Pyarrow datetime64 If you install PySpark using pip, then PyArrow can be brought in as an extra dependency of the SQL module with the command pip install pyspark[sql]. date64` type. g. dtype of a arrays. pyarrow. set("spark. read_csv # Get the date column array = table['my_date_column']. The . Arrow to NumPy#. This is the code import pyarrow. Concrete class for time64 data types. This blog will demonstrate how to load data from an Iceberg table into PyArrow or DuckDB using PyIceberg. string# pyarrow. Create an instance of time64 type: >>> arrays list of pyarrow. 0 scipy : None snappy : None Astype Bug Needs Triage Issue that has not been reviewed by a pandas team member Non-Nano datetime64/timedelta64 with non-nanosecond resolution. I want to convert This is sadly a known limitation of the type inferrence from empty DataFrames. 0 The arrays. Possibly due to some of the depreciated types in NumPy. ArrowExtensionArray is backed by a pyarrow. exe prompt, Write pip install pyarrow. pojo. ArrowType$Timestamp. date or list objects when filled. These get converted to dateTime64[ns], witch has a limited date range which it can hold. Supported time unit resolutions are ‘us’ [microsecond] and ‘ns’ [nanosecond]. timestamp() Since the # values are normalized to `numpy. commit : bb1f651 python : 3. ------- typ: The following are 3 code examples of pyarrow. What did you expect to see? I expected the datetime data written to the database verbatim with nanosecond precision. 11. To use Apache Arrow in PySpark, the recommended version of PyArrow should be installed. spark. The number of child fields. date32() to pandas, and would like these date to be converted to datetime64[ns]. Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics - apache/arrow If False, convert to datetime64[ns] dtype. If you do not have PyArrow installed, you do not need to install PyArrow yourself; installing the Python Connector as documented below automatically installs the appropriate version of PyArrow. Then, we create pyarrow Tables directly from these arrays, avoiding the use of pandas altogether. I have the following pandas dataframe object using the pyarrow back end: crsp_m. 20. For example, 2020-07-09 04:23:50. types. If False, convert to datetime64 dtype with the equivalent time unit (if supported). Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number of features from other Python libraries like scikits. NaT, pd. Modified 2 years, 4 months ago. Bases: FileSystem HDFS backed FileSystem implementation. parquet') What did you do? I invoked snowflake. Parameters: obj ndarray, pandas. Each data type is an instance of this class. head of DAT_RUN DAT_FORECAST LIB_SOURCE MES_LONGITUDE MES_LATITUDE MES_TEMPERATURE MES_HUMIDITE MES_PLUIE See pyarrow. pyspark. args See pyarrow. import pyarrow. DataFrame See pyarrow. ArrowInvalid: Floating point value truncated' ERROR when use toPandas() on a DataFrame in pyspark 4 PyArrow Table to PySpark Dataframe conversion See pyarrow. unit str, default ‘ns’. A function mapping a pyarrow DataType to a pandas ExtensionDtype. I ran into the same issue and I think I was able to solve it using the following: import pandas as pd import pyarrow as pa import pyarrow. e. If not passed, schema must be passed. ISSUE FACING: See pyarrow. from_batches (batches) # Ensure only the table has a reference to the batches, so that # self_destruct (if enabled) is effective del batches # Pandas DataFrame created from PyArrow uses datetime64[ns] for date type # values, but we should use datetime. Expected Behavior. id-column AS id-column, workspace. Time64Type# class pyarrow. Since the # values are normalized to `numpy. 267000+00:00 to 2020-07-09 04:23:50 Can anyone explain me what is the meaning of this 2020-07-09T04:23:50. Should be possible to read columns of dtype interval[datetime64] either from parquet or from pyarrow table which where created with pandas in the first place. ArrowTypeError: Expected a string or bytes dtype, got int64 The code and data looks normal. execution. write_table(table, 'file_name. I want to convert the above datetime64[ns, UTC] format to normal datetime. DatetimeGregorian timestamps to datetime64[ms] and create pyarrow Arrays. 