Description

I've rewritten the current read_gbq() function using google-cloud-python, which handles the naming of structs and arrays out of the box. For more discussion about this, see: https://github.com/pydata/pandas-gbq/issues/23.

~However, because of the fact that google-cloud-python potentially uses different authentication flows and may break existing behavior, I've left the existing read_gbq() function and and named this new function from_gbq(). If in the future we are able to reconcile the authentication flows and/or decide to deprecate flows that are not supported in google-cloud-python, we can rename this to read_gbq().~

UPDATE: As requested in comment by @jreback (https://github.com/pydata/pandas-gbq/pull/25/files/a763cf071813c836b7e00ae40ccf14e93e8fd72b#r110518161), I deleted old read_gbq() and named my new function read_gbq(), deleting all legacy functions and code.

Added in a few lines to requirements file, but I'll leave it to you @jreback to deal with conda dependency issues which you mentioned in Issue 23.

Let know if any questions or if any tests need to be written. You can confirm that it works by running the following:

q = """
select ROW_NUMBER() over () row_num, struct(a,b) col, c, d, c*d c_times_d, e
from
(select * from
    (SELECT 1 a, 2 b, null c, 0 d, 100 e)
    UNION ALL
    (SELECT 5 a, 6 b, 0 c, null d, 200 e)
    UNION ALL
    (SELECT 8 a, 9 b, 10.0 c, 10 d, 300 e)
)
"""
df = gbq.read_gbq(q, dialect='standard')
df

| row_num | col | c | d | c_times_d | e | |---------|--------------------|------|------|-----------|-----| | 2 | {u'a': 5, u'b': 6} | 0.0 | NaN | NaN | 200 | | 1 | {u'a': 1, u'b': 2} | NaN | 0.0 | NaN | 100 | | 3 | {u'a': 8, u'b': 9} | 10.0 | 10.0 | 100.0 | 300 |

q = """
select array_agg(a) mylist
from
(select "1" a UNION ALL select "2" a)
"""
df = gbq.read_gbq(q, dialect='standard')
df

| mylist | |--------| | [1, 2] |

q = """
select array_agg(struct(a,b)) col, f
from
(select * from
    (SELECT 1 a, 2 b, null c, 0 d, 100 e, "hello" f)
    UNION ALL
    (SELECT 5 a, 6 b, 0 c, null d, 200 e, "ok" f)
    UNION ALL
    (SELECT 8 a, 9 b, 10.0 c, 10 d, 300 e, "ok" f)
)
group by f
"""
df = gbq.read_gbq(q, dialect='standard')
df

| col | f | |------------------------------------------|-------| | [{u'a': 5, u'b': 6}, {u'a': 8, u'b': 9}] | ok | | [{u'a': 1, u'b': 2}] | hello |

Confirmed that col_order and index_col still work ~(feel free to pull that out into a separate function since there's now redundant code with read_gbq())~, and I removed the type conversion lines which appear to be unnecessary (google-cloud-python and/or pandas appears to do the necessary type conversion automatically, even if there are nulls; can confirm by examining the datatypes in the resulting dataframes).

We're starting to use BigQuery heavily but becoming increasingly 'bottlenecked' with the performance of moving moderate amounts of data from BigQuery to python.

Here's a few stats:

• 29.1s: Pulling 500k rows with 3 columns of data (with cached data) using pandas-gbq
• 36.5s: Pulling the same query with google-cloud-bigquery - i.e. client.query(query)..to_dataframe()
• 2.4s: Pulling very similar data - same types, same size, from our existing MSSQL box hosted in AWS (using pd.read_sql). That's on standard drivers, nothing like turbodbc involved

...so using BigQuery with python is at least an order of magnitude slower than traditional DBs.

We've tried exporting tables to CSV on GCS and reading those in, which works fairly well for data processes, though not for exploration.

A few questions - feel free to jump in with partial replies:

• Are these results expected, or are we doing something very wrong?
• My prior is that a lot of this slowdown is caused by pulling in HTTP pages, converting to python objects, and then writing those into arrays. Is this approach really scalable? Should pandas-gbq invest resources into getting a format that's query-able in exploratory workflows that can deal with more reasonable datasets? (or at least encourage Google to)

Remove the use of oauth2client and use google-auth library, instead.

