Use the BigQuery DataFrames data type system

The BigQuery DataFrames data type system is built upon BigQuery data types. This design ensures seamless integration and alignment with the Google Cloud data warehouse, reflecting the built-in types used for data storage in BigQuery.

Type mappings

The following table shows data type equivalents in BigQuery, BigQuery DataFrames, and other Python libraries as well as their levels of support:

Type conversions

When used with local data, BigQuery DataFrames converts data types to their corresponding BigQuery DataFrames equivalents wherever a type mapping is defined , as shown in the following example:

  import 
  
 pandas 
  
 as 
  
 pd 
 import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 s 
 = 
 pd 
 . 
 Series 
 ([ 
 pd 
 . 
 Timestamp 
 ( 
 "20250101" 
 )]) 
 assert 
 s 
 . 
 dtype 
 == 
 "datetime64[ns]" 
 assert 
 bpd 
 . 
 read_pandas 
 ( 
 s 
 ) 
 . 
 dtype 
 == 
 "timestamp[us][pyarrow]" 
 

PyArrow dictates behavior when there are discrepancies between the data type equivalents. In rare cases when the Python built-in type functions differently from its PyArrow counterpart, BigQuery DataFrames generally favors the PyArrow behavior to ensure consistency.

The following code sample uses the datetime.date + timedelta operation to show that, unlike the Python datetime library that still returns a date instance, BigQuery DataFrames follows the PyArrow behavior by returning a timestamp instance:

  import 
  
 datetime 
 import 
  
 pandas 
  
 as 
  
 pd 
 import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 s 
 = 
 pd 
 . 
 Series 
 ([ 
 datetime 
 . 
 date 
 ( 
 2025 
 , 
 1 
 , 
 1 
 )]) 
 s 
 + 
 pd 
 . 
 Timedelta 
 ( 
 hours 
 = 
 12 
 ) 
 # 0	2025-01-01 
 # dtype: object 
 bpd 
 . 
 read_pandas 
 ( 
 s 
 ) 
 + 
 pd 
 . 
 Timedelta 
 ( 
 hours 
 = 
 12 
 ) 
 # 0    2025-01-01 12:00:00 
 # dtype: timestamp[us][pyarrow] 
 

Special types

The following sections describe the special data types that BigQuery DataFrames uses.

JSON

Within BigQuery DataFrames, columns using the BigQuery JSON format (a lightweight standard) are represented by pandas.ArrowDtype . The exact underlying Arrow type depends on your library versions. Older environments typically use db_dtypes.JSONArrowType() for compatibility, which is an Arrow extension type that acts as a light wrapper around pa.string() . In contrast, newer setups (pandas 3.0 and later and PyArrow 19.0 and later) utilize the more recent pa.json_(pa.string()) representation.

timedelta

The timedelta type lacks a direct equivalent within the BigQuery native type system. To manage duration data, BigQuery DataFrames utilizes the INT64 type as the underlying storage format in BigQuery tables. You can expect the results of your computations to be consistent with the behavior you would expect from equivalent operations performed with the pandas library.

You can directly load timedelta values into BigQuery DataFrames and Series objects, as shown in the following example:

  import 
  
 pandas 
  
 as 
  
 pd 
 import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 s 
 = 
 pd 
 . 
 Series 
 ([ 
 pd 
 . 
 Timedelta 
 ( 
 "1s" 
 ), 
 pd 
 . 
 Timedelta 
 ( 
 "2m" 
 )]) 
 bpd 
 . 
 read_pandas 
 ( 
 s 
 ) 
 # 0    0 days 00:00:01 
 # 1    0 days 00:02:00 
 # dtype: duration[us][pyarrow] 
 

Unlike pandas, BigQuery DataFrames only supports timedelta values with microsecond precision. If your data includes nanoseconds, you must round them to avoid potential exceptions, as shown in the following example:

  import 
  
 pandas 
  
 as 
  
 pd 
 s 
 = 
 pd 
 . 
 Series 
 ([ 
 pd 
 . 
 Timedelta 
 ( 
 "999ns" 
 )]) 
 bpd 
 . 
 read_pandas 
 ( 
 s 
 . 
 dt 
 . 
 round 
 ( 
 "us" 
 )) 
 # 0    0 days 00:00:00.000001 
 # dtype: duration[us][pyarrow] 
 

You can use the bigframes.pandas.to_timedelta function to cast a BigQuery DataFrames Series object to the timedelta type, as shown in the following example:

  import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 bpd 
 . 
 to_timedelta 
 ([ 
 1 
 , 
 2 
 , 
 3 
 ], 
 unit 
 = 
 "s" 
 ) 
 # 0    0 days 00:00:01 
 # 1    0 days 00:00:02 
 # 2    0 days 00:00:03 
 # dtype: duration[us][pyarrow] 
 

When you load data containing timedelta values to a BigQuery table, the values are converted to microseconds and stored in INT64 columns. To preserve the type information, BigQuery DataFrames appends the #microseconds string to the descriptions of these columns. Some operations, such as SQL query executions and UDF invocations, don't preserve column descriptions, and the timedelta type information is lost after these operations are completed.