19. Modified 5 years, 3 months ago. vector. Pyarrow provides similar array and data type support as NumPy including first-class nullability support for all data types, immutability and more. paraquet') could in-place type coercion or promotion be applied with a warning to prevent this? I think these errors arise because the pyarrow. combine_chunks() # Replace string ending with I'm trying to store a timestamp with all the other data in my dataframe, signifying the time the data was stored to disk, in a Parquet file. from_pandas` with a `pandas. nan, pd. arrow. HadoopFileSystem (unicode host, int port=8020, unicode user=None, *, int replication=3, int buffer_size=0, default_block_size=None, kerb_ticket=None, extra_conf=None) #. arrays list of pyarrow. Either update your pyarrow version or downgrade to numpy<1. date BUG: is_datetime64_any_dtype returns False for Series with pyarrow dtype #57055. read_table( source = Table. The code is publicly available on the docker-spark-iceberg Github repository that contains a full docker-compose stack of Apache Spark with Iceberg support, MinIO as a storage backend, and a REST catalog as a catalog. parquet as pq import pandas as pd df1 = pd. What is happening here is that reading the Parquet file with pyarrow results in a pyarrow. timestamp("ns")` if column_type == pa. 0 and 13. Number of data buffers required to construct Array type excluding children. read_csv to support this. timestamp_as_object bool, default False. Thus we are unable to guess the See pyarrow. Thus, missing data can cause problems, especially with data types, such as integers, for which there is no missing data designation. num_fields. Improve this answer. date is not a supported dtype in pandas, so any column/Series storing them becomes object dtype, which won't do if a function expects datetime64[D] or datetime. Time64Type # Bases: DataType. timestamp() type with and without timezone Problem description This was the first mention of numpy. Names for the table columns. This will be based off the origin. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links When trying to call `pyarrow. date32# pyarrow. Schema` provided, the function call results in a segmentation fault if Pandas `datetime64 [ns] ` column tries to be converted to a `pyarrow. Series, array-like If False, convert to datetime64 dtype with the equivalent time unit (if supported). (np. Disable PyArrow for Conversion (Fallback to Legacy Conversion) Disable PyArrow during the conversion. """ Pandas Data Types for SQL systems (BigQuery, Spanner) """ import datetime import re from typing import Optional, Union import warnings import numpy import packaging. Returns. Parameters: unit str. ArrowInvalid: Cannot locate timezone 'UTC': Unable to get Timezone database version from C:\\Users\\Nick\\ The table in this section of the data types API docs reads to me as implying that a pyarrow tz-aware timestamp dtype should map to a numpy datetime64 dtype. flight. CHAPTER 4 File interfaces and Memory Maps PyArrow features a number of file-like interfaces Hadoop File System (HDFS) A Series, Index, or the columns of a DataFrame can be directly backed by a pyarrow. HDFS host to connect to. DataFrame` and a `pyarrow. FechaHora datetime64[ns] Fecha object Hora object Entrada object Grupo1 object HourlyCountOne int64 HourlyCountTwo int64 Variacion int64 sumToAforo bool DiaSemana object Tipo object NDiaSemana int64 This When I try to convert the pyspark dataframe to pandas I get the error: TypeError: Casting to unit-less dtype 'datetime64' is not supported. The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. ChunkedArray. "int64[pyarrow]"" into the dtype parameter Arrow pyarrow functionality Bug good first issue IO CSV read_csv, to_csv Missing-data np. 2 datetime. Viewed 4k times 1 . com this lack of numpy datetime conversion is the reason I always have numpy -> pyarrow -> polars conversion chains instead of simple pl. TypeError: dtype datetime64[ns] cannot be converted to timedelta64[ns] Ask Question Asked 2 years, 4 months ago. Examples See pyarrow. For some reason, when I call the pandas udf before writing the See pyarrow. datetime64) to Ensure PyArrow Installed¶. date32 DataType(date32[day]) Create a scalar with 32-bit date type: pyarrow. You can test this with a nightly build if you want. parquet as pq chunksize=10000 # this is the number of lines pqwriter = None for i, df in enumerate(pd. Parameters: target_type DataType, default None. Return datetime objects as expected. time64 (unit) # Create instance of 64-bit time (time of day) type with unit resolution. astype(schema) before saving the file to Parquet. I have a column of years from the sunspots dataset. apache. Table. Consider this example: from datetime import datetime, date import pyarrow. datetime64) to objects. from_pandas(df_image_0) STEP-2: Now, write the data in paraquet format. I am observing a related but separate issue where the the frequency type of DateTimeIndex is not preserved in round trip from pandas to table. If one class has a time zone and the other does not, direct comparison is not possible. BUG: is_datetime64_any_dtype returns False for Series with pyarrow dtype #57055. Installed Versions INSTALLED VERSIONS. 