Rather than check for multiple versions of the libraries, use the setup.py to specify compatible versions. I believe this is safe since Pandas checks for the pandas_gbq package.

Since google-auth does not use the argparse module to override user authentication flow settings, add a parameter to choose between the web and console flow.

Closes https://github.com/pydata/pandas-gbq/issues/37.

Hi, I'm using Heroku to run a python based ETL process where I'm pushing the contents of a Pandas dataframe into Google BQ using to_gbq. However, it's generating a UnicodeEncodeError with the following stack trace, due to some non-latin characters.

Strangely this works fine on my Mac but when I try to run it on Heroku, it's failing. It seems that for some reason, http.client.py is getting an un-encoded string rather than bytes and therefore, it's trying to encode with latin-1, which is the default but obviously would choke on anything non-latin, like Chinese chars.

2018-01-08T04:54:17.307496+00:00 app[run.2251]: Load is 100.0% Complete044+00:00 app[run.2251]: 2018-01-08T04:54:20.443238+00:00 app[run.2251]: Traceback (most recent call last): 2018-01-08T04:54:20.443267+00:00 app[run.2251]: File "AllCostAndRev.py", line 534, in 2018-01-08T04:54:20.443708+00:00 app[run.2251]: main(yaml.dump(data=ads_dict)) 2018-01-08T04:54:20.443710+00:00 app[run.2251]: File "AllCostAndRev.py", line 475, in main 2018-01-08T04:54:20.443915+00:00 app[run.2251]: private_key=environ['skynet_bq_pk'] 2018-01-08T04:54:20.443917+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/pandas_gbq/gbq.py", line 989, in to_gbq 2018-01-08T04:54:20.444390+00:00 app[run.2251]: connector.load_data(dataframe, dataset_id, table_id, chunksize) 2018-01-08T04:54:20.444391+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/pandas_gbq/gbq.py", line 590, in load_data 2018-01-08T04:54:20.444653+00:00 app[run.2251]: job_config=job_config).result() 2018-01-08T04:54:20.444656+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/google/cloud/bigquery/client.py", line 748, in load_table_from_file 2018-01-08T04:54:20.445248+00:00 app[run.2251]: response = upload.transmit_next_chunk(transport) 2018-01-08T04:54:20.445250+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/google/resumable_media/requests/upload.py", line 395, in transmit_next_chunk 2018-01-08T04:54:20.444942+00:00 app[run.2251]: file_obj, job_resource, num_retries) 2018-01-08T04:54:20.445457+00:00 app[run.2251]: retry_strategy=self._retry_strategy) 2018-01-08T04:54:20.444943+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/google/cloud/bigquery/client.py", line 777, in _do_resumable_upload 2018-01-08T04:54:20.445458+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/google/resumable_media/requests/_helpers.py", line 101, in http_request 2018-01-08T04:54:20.445592+00:00 app[run.2251]: func, RequestsMixin._get_status_code, retry_strategy) 2018-01-08T04:54:20.445594+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/google/resumable_media/_helpers.py", line 146, in wait_and_retry 2018-01-08T04:54:20.445725+00:00 app[run.2251]: response = func() 2018-01-08T04:54:20.445726+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/google/auth/transport/requests.py", line 186, in request 2018-01-08T04:54:20.445866+00:00 app[run.2251]: method, url, data=data, headers=request_headers, **kwargs) 2018-01-08T04:54:20.445867+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/requests/sessions.py", line 508, in request 2018-01-08T04:54:20.446099+00:00 app[run.2251]: resp = self.send(prep, **send_kwargs) 2018-01-08T04:54:20.446101+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/requests/sessions.py", line 618, in send 2018-01-08T04:54:20.446456+00:00 app[run.2251]: r = adapter.send(request, **kwargs) 2018-01-08T04:54:20.446457+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/requests/adapters.py", line 440, in send 2018-01-08T04:54:20.446728+00:00 app[run.2251]: timeout=timeout 2018-01-08T04:54:20.446730+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/urllib3/connectionpool.py", line 601, in urlopen 2018-01-08T04:54:20.446969+00:00 app[run.2251]: chunked=chunked) 2018-01-08T04:54:20.446970+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/site-packages/urllib3/connectionpool.py", line 357, in _make_request 2018-01-08T04:54:20.447229+00:00 app[run.2251]: conn.request(method, url, **httplib_request_kw) 2018-01-08T04:54:20.447231+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/http/client.py", line 1239, in request 2018-01-08T04:54:20.447690+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/http/client.py", line 1284, in _send_request 2018-01-08T04:54:20.448232+00:00 app[run.2251]: body = _encode(body, 'body') 2018-01-08T04:54:20.448234+00:00 app[run.2251]: File "/app/.heroku/python/lib/python3.6/http/client.py", line 161, in _encode 2018-01-08T04:54:20.448405+00:00 app[run.2251]: UnicodeEncodeError: 'latin-1' codec can't encode characters in position 553626-553628: Body ('信用卡') is not valid Latin-1. Use body.encode('utf-8') if you want to send it encoded in UTF-8. 2018-01-08T04:54:20.447689+00:00 app[run.2251]: self._send_request(method, url, body, headers, encode_chunked) 2018-01-08T04:54:20.448396+00:00 app[run.2251]: (name.title(), data[err.start:err.end], name)) from None 2018-01-08T04:54:20.621819+00:00 heroku[run.2251]: State changed from up to complete 2018-01-08T04:54:20.609814+00:00 heroku[run.2251]: Process exited with status 1