Tools for composite types

For certain composite types, BigQuery DataFrames provides tools that let you access and process the elemental values within those types.

List accessor

The ListAccessor object can help you perform operations on each list element by using the list property of the Series object, as shown in the following example:

  import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 s 
 = 
 bpd 
 . 
 Series 
 ([[ 
 1 
 , 
 2 
 , 
 3 
 ], 
 [ 
 4 
 , 
 5 
 ], 
 [ 
 6 
 ]]) 
 # dtype: list<item: int64>[pyarrow] 
 # Access the first elements of each list 
 s 
 . 
 list 
 [ 
 0 
 ] 
 # 0    1 
 # 1    4 
 # 2    6 
 # dtype: Int64 
 # Get the lengths of each list 
 s 
 . 
 list 
 . 
 len 
 () 
 # 0    3 
 # 1    2 
 # 2    1 
 # dtype: Int64 
 

Struct accessor

The StructAccessor object can access and process fields in a series of structs. The API accessor object is series.struct , as shown in the following example:

  import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 structs 
 = 
 [ 
 { 
 "id" 
 : 
 101 
 , 
 "category" 
 : 
 "A" 
 }, 
 { 
 "id" 
 : 
 102 
 , 
 "category" 
 : 
 "B" 
 }, 
 { 
 "id" 
 : 
 103 
 , 
 "category" 
 : 
 "C" 
 }, 
 ] 
 s 
 = 
 bpd 
 . 
 Series 
 ( 
 structs 
 ) 
 # Get the 'id' field of each struct 
 s 
 . 
 struct 
 . 
 field 
 ( 
 "id" 
 ) 
 # 0    101 
 # 1    102 
 # 2    103 
 # Name: id, dtype: Int64 
 

If the struct field you plan to access is unambiguous from other Series properties, you can skip calling struct , as shown in the following example:

  import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 structs 
 = 
 [ 
 { 
 "id" 
 : 
 101 
 , 
 "category" 
 : 
 "A" 
 }, 
 { 
 "id" 
 : 
 102 
 , 
 "category" 
 : 
 "B" 
 }, 
 { 
 "id" 
 : 
 103 
 , 
 "category" 
 : 
 "C" 
 }, 
 ] 
 s 
 = 
 bpd 
 . 
 Series 
 ( 
 structs 
 ) 
 # not explicitly using the "struct" property 
 s 
 . 
 id 
 # 0    101 
 # 1    102 
 # 2    103 
 # Name: id, dtype: Int64 
 

However, it's a best practice to use struct for accessing fields, because it makes your code easier to understand and less error-prone.

String accessor

You can access the StringAccessor object with the str property on a Series object, as shown in the following example:

  import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 s 
 = 
 bpd 
 . 
 Series 
 ([ 
 "abc" 
 , 
 "de" 
 , 
 "1" 
 ]) 
 # dtype: string[pyarrow] 
 # Get the first character of each string 
 s 
 . 
 str 
 [ 
 0 
 ] 
 # 0    a 
 # 1    d 
 # 2    1 
 # dtype: string 
 # Check whether there are only alphabetic characters in each string 
 s 
 . 
 str 
 . 
 isalpha 
 () 
 # 0     True 
 # 1     True 
 # 2     False 
 # dtype: boolean 
 # Cast the alphabetic characters to their upper cases for each string 
 s 
 . 
 str 
 . 
 upper 
 () 
 # 0    ABC 
 # 1     DE 
 # 2      1 
 # dtype: string 
 

Geography accessor

BigQuery DataFrames provides a GeographyAccessor object that shares similar APIs with the GeoSeries structure provided by the GeoPandas library. You can invoke the GeographyAccessor object with the geo property on a Series object, as shown in the following example:

  from 
  
 shapely.geometry 
  
 import 
 Point 
 import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 s 
 = 
 bpd 
 . 
 Series 
 ([ 
 Point 
 ( 
 1 
 , 
 0 
 ), 
 Point 
 ( 
 2 
 , 
 1 
 )]) 
 # dtype: geometry 
 s 
 . 
 geo 
 . 
 y 
 # 0    0.0 
 # 1    1.0 
 # dtype: Float64 
 

What's next

• Learn how to use BigQuery DataFrames .
• Learn about BigQuery DataFrames sessions and I/O .
• Learn how to visualize graphs using BigQuery DataFrames .
• Explore the BigQuery DataFrames API reference .