000 2 2020-02-02 00:35:31. Asking for help, clarification, or responding to other answers. . Type to cast scalar to. 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. dt. Create an instance of 32-bit date type: >>> import pyarrow as pa >>> pa. Set to “default” for Consider pandas' datetime64[ns UTC] to be an extension of numpy's datetime (datetime64[]), that additionally allows to handle time zones. To construct these from the main pandas data structures, you can pass in a string of the type followed by [pyarrow], e. The code is publicly available on the docker-spark-iceberg Github repository that contains a full docker-compose timedelta64 and datetime64 data are stored internally as 8-byte ints (dtype '<i8'). How do I get rid of it? from pyarrow import Table import pandas as pd df_empty = pd. conf. The underlying problem here is that Pandas represents a datetime with nanoseconds since 1970. If True, require an exact format match. to_pandas() # Could eventually be phased out index_dct = dict ( zip (df[column]. I'm not too familiar with the java API, but you may Pandas Timestamps use the datetime64[ns] type in Pandas and are converted to an Arrow TimestampArray. We should certainly do so if the pandas metadata indicates which resolution was originally used See pyarrow. today()]}) df1. STEP-1: Convert the pandas dataframe into pyarrow table with following line of code. I have a panda dataframe df: &lt;bound method NDFrame. datetime64[ns]) 10 Chapter 3. This is limited to primitive types for which NumPy has the same physical representation as Arrow, and assuming the Arrow data has no See pyarrow. to_pydatetime() on a date series s. In the reverse direction, it is possible to produce a view of an Arrow Array for use with NumPy using the to_numpy() method. date32 # Create instance of 32-bit date (days since UNIX epoch 1970-01-01). datetime64[ns]` anyways, we do not care about this # and load the column type as `pa. compute. schema = { 'id': 'int64', 'date': 'datetime64[ns]', 'name': 'object', 'status': 'category', } Otherwise, I will make the dtype schema, print it out and paste it into a file, make any required corrections, and then do a df = df. This was working, but now I call a pandas udf before writing the data to bigquery. ChunkedArray with a pyarrow. timestamp_as_object (bool, default False) – Cast non-nanosecond timestamps (np. In Arrow, you can represent these dates by using a more granular representation like milliseconds-since-1970 but on the In pandas, however, the dataframes are identical with both having the same dtypes: datetime64[ns] 1 2020-02-02 00:35:28. Normally I'd just store the timestamp within the pandas dataframe itself, but pyarrow doesn't like pandas' way of storing timestamps and complains that it will lose precision converting from nanoseconds to microseconds when I run See pyarrow. The following example demonstrates the implemented functionality by doing a round trip: pandas data frame -> parquet file -> pandas data frame. compute from db_dtypes import core from db_dtypes. Table with a string column that consists of multiple chunks. ArrowInvalid: offset overflow while concatenating arrays Sep 4, 2023 I've been trying to read and subset a parquet file using pyarrow read_table. DataFrame({'date': [date. According to this Jira issue, reading and writing nested Parquet data with a mix of struct and list nesting levels was implemented in version 2. The following are 30 code examples of pyarrow. Otherwise, you must ensure that PyArrow is installed and available on all cluster nodes. Even if you use pandas datetime consistently, either both datetime Series have to have a tz defined (be "tz-aware") or both have no tz For context, I was feeding a pandas DataFrame with pyarrow dtypes into the dash