There's a bug with the most recent google bigquery library. Hence, this error occurs in pandas-gbq

ImportError: pandas-gbq requires google-cloud-bigquery: cannot import name 'collections_abc'

Purpose

Current behavior of to_gbq is fail if the schema of the new data is not equivalent to the current schema. However, this means that the load fails if the new data is missing columns that are present in the current schema. For instance, this may occur when the data source I am using to construct the dataframe only provides non-empty values. Rather than determining the current schema of the GBQ table and adding empty columns to my dataframe, I would like to_gbq to load my data if the columns in the dataframe are a subset of the current schema.

Primary changes made

• Factoring a schema function out of verify_schema to support both verify_schema and schema_is_subset
• schema_is_subset determines whether local_schema is a subset of remote_schema
• the append flag uses schema_is_subset rather than verify_schema to determine if the data can be loaded

Auxiliary changes made

• PROJECT_ID etc are retrieved from an environment variable to facilitate local testing
• Running test_gbq through autopep8 added a row after two class names

One frustrating thing is having to pass the project_id (among other parameters) every time you write a query. For example, personally, I usually use the same project_id, almost always query with standard sql, and usually turn off verbose. I have to pass those three with every read_gbq, typing which adds up.

Potential options include setting an environment variable and reading from these default settings, but sometimes it can be different each time and fiddling with environment variables feels unfriendly. My suggestion would perhaps be to add a class that can wrap read_gbq() and to_gbq() in a client object. You could set the project_id attribute and dialect and whatever else in the client object, then re-use the object every time you want a query with those settings.

A very naive implementation here in this branch: https://github.com/pydata/pandas-gbq/compare/master...jasonqng:client-object-class?expand=1

Usage would be like:

>>> import gbq
>>> client = gbq.Client(project_id='project-name',dialect='standard',verbose=False)
>>> client.read("select 1")
   f0_
0    1
>>> client.read("select 2")
   f0_
0    2
>>> client.verbose=True
>>> client.read("select 3")
Requesting query... ok.
Job ID: c7d7e4c0-883a-4e14-b35f-61c9fae0c08b
Query running...
Query done.
Processed: 0.0 B Billed: 0.0 B
Standard price: $0.00 USD

Retrieving results...
Got 1 rows.

Total time taken 1.66 s.
Finished at 2018-01-02 14:06:01.
   f0_
0    3

Does that seem like a reasonable solution to all this extra typing or is there another preferred way? If so, I can open up a PR with the above branch.

