still gives ArrowTypeError: an integer is required (got type str). Hm, on second thought. their own activities please go to the settings off state, please visit, Using Conda? Public signup for this instance is disabled. numpy==1.20.1 IMHO, there should be an option to write a column with a string type even if all the values inside are integers - for example, to maintain consistency of column types among multiple files. time str ( time time [ astype float Schema from_pandas ( df=df [ [ "c0" ]]) which then generates the desired schema. Content is licensed under CC BY SA 2.5 and CC BY SA 3.0. re ' you have partly strings, partly integer values. For example Pandas has the very generic type of object. "Conversion failed for column {!s} with type {!s}", "Field {} was non-nullable but pandas column ", 'Conversion failed for column x with type int64', pyarrow.lib.ArrowTypeError: ('Did not pass numpy.dtype object', 'Conversion failed for column X with type int64'). 1 Answer Sorted by: 1 The new Jupyter, apparently, has changed some of the pandas related libraries. Public signup for this instance is disabled. documents, but faster for large documents . arrow_table = pa.Table.from_pandas(df) Error converting to Python I am pretty sure it has to do with all of the columns having a dtype of string. Quick Start PyMongoArrow 1.0.2 documentation processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel Try Jira - bug tracking software for your team. Hi @rcsmit, this is expected behavior. The issue here is that the last entry of the MM column has type str, which mismatches with the other elements of type int64. https://github.com/apache/arrow/issues/20520. python: 3.6.5.final.0 We could have some mechanism to indicate "this column should have a string type in the final parquet file", like we have a dtype argument for to_sql (you can actually already do something like manually this by passing the schema argument). This tutorial is intended as a comparison between using just PyMongo, versus @titsitits you might want to have a look at DataFrame.infer_objects to see if this helps converting object dtypes to proper dtypes (although it will not do any forced conversions, eg no string number to an actual numeric dtype). privacy statement. I know this is a closed issue, but in case someone looks for a patch, here is what worked for me: I needed this as I was dealing with a large dataframe (coming from openfoodfacts: https://world.openfoodfacts.org/data ), containing 1M lines and 177 columns of various types, and I simply could not manually cast each column. Apache Arrow; ARROW-7986 [Python] pa.Array.from_pandas cannot convert pandas.Series containing pyspark.ml.linalg.SparseVector Your Name. MongoDB, Mongo, and the leaf logo are registered trademarks of MongoDB, Inc. _id: extension>. ArrowInvalid: Could not convert with type Image: did not recognize I want to state clear that this is not a problem for the pd.DataFrame.to_parquet function. With PyMongo, a Decimal128 value behaves as follows: In both cases the underlying values are the bson class type: Writing data from an Arrow table using PyMongo looks like the following: As of PyMongoArrow 1.0, the main advantage to using the write function We read every piece of feedback, and take your input very seriously. ArrowInvalid: Could not convert ObjectId ('642f2f4720d92a85355671b3') with type ObjectId: did not recognize Python value type when inferring an Arrow data type. ArrowInvalid: Could not convert 1 with type pyarrow.lib.Int64Value: did not recognize Python value type when inferring an Arrow data type In [44]: pa.array (list (arr)) . to your account, to_parquet tries to convert an object column to int64. Returns: Table as noted by the schema showing _id is a string. Upgraded pyarrow to 3.0.0 and numpy 1.20.1 also worked well. Some read in as float and others as string. If it helps: print(df) doesn't throw an error. DTREX-670 :: feat (storage): Adds amora.storage.cache decorator to cache functions that returns a pandas.DataFrame mundipagg/amora-data-build-tool#144. It has nothing to do with to_parquet, and as he pointed out, the user can always do df.astype({'col': str}).to_parquet(..) to manage and mix types as needed. Downgraded to 1.19.1 and it worked. We read every piece of feedback, and take your input very seriously. Error Loading DataFrame to BigQuery Table (pyarrow.lib.ArrowInvalid and not convert the entire object to a list. By clicking Sign up for GitHub, you agree to our terms of service and feather: None Pandas : pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y what code are you calling that produces this? MongoDB concepts. Disclaimer: All information is provided as it is with no warranty of any kind. There appears to have been a regression introduced in 0.11.0 such that we can no longer create a Decimal128 array using integers. (conventional), and uses the same amount of memory. Sign In Apperently the total column is a single object? I had a similar problem with being unable to install 0.9.0+ arrow-cpp version as described here: The problem with mixed type columns still exists in. 0 Answer . pyarrow.lib.ArrowInvalid: Could not convert with type numpy.ndarray setuptools: 39.1.0 I just want to point