package, which called s. Solution. Nanosecond timestamps as written by pyarrow's default is quite new and probably not understood correctly by contentid object processed_time datetime64[ns] access_time datetime64 compression=None, engine='pyarrow', allow_truncated_timestamps=True, use_deprecated_int96_timestamps=True) Share. This column could also contain datetime. Note that you need NumPy version 1. pandas. If False, all timestamps are converted to datetime64[ns If I create a pandas df and convert that to a pyarrow Table, I got an additional column 'index_level_0'. A I am writing a spark dataframe to a bigquery table. Convert pandas-on-Spark DataFrame to PySpark DataFrame >>> sdf = psdf. safe bool, default True. enabled", "false") I'm not sure you'll be able to get pyarrow. Projects None yet Milestone No milestone Development No branches or pull requests. from_pandas(df) # for the first chunk of records if i == Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. version import See pyarrow. parquet as pq s3_uri = &quot;Path to s3&quot; fp = pq. equals (self, other, *[, check_metadata]). Parameters. Returns: type pyarrow. Create an instance of a string type: >>> import pyarrow as pa >>> pa. – Im trying to use the method pyarrow. In this example the Pandas Timestamp is time zone aware (UTC on this I converted a pandas df (two versions: one with datetime64 [ns] and another with datetime64 [us]) to parquet bytes using both pyarrow 12. 8. If False, all timestamps are converted to datetime64[ns] dtype. write_pandas on a DataFrame with a column of type datetime64[ns] (using PyArrow as the default backend for ParquetWriter). Share. 253402214400000000 micro seconds from epoch is the year 10`000. Using these data types (in particular string[pyarrow]) can lead to large performance improvements in terms of memory usage and computation wall time. Equal-length arrays that should form the table. string # Create UTF8 variable-length string type. api. I was able to get it to upload timestamps by changing all instances of bit_width. All reactions. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or None if the default conversion should be used for that type. Schema` provided, the function call results in a segmentation fault if Pandas Redshift spectrum incorrectly parsing Pyarrow datetime64[ns] Ask Question Asked 5 years, 3 months ago. pandas_options See pyarrow. NA, dropna, isnull, interpolate Needs Tests Unit test(s) needed to prevent regressions Projects Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company See pyarrow. 0, only datetime64[ns] conversion is supported. to_pandas(timestamp_as_object=True) to avoid trying to convert to pandas' nanosecond-resolution timestamps. You signed in with another tab or window. Create an instance of 64-bit date type: >>> Convert a JVM timestamp type to its Python equivalent. HadoopFileSystem# class pyarrow. The query looks like this: SELECT workspace. pyarrow : 6. Solution ideas One alternative solution to the to_gbq() method is to use google cloud's bigquery package. This is useful if you have timestamps that don’t fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). pandas contains extensive capabilities and features for working with time series data for all domains. sql. jmoralez opened this issue Jan 24, 2024 · 1 comment · Fixed by #57060. final. metadata dict or Mapping, default None See pyarrow. Return true if type is equivalent to passed value. field (self, i). options CastOptions, default None. Reload to refresh your session. 5 See pyarrow. Array or pyarrow. 0 should support parsing time strings in CSV to time32. connector. Parameters:. date64(). You signed out in another tab or window. Follow answered Sep 22, 2020 at 7:14. assume_timezone but i get the error: pyarrow. Parquet as well as Pandas. I did a bit more research and pypi_0 just means the package was installed via pip. 2 participants Now cast the cftime. , the nullability, or lack thereof, of data types. next. A temporary fix for me seemed to be fixing numpy==1. table = pa. If False, convert to datetime64[ns] dtype. Few libraries support this range for timestamps. csv', chunksize=chunksize)): table = pa. datetime64[ns]) The problem is pandas/pyarrow can not deal with the timestamps. DataType #. csv import pyarrow as pa import pyarrow as pc table = pyarrow. core. 267Z representation and also how to convert this into datetime object? 