Thanks, my tired fingers thank you all!

@tswast @jreback @parthea @maxim-lian

Version 0.1.4

This query returns a improperly named dict:

q = """
select struct(a,b) col
from
(SELECT 1 a, 2 b)
"""
df = gbq.read_gbq(q, dialect='standard', verbose=False)

Compare with result from Big Query:

An array of items also get turned into a arrays of dicts sometimes. For example:

q = """
select array_agg(a)
from
(select "1" a UNION ALL select "2" a)
"""
gbq.read_gbq(q, dialect='standard', verbose=False, project_id='project')

outputs:

Compare to Big Query:

These issues may or may not be related?

We're printing in addition to logging, when querying from BigQuery. This makes controlling the output much harder, aside from being un-idiomatic.

Printing in white, logging in red:

https://cloud.githubusercontent.com/assets/5635139/23176541/6028b884-f831-11e6-911a-48aa7741a4da.png

Bigquery requires scoped credentials when loading application default credentials

Quick test code below, it will return "invalid token" error. When uncomment the create_scoped() statement, the code run correctly without any error.

# use google default application credentials
export GOOGLE_APPLICATION_CREDENTIALS=/PATH/TO/GOOGLE_DEFAULT_CREDENTIALS.json

import httplib2

from googleapiclient.discovery import build
from oauth2client.client import GoogleCredentials

credentials = GoogleCredentials.get_application_default()
#credentials = credentials.create_scoped('https://www.googleapis.com/auth/bigquery')

http = httplib2.Http()
http = credentials.authorize(http)

service = build('bigquery', 'v2', http=http)

jobs = service.jobs()
job_data = {'configuration': {'query': {'query': 'SELECT 1'}}}

jobs.insert(projectId='projectid', body=job_data).execute()

When attempting to grant pandas access to my GBQ project, I am running into an issue where read_gbq is trying to get default credentials, failing / timing out, then printing out a URL to go to to grant the credentials. Since I'm not running this on google cloud platform, I do not expect to be able to get default credentials. In my case, I only want to run the CLI flow (without having oauth call back to my local server).

Here's the code

>>> import pandas_gbq as gbq
>>> gbq.read_gbq('SELECT 1', project_id=<project_id>, auth_local_webserver=False)

Here's what I see when I trigger a SIGINT once the query is invoked:

  File "/usr/lib/python3.5/site-packages/pandas_gbq/gbq.py", line 214, in get_credentials
    credentials = self.get_application_default_credentials()
  File "/usr/lib/python3.5/site-packages/pandas_gbq/gbq.py", line 243, in get_application_default_credentials
    credentials, _ = google.auth.default(scopes=[self.scope])
  File "/usr/lib/python3.5/site-packages/google/auth/_default.py", line 277, in default
    credentials, project_id = checker()
  File "/usr/lib/python3.5/site-packages/google/auth/_default.py", line 274, in <lambda>
    lambda: _get_gce_credentials(request))
  File "/usr/lib/python3.5/site-packages/google/auth/_default.py", line 176, in _get_gce_credentials
    if _metadata.ping(request=request):
  File "/usr/lib/python3.5/site-packages/google/auth/compute_engine/_metadata.py", line 73, in ping
    timeout=timeout)
  File "/usr/lib/python3.5/site-packages/google/auth/transport/_http_client.py", line 103, in __call__
    method, path, body=body, headers=headers, **kwargs)
  File "/usr/lib/python3.5/http/client.py", line 1106, in request
    self._send_request(method, url, body, headers)
  File "/usr/lib/python3.5/http/client.py", line 1151, in _send_request
    self.endheaders(body)
  File "/usr/lib/python3.5/http/client.py", line 1102, in endheaders
    self._send_output(message_body)
  File "/usr/lib/python3.5/http/client.py", line 934, in _send_output
    self.send(msg)
  File "/usr/lib/python3.5/http/client.py", line 877, in send
    self.connect()
  File "/usr/lib/python3.5/http/client.py", line 849, in connect
    (self.host,self.port), self.timeout, self.source_address)
  File "/usr/lib/python3.5/socket.py", line 702, in create_connection
    sock.connect(sa)
KeyboardInterrupt

I've also tried setting the env variable GOOGLE_APPLICATIONS_CREDENTIALS to empty. I'm using pandas-gbq version at commit 64a19b.

