In this chapter, we will briefly show you how data types change when converting pandas-on-Spark DataFrame from/to PySpark DataFrame or pandas DataFrame.
When converting a pandas-on-Spark DataFrame from/to PySpark DataFrame, the data types are automatically casted to the appropriate type.
The example below shows how data types are casted from PySpark DataFrame to pandas-on-Spark DataFrame.
# 1. Create a PySpark DataFrame >>> sdf = spark.createDataFrame([ ... (1, Decimal(1.0), 1., 1., 1, 1, 1, datetime(2020, 10, 27), "1", True, datetime(2020, 10, 27)), ... ], 'tinyint tinyint, decimal decimal, float float, double double, integer integer, long long, short short, timestamp timestamp, string string, boolean boolean, date date') # 2. Check the PySpark data types >>> sdf DataFrame[tinyint: tinyint, decimal: decimal(10,0), float: float, double: double, integer: int, long: bigint, short: smallint, timestamp: timestamp, string: string, boolean: boolean, date: date] # 3. Convert PySpark DataFrame to pandas-on-Spark DataFrame >>> psdf = sdf.pandas_api() # 4. Check the pandas-on-Spark data types >>> psdf.dtypes tinyint int8 decimal object float float32 double float64 integer int32 long int64 short int16 timestamp datetime64[ns] string object boolean bool date object dtype: object
The example below shows how data types are casted from pandas-on-Spark DataFrame to PySpark DataFrame.
# 1. Create a pandas-on-Spark DataFrame >>> psdf = ps.DataFrame({"int8": [1], "bool": [True], "float32": [1.0], "float64": [1.0], "int32": [1], "int64": [1], "int16": [1], "datetime": [datetime.datetime(2020, 10, 27)], "object_string": ["1"], "object_decimal": [decimal.Decimal("1.1")], "object_date": [datetime.date(2020, 10, 27)]}) # 2. Type casting by using `astype` >>> psdf['int8'] = psdf['int8'].astype('int8') >>> psdf['int16'] = psdf['int16'].astype('int16') >>> psdf['int32'] = psdf['int32'].astype('int32') >>> psdf['float32'] = psdf['float32'].astype('float32') # 3. Check the pandas-on-Spark data types >>> psdf.dtypes int8 int8 bool bool float32 float32 float64 float64 int32 int32 int64 int64 int16 int16 datetime datetime64[ns] object_string object object_decimal object object_date object dtype: object # 4. Convert pandas-on-Spark DataFrame to PySpark DataFrame >>> sdf = psdf.to_spark() # 5. Check the PySpark data types >>> sdf DataFrame[int8: tinyint, bool: boolean, float32: float, float64: double, int32: int, int64: bigint, int16: smallint, datetime: timestamp, object_string: string, object_decimal: decimal(2,1), object_date: date]
When converting pandas-on-Spark DataFrame to pandas DataFrame, the data types are basically the same as pandas.
# Convert pandas-on-Spark DataFrame to pandas DataFrame >>> pdf = psdf.to_pandas() # Check the pandas data types >>> pdf.dtypes int8 int8 bool bool float32 float32 float64 float64 int32 int32 int64 int64 int16 int16 datetime datetime64[ns] object_string object object_decimal object object_date object dtype: object
However, there are several data types only provided by pandas.
# pd.Catrgorical type is not supported in pandas API on Spark yet. >>> ps.Series([pd.Categorical([1, 2, 3])]) Traceback (most recent call last): ... pyarrow.lib.ArrowInvalid: Could not convert [1, 2, 3] Categories (3, int64): [1, 2, 3] with type Categorical: did not recognize Python value type when inferring an Arrow data type
These kinds of pandas specific data types below are not currently supported in the pandas API on Spark but planned to be supported.
pd.Timedelta
pd.Categorical
pd.CategoricalDtype
The pandas specific data types below are not planned to be supported in the pandas API on Spark yet.
pd.SparseDtype
pd.DatetimeTZDtype
pd.UInt*Dtype
pd.BooleanDtype
pd.StringDtype
The table below shows which NumPy data types are matched to which PySpark data types internally in the pandas API on Spark.
NumPy
PySpark
np.character
BinaryType
np.bytes_
np.string_
np.int8
ByteType
np.byte
np.int16
ShortType
np.int32
IntegerType
np.int64
LongType
np.float32
FloatType
np.float64
DoubleType
np.unicode_
StringType
np.datetime64
TimestampType
np.ndarray
ArrayType(StringType())
The table below shows which Python data types are matched to which PySpark data types internally in pandas API on Spark.
Python
bytes
int
float
str
bool
BooleanType
datetime.datetime
datetime.date
DateType
decimal.Decimal
DecimalType(38, 18)
For decimal type, pandas API on Spark uses Spark’s system default precision and scale.
You can check this mapping by using the as_spark_type function.
>>> import typing >>> import numpy as np >>> from pyspark.pandas.typedef import as_spark_type >>> as_spark_type(int) LongType >>> as_spark_type(np.int32) IntegerType >>> as_spark_type(typing.List[float]) ArrayType(DoubleType,true)
You can also check the underlying PySpark data type of Series or schema of DataFrame by using Spark accessor.
>>> ps.Series([0.3, 0.1, 0.8]).spark.data_type DoubleType >>> ps.Series(["welcome", "to", "pandas-on-Spark"]).spark.data_type StringType >>> ps.Series([[False, True, False]]).spark.data_type ArrayType(BooleanType,true) >>> ps.DataFrame({"d": [0.3, 0.1, 0.8], "s": ["welcome", "to", "pandas-on-Spark"], "b": [False, True, False]}).spark.print_schema() root |-- d: double (nullable = false) |-- s: string (nullable = false) |-- b: boolean (nullable = false)
Note
Pandas API on Spark currently does not support multiple types of data in a single column.
>>> ps.Series([1, "A"]) Traceback (most recent call last): ... TypeError: an integer is required (got type str)