1 Install PyArrow 3 TIMESTAMP(unit=*) pd. Control how format is used:. to_pandas See pyarrow. date64# pyarrow. I'm using Pyarrow, Pyarrow. I am working with parquet and I need to use date32[day] objects for my dates but I am unclear how to use pandas to generate this exact datatype, rather than a timestamp. lib. If the input series is not a timestamp series, then the same series is returned. timestamp("us"): column_type = pa. Table to a pandas DataFrame without running into the out of bounds issue, you can use . num_buffers. timestamp("ns") df = _fix_pyarrow_07992_table (table). If False, allow the format to match anywhere in the target string. cast() for usage. Nguyễn Văn Thưởng Nguyễn Văn Thưởng. Examples. Whether to check for conversion errors such as overflow. csv. Closed 3 tasks done. 000 Therefore, I believe the problem is rooted in the way fastparquet and pyarrow deal with datetime values and subsequently how snowflake reads the parquet files. pandas_tools. Provide details and share your research! But avoid . One of ‘us’ [microsecond], or ‘ns’ [nanosecond]. You have a few options: Truncate all the values that are out of range before converting to arrow/parquet See pyarrow. int64 DataType(int64) Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company pyarrow. exact bool, default True. date-column AS If False, convert to datetime64 dtype with the equivalent time unit (if supported). 6. datetime64[ns]) TIMESTAMP_NTZ, TIMESTAMP_LTZ, TIMESTAMP_TZ. from_numpy in my pipeline. to_spark # 5. 7 or newer to work with datetime64/timedelta64s. __init__ (*args, **kwargs). names list of str, optional. FlightTimedOutError. date to match the behavior with when # Arrow optimization is disabled. This blog shows how you can load data from an Iceberg table into PyArrow or DuckDB using PyIceberg. 1 Supported Environments C/GLib C++ Getting Started Using Arrow C++ in your own project You signed in with another tab or window. metadata dict or Mapping, default None I tried to install pyarrow in command prompt with the command 'pip install pyarrow', but it didn't work for me. Cast non-nanosecond timestamps (np. info(verbose = True) out: <class 'pandas. Check the pandas-on-Spark data types >>> psdf. aihao2000 changed the title pyarrow. When users have pandas >= 2, we should support converting with preserving the resolution. I then converted back I'm trying to convert arrow data of type pyarrow. DataFrame'> RangeIndex: 4921811 entries, 0 to 4921810 Data columns (total 87 columns): # Column Dtype --- ----- ----- 0 permno int64[pyarrow] 1 secinfostartdt date32[day][pyarrow] 2 secinfoenddt date32[day][pyarrow] 3 Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing - apache/arrow Well, the good news is that pyarrow 6. time64# pyarrow. Uwe L. When trying to call `pyarrow. 1 pyreadstat : None pyxlsb : None s3fs : 0. You switched accounts on another tab or window. The mentioned PR changed our conversion to first concatenate those chunks on the pyarrow side, and only then convert to an numpy array and put in a pandas string column. 857 3 2020-02-02 00:35:21. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company Visit the blog I experienced a similar problem while using pd. astype("datetime64[ms]") did not work for me (pandas v. 2. What did you see instead? See pyarrow. Table. And if you want to convert the pyarrow. read_csv('sample. So some timestamps (especially all high water marks) get wrong entries. add_note() FlightServerError. Time64Type. However you can load the date column as strings and convert it later using pyarrow. extensions from pandas. Mysterious 'pyarrow. frame. ChunkedArray which is similar to a NumPy array. Instance of int64 type: >>> import pyarrow as pa >>> pa. 5, pandas added support for using pyarrow-backed extension data dtypes. 