Hello. I faced the same confusion as #579, so have tried to update the docs.

Not only the BQ DATE type but also the TIME, TIMESTAMP, and FLOAT64 types seems to be wrong.

It seems to be due to a breaking change in google-cloud-bigquery v3.
https://cloud.google.com/python/docs/reference/bigquery/latest/upgrading#changes-to-data-types-loading-a-pandas-dataframe

I have confirmed the correct dtypes as:

>>> import pandas

>>> sql1 = """
SELECT
  TRUE AS BOOL,
  123 AS INT64,
  123.456 AS FLOAT64,

  TIME '12:30:00.45' AS TIME,
  DATE "2023-01-01" AS DATE,
  DATETIME "2023-01-01 12:30:00.45" AS DATETIME,
  TIMESTAMP "2023-01-01 12:30:00.45" AS TIMESTAMP
"""

>>> pandas.read_gbq(sql).dtypes
BOOL                        boolean
INT64                         Int64
FLOAT64                     float64
TIME                         dbtime
DATE                         dbdate
DATETIME             datetime64[ns]
TIMESTAMP       datetime64[ns, UTC]
dtype: object

>>> sql2 = """
SELECT
  DATE "2023-01-01" AS DATE,
  DATETIME "2023-01-01 12:30:00.45" AS DATETIME,
  TIMESTAMP "2023-01-01 12:30:00.45" AS TIMESTAMP,
UNION ALL
SELECT
  DATE "2263-04-12" AS DATE,
  DATETIME "2263-04-12 12:30:00.45" AS DATETIME,
  TIMESTAMP "2263-04-12 12:30:00.45" AS TIMESTAMP
"""

>>> pandas.read_gbq(sql2).dtypes
DATE         object
DATETIME     object
TIMESTAMP    object
dtype: object

Fixes #579 🦕

Introducing this for discussion. Planning on adding version 3.11 as a test platform. Not sure about all the inner workings behind our codebase, so there are prolly a number of things that need to be added/edited. Feel free to comment.

@tswast @parthea

WIP PR for discussion: aiming to provide the ability to include a redirect URI, client ID, and client secrets to facilitate the migration away from "out of band" OAuth authentication.

@tswast

See also changes in these repos:

• https://github.com/googleapis/python-bigquery-pandas/pull/595
• https://github.com/googleapis/google-auth-library-python-oauthlib/pull/259
• https://github.com/pydata/pydata-google-auth/pull/58

Hi,

I am having problems to install this package on a macos with M1 chip.

The error: Could not find <Python.h>. This could mean the following: * You're on Ubuntu and haven't run apt-get install python3-dev. * You're on RHEL/Fedora and haven't run yum install python3-devel or dnf install python3-devel (make sure you also have redhat-rpm-config installed) * You're on Mac OS X and the usual Python framework was somehow corrupted (check your environment variables or try re-installing?) * You're on Windows and your Python installation was somehow corrupted (check your environment variables or try re-installing?)

  [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip. error: legacy-install-failure

× Encountered error while trying to install package. ╰─> grpcio

note: This is an issue with the package mentioned above, not pip. hint: See above for output from the failure.

Environment details

• OS type and version: MacOS 12.4 (chip M1)
• Python version: Python 3.10.0
• pip version: pip 22.3.1

Thanks!

Hi, after checking out the pandas_gbq.read_gbq call parametrization I see that I can supply configuration={'dry_run': True} to make the query job to be a dry run.

