sas7bdat

# Specify output file
sas7bdat_to_csv input_file.sas7bdat output_file.csv

# Output to stdout
sas7bdat_to_csv input_file.sas7bdat -

# Batch convert multiple files
sas7bdat_to_csv file1.sas7bdat file2.sas7bdat file3.sas7bdat

# Display header information only
sas7bdat_to_csv --header input_file.sas7bdat

# Custom delimiter (tab-separated)
sas7bdat_to_csv --delimiter $'	' input_file.sas7bdat

# Debug mode with progress reporting
sas7bdat_to_csv --debug --progress-step 10000 large_file.sas7bdat

# Disable alignment correction (for problematic files)
sas7bdat_to_csv --no-align-correction problematic_file.sas7bdat

# Keep whitespace in string fields
sas7bdat_to_csv --no-strip-whitespace input_file.sas7bdat
```

--------------------------------

### Read SAS7BDAT using File Handles

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Utilize existing file handles for reading SAS7BDAT files, which is beneficial for streams or custom I/O operations. Demonstrates both manual file handle management and using context managers.

```python
from sas7bdat import SAS7BDAT
import tempfile

# Use existing file handle
with open('data.sas7bdat', 'rb') as f:
    reader = SAS7BDAT('display_name.sas7bdat', fh=f)
    for row in reader:
        print(row)
    reader.close()

# With context manager
filename = 'data.sas7bdat'
with open(filename, 'rb') as f:
    with SAS7BDAT(filename, fh=f) as reader:
        data = list(reader.readlines())
```

--------------------------------

### Read SAS7BDAT file contents

Source: https://github.com/jaredhobbs/sas7bdat/blob/master/README.md

Demonstrates how to open a SAS7BDAT file and iterate through its rows. The reader object can skip headers and returns data as a list of typed values.

```python
from sas7bdat import SAS7BDAT
with SAS7BDAT('foo.sas7bdat', skip_header=True) as reader:
    for row in reader:
        print(row)
```

--------------------------------

### Access SAS7BDAT File Properties and Header

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Retrieve metadata such as dataset name, row/column counts, creation/modification dates, SAS release, platform, endianness, and compression type. It also allows printing the full header information.

```python
from sas7bdat import SAS7BDAT

with SAS7BDAT('data.sas7bdat') as reader:
    props = reader.properties
    print(f"Dataset name: {props.name}")
    print(f"Row count: {props.row_count}")
    print(f"Column count: {props.column_count}")
    print(f"Created: {props.date_created}")
    print(f"Modified: {props.date_modified}")
    print(f"SAS release: {props.sas_release}")
    print(f"Platform: {props.platform}")
    print(f"Endianness: {props.endianess}")
    print(f"Compression: {props.compression}")

    # Print full header information
    print(reader.header)
```

--------------------------------

### Convert SAS7BDAT to pandas DataFrame

Source: https://github.com/jaredhobbs/sas7bdat/blob/master/README.md

Shows how to convert the contents of a SAS7BDAT reader object into a pandas DataFrame for data analysis.

```python
df = reader.to_data_frame()
```

--------------------------------

### Accessing SAS7BDAT Column Metadata

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Explains how to retrieve detailed metadata for each column in a SAS7BDAT file. The `columns` attribute provides access to information such as column ID, name, label, SAS format, data type ('number' or 'string'), and length in bytes.

```python
from sas7bdat import SAS7BDAT

with SAS7BDAT('data.sas7bdat') as reader:
    for col in reader.columns:
        print(f"Column {col.col_id}:")
        print(f"  Name: {col.name}")           # bytes - column name
        print(f"  Label: {col.label}")         # bytes - column label/description
        print(f"  Format: {col.format}")       # str - SAS format string
        print(f"  Type: {col.type}")           # str - 'number' or 'string'
        print(f"  Length: {col.length}")       # int - data length in bytes
# Output: Column 0:
# Output:   Name: b'intvalue'
# Output:   Label: b''
# Output:   Format: 
# Output:   Type: number
# Output:   Length: 8
```

--------------------------------

### Convert SAS7BDAT to CSV

Source: https://context7.com/jaredhobbs/sas7bdat/llms.txt

Convert SAS7BDAT files into CSV format. Supports basic conversion, custom delimiters (like tabs), progress reporting for large files, specifying output encoding, and outputting directly to standard output.

```python
from sas7bdat import SAS7BDAT

# Basic conversion
with SAS7BDAT('input.sas7bdat') as reader:
    success = reader.convert_file('output.csv')
    if success:
        print("Conversion completed successfully")

# Conversion with custom options
with SAS7BDAT('large_file.sas7bdat') as reader:
    success = reader.convert_file(
        'output.csv',
        delimiter='\t',         # Tab-separated output
        step_size=50000,        # Show progress every 50k rows
        encoding='utf-8'        # Output file encoding
    )

# Output to stdout
with SAS7BDAT('data.sas7bdat') as reader:
    reader.convert_file('-')  # Writes CSV to stdout
```

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