8,776 1 1 gold badge 35 35 silver badges 45 45 bronze badges. These columns in Pandas DataFrames are from type object, not str. So the above views the timedelta64s as 8-byte ints and then does integer division to convert nanoseconds to seconds. timeseries as well as created a tremendous amount of new functionality for unique_key object complaint_description object source object status object status_change_date datetime64[us, UTC] created_date datetime64[us, UTC] last_update_date datetime64[us, UTC] close_date datetime64[us, UTC] incident_address object street_number object street_name object city object incident_zip Int64 county object state_plane_x_coordinate object See pyarrow. Parameters: host str. ---------- jvm_type: org. pq. array for more general conversion from arrays or sequences to Arrow arrays. fs. 0. If not passed, names must be passed. timeseries as well as created a tremendous amount of new functionality for pyarrow. ArrowInvalid: offset overflow while concatenating arrays When calling load_dataset, raise error: pyarrow. memory_pool MemoryPool, optional. Bit width for fixed width type. Cannot be used alongside format='ISO8601' or format='mixed'. It does not See pyarrow. See pyarrow. to_parquet, my final workaround was to use the argument engine='fastparquet', but I realize this doesn't help if you need to use PyArrow specifically. errors import OutOfBoundsDatetime import pyarrow import pyarrow. This question gets at one of the nastier problems in Pandas and Dask, i. datetime64 I had seen so I'm not sure how common its usage is? From https://stackoverflow. Things I tried which did not work: @DrDeadKnee's workaround of manually casting columns . Schema for the created table. Korn Uwe L. timestamp("ns") df = _fix_pyarrow_07992_table(table). I w This is because we have a coerce_temporal_nanoseconds conversion option which we hardcode to True (for top-level columns, we hardcode it to False for nested data). FlightServerError. To do so I provide a types_mapper argument: Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone. Maybe the problem is dash's fault, but this seems like a pandas bug. astype('datetime64[ns]') Upon checking the dtype, I can confirm that the datatype has been converted to datetime64[ns]. Otherwise you'll have to parse it yourself with something else. parquet module used by the BigQuery library does convert Python's built in datetime or time types into something that BigQuery recognises by default, but the BigQuery library does have its own method for converting pandas types. Pandas Interface. DataType instead of a NumPy array and data type. 0 I used the following code to convert my 'Date' from Object to datetime64: df. When I send a Pandas datetime64[ns] series to a Parquet file and load it again via a drill query, the query shows an Integer like: 1467331200000000 which seems to be something else than a UNIX timestamp. I got the message; Installing collected packages: pyarrow Successfully installed pyarrow-10. values, (list (x) for x in 1 As @unutbu mentions, pandas only supports datetime64 in nanosecond resolution, so datetime64[D] in a numpy array becomes datetime64[ns] when stored in a pandas column. ArrowExtensionArray is an ArrowDtype . Follow answered May 12, 2021 at 12:05. datetime64[ns]) DATE pd. While the schema of the bigquery table and the local df are the same, appending to the BigQuery table can be accomplished with the following code: Cast dates to objects. Time series / date functionality#. Note: in pandas version < 2. On this page FlightServerError. date64 # Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01). version import pandas import pandas. Timestamp(np. The arrays. Additional checks pass by CastOptions. Date= df['Date']. DataType# class pyarrow. schema Schema, default None. I would definitely defer to someone else's judgement on whether that is correct, or if there should be a distinction in that table linked between a pa. to_parquet('testdates. Cast dates to objects. Bases: _Weakrefable Base class of all Arrow data types. This is a companion piece to our In pandas=1. string DataType(string) and use the string type to create an array: Converting from NumPy supports a wide range of input dtypes, including structured dtypes or strings. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of Timestamp in arrow are represented as the number of nanos (if you use nano as the unit) since epoch, as an integer 64. 10. Korn. The pyarrow package you had installed did not come from conda-forge and it does not appear to match the package on PYPI. The time around the year 2700 is simply the limitation that there the number of nanoseconds-since-1970 exceeds the space that can be represented with an int64. to_pandas I don't know what the exact cause of the issue is, but it appears to cause an incompatibility within pyarrow. This has worked: Open the Anaconda Navigator, launch CMD. Casting from time32 to time64 should be doable. id. hqxqi mxqj isxv wee jdeignz anbkvx qoyfkg tqasbak uxhlqp bgmf