However it will still attempt to find query destination to try download rows, which in this case will be nonexistent. It would be great if the pandas_gbq would be aware of dry_run and just output the query stats to debug log or return some stats data.

e.g. querying something like this: pandas_gbq.read_gbq("SELECT * FROM 'my_project_id.billing_ds.cloud_pricing_export'", configuration={'dry_run': True})

still results in the exception

Traceback (most recent call last): File "big_query_utils.py", line 134, in print(read_df('SELECT * FROM 'my_project_id.billing_ds.cloud_pricing_export'', configuration={'dry_run': True})) File "/Users/.../big_query/big_query_utils.py", line 95, in read_df return pandas_gbq.read_gbq(sql_or_table_id, **gbq_kwargs) File "/Users/.../lib/python3.9/site-packages/pandas_gbq/gbq.py", line 921, in read_gbq final_df = connector.run_query( File "/Users/.../lib/python3.9/site-packages/pandas_gbq/gbq.py", line 526, in run_query rows_iter = self.client.list_rows( File "/Users/.../lib/python3.9/site-packages/google/cloud/bigquery/client.py", line 3790, in list_rows table = self.get_table(table.reference, retry=retry, timeout=timeout) File "/Users/.../lib/python3.9/site-packages/google/cloud/bigquery/client.py", line 1034, in get_table api_response = self._call_api( File "/Users/.../lib/python3.9/site-packages/google/cloud/bigquery/client.py", line 782, in _call_api return call() File "/Users/.../lib/python3.9/site-packages/google/api_core/retry.py", line 283, in retry_wrapped_func return retry_target( File "/Users/.../lib/python3.9/site-packages/google/api_core/retry.py", line 190, in retry_target return target() File "/Users/.../lib/python3.9/site-packages/google/cloud/_http/init.py", line 494, in api_request raise exceptions.from_http_response(response) google.api_core.exceptions.NotFound: 404 GET https://bigquery.googleapis.com/bigquery/v2/projects/my_project_id/datasets/_6a20f817b1e72d456384bdef157062be9989000e/tables/anon71d825e7efee2856ce2b5e50a3df3a2579fd5583d14740ca3064bab740c8ffd9?prettyPrint=false: Not found: Table my_project_id:_6a20f817b1e72d456384bdef157062be9989000e.anon71d825e7efee2856ce2b5e50a3df3a2579fd5583d14740ca3064bab740c8ffd9

Saving data to NUMERIC Field failing with conversion from NoneType to Decimal is not supported

• python 3.9
• pandas 1.5.1

Stack trace

...........

df.to_gbq(project_id=self.client.project,
          File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas-1.5.1-py3.9-macosx-10.9-x86_64.egg/pandas/core/frame.py", line 2168, in to_gbq
gbq.to_gbq(
    File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas-1.5.1-py3.9-macosx-10.9-x86_64.egg/pandas/io/gbq.py", line 218, in to_gbq
pandas_gbq.to_gbq(
    File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas_gbq-0.17.9-py3.9.egg/pandas_gbq/gbq.py", line 1198, in to_gbq
connector.load_data(
    File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas_gbq-0.17.9-py3.9.egg/pandas_gbq/gbq.py", line 591, in load_data
chunks = load.load_chunks(
    File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas_gbq-0.17.9-py3.9.egg/pandas_gbq/load.py", line 240, in load_chunks
load_parquet(
    File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas_gbq-0.17.9-py3.9.egg/pandas_gbq/load.py", line 128, in load_parquet
dataframe = cast_dataframe_for_parquet(dataframe, schema)
File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas_gbq-0.17.9-py3.9.egg/pandas_gbq/load.py", line 103, in cast_dataframe_for_parquet
cast_column = dataframe[column_name].map(decimal.Decimal)
File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas-1.5.1-py3.9-macosx-10.9-x86_64.egg/pandas/core/series.py", line 4539, in map
new_values = self._map_values(arg, na_action=na_action)
File "/Users/xxxx/.local/lib/python3.9/site-packages/pandas-1.5.1-py3.9-macosx-10.9-x86_64.egg/pandas/core/base.py", line 890, in _map_values
new_values = map_f(values, mapper)
File "pandas/_libs/lib.pyx", line 2918, in pandas._libs.lib.map_infer
TypeError: conversion from NoneType to Decimal is not supported