get_column_count()function

Get the number of columns in a table.

USAGE

get_column_count(data)

The get_column_count() function returns the number of columns in a table. The function works with any table that is supported by the pointblank library, including Pandas, Polars, and Ibis backend tables (e.g., DuckDB, MySQL, PostgreSQL, SQLite, Parquet, etc.). It also supports direct input of CSV files, Parquet files, and database connection strings.

Parameters

data : FrameT | Any

The table for which to get the column count, which could be a DataFrame object, an Ibis table object, a CSV file path, a Parquet file path, or a database connection string. Read the Supported Input Table Types section for details on the supported table types.

Returns

int

The number of columns in the table.

Supported Input Table Types

The data= parameter can be given any of the following table types:

  • Polars DataFrame ("polars")
  • Pandas DataFrame ("pandas")
  • DuckDB table ("duckdb")*
  • MySQL table ("mysql")*
  • PostgreSQL table ("postgresql")*
  • SQLite table ("sqlite")*
  • Microsoft SQL Server table ("mssql")*
  • Snowflake table ("snowflake")*
  • Databricks table ("databricks")*
  • PySpark table ("pyspark")*
  • BigQuery table ("bigquery")*
  • Parquet table ("parquet")*
  • CSV files (string path or pathlib.Path object with .csv extension)
  • Parquet files (string path, pathlib.Path object, glob pattern, directory with .parquet extension, or partitioned dataset)
  • Database connection strings (URI format with optional table specification)

The table types marked with an asterisk need to be prepared as Ibis tables (with type of ibis.expr.types.relations.Table). Furthermore, using get_column_count() with these types of tables requires the Ibis library (v9.5.0 or above) to be installed. If the input table is a Polars or Pandas DataFrame, the availability of Ibis is not needed.

To use a CSV file, ensure that a string or pathlib.Path object with a .csv extension is provided. The file will be automatically detected and loaded using the best available DataFrame library. The loading preference is Polars first, then Pandas as a fallback.

GitHub URLs pointing to CSV or Parquet files are automatically detected and converted to raw content URLs for downloading. The URL format should be: https://github.com/user/repo/blob/branch/path/file.csv or https://github.com/user/repo/blob/branch/path/file.parquet

Connection strings follow database URL formats and must also specify a table using the ::table_name suffix. Examples include:

"duckdb:///path/to/database.ddb::table_name"
"sqlite:///path/to/database.db::table_name"
"postgresql://user:password@localhost:5432/database::table_name"
"mysql://user:password@localhost:3306/database::table_name"
"bigquery://project/dataset::table_name"
"snowflake://user:password@account/database/schema::table_name"

When using connection strings, the Ibis library with the appropriate backend driver is required.

Examples

To get the number of columns in a table, we can use the get_column_count() function. Here’s an example using the small_table dataset (itself loaded using the load_dataset() function):

import pointblank as pb

small_table_polars = pb.load_dataset("small_table")

pb.get_column_count(small_table_polars)
8

This table is a Polars DataFrame, but the get_column_count() function works with any table supported by pointblank, including Pandas DataFrames and Ibis backend tables. Here’s an example using a DuckDB table handled by Ibis:

small_table_duckdb = pb.load_dataset("small_table", tbl_type="duckdb")

pb.get_column_count(small_table_duckdb)
8

Working with CSV Files

The get_column_count() function can directly accept CSV file paths:

# Get a path to a CSV file from the package data
csv_path = pb.get_data_path("global_sales", "csv")

pb.get_column_count(csv_path)
20

Working with Parquet Files

The function supports various Parquet input formats:

# Single Parquet file from package data
parquet_path = pb.get_data_path("nycflights", "parquet")

pb.get_column_count(parquet_path)
18

You can also use glob patterns and directories:

# Multiple Parquet files with glob patterns
pb.get_column_count("data/sales_*.parquet")

# Directory containing Parquet files
pb.get_column_count("parquet_data/")

# Partitioned Parquet dataset
pb.get_column_count("sales_data/")  # Auto-discovers partition columns

Working with Database Connection Strings

The function supports database connection strings for direct access to database tables:

# Get path to a DuckDB database file from package data
duckdb_path = pb.get_data_path("game_revenue", "duckdb")

pb.get_column_count(f"duckdb:///{duckdb_path}::game_revenue")
11

The function always returns the number of columns in the table as an integer value, which is 8 for the small_table dataset.

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