out something I encountered with the solution astype. The workaround gets ugly (especially if you're using more than ObjectIds): . Try Jira - bug tracking software for your team. Then find out list type column and convert them to string if not you may get pyarrow.lib.ArrowInvalid: Nested column branch had multiple children, Reference:https://stackoverflow.com/questions/29376026/whats-a-good-strategy-to-find-mixed-types-in-pandas-columns Public signup for this instance is disabled. to_parquet can't handle mixed type columns #21228 - GitHub pytest: 3.5.1 This is not the case for my example - column B can't have integer type. @xhochy pyarrow: 0.9.0 Trademarks are property of respective owners and stackexchange. IPython: 6.4.0 We have started to get runtime errors when saving model predictions as parquet files in AzureML compute instances. As @jorisvandenbossche mentioned, the OP's problem is type inference when doing pd.read_excel(). In order to fix it you need to change the column dtype beforehand like: import time import pandas as pd import pyarrow as pa DataFrame ( { "c0": [ int ( time. Showing the dataframe. pyarrow.lib.ArrowTypeError: ('Did not pass numpy.dtype object - GitHub Type Support in Pandas API on Spark column Array, list of Array, or values coercible to arrays Column data. with PyMongoArrow. A temporary fix for me seemed to be fixing numpy==1.19.5 for the time being. I don't know what the exact cause of the issue is, but it appears to cause an incompatibility within pyarrow. lxml: None What I fail to understand is why this worked before and now it does not. field_ str or Field If a string is passed then the type is deduced from the column data. Have a question about this project? Powered by a free Atlassian Jira open source license for Apache Software Foundation. jinja2: 2.10 pyarrow.Table Apache Arrow v12.0.1 Try Jira - bug tracking software for your team. scipy: 1.1.0 Could any new AzureML release break something? Possibly due to some of the depreciated types in NumPy. Cython: None Do we need to also add "coerce_timestamps" and "allow_truncated_timestamps" parameters found in write_table() to from_pandas()? OS: Windows pyarrow.lib.ArrowInvalid: ('Could not convert <Jack (21)> with type Player: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column 0 with type object') Same error encountered by using df.to_parquet ('players.pq') Is it possible for pyarrow to fallback to serializing these Python objects using pickle? It is also strange that to_parquet tries to infer column types instead of using dtypes as stated in .dtypes or .info(), to_parquet tries write parquet file using dtypes as specified, commit: None Powered by a free Atlassian Jira open source license for Apache Software Foundation. Pandas : pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type') \r[ Beautify Your Computer : https://www.hows.tech/p/recommended.html ] \r \rPandas : pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type') \r\rNote: The information provided in this video is as it is with no modifications.\rThanks to many people who made this project happen. with type DataFrame: did not recognize Python value type when inferring an Arrow data type . Your Answer. To see all available qualifiers, see our documentation. This allows you to avoid the ugly workaround: And it also lets Arrow correctly identify the type! If I create a conda environment locally without the azureml-sdk dependency I don't get any errors which makes me think the problem might be more related to the base image used instead. I would expect it to be a string. Nested Json File gives pyarrow.lib.ArrowInvalid #647 - GitHub pinning numpy version so solve test issue: build(setup.cfg): pin numpy dependency <1.20.0 to avoid incompatibili, upgrading pyarrow to fix the numpy 1.21.0 broken changes and fixing integ tests. The following measurements were taken with PyMongoArrow 1.0 and PyMongo 4.4. I have been able to reproduce this, both from the specific compute instance image and from a brand new docker image with the following commands: Inside the docker container (this just reproduces what is inside our environment.yml file): I observed that the package azureml-dataset-runtime[fuse] (azureml-sdk dependency) actually requires pyarrow<2.0.0,>=0.17.0 and downgrades the pyarrow version to 1.0.1 but I am not sure if this is actually the reason of the error. Thank you @crmcpherson for the heads up, good catch! It is a table with expenses, quit simple (date, category, amount), I already converted the columnnames into float and removed the totals. You signed in with another tab or window. Well occasionally send you account related emails. What would be the expected type when writing this column?' Make software development more efficient, Also welcome to join our telegram. Is there any way to avoid this issue? Parameters: i int Index to place the column at. Sign in [ARROW-3907] [Python] from_pandas errors when schemas are used with You switched accounts on another tab or window. To see all available qualifiers, see our documentation. Share Improve this answer Follow answered Oct 19, 2022 at 4:02 Olivia Rodrigo Stan pyarrow==2.0.0 bs4: None EDIT: This seems to do the trick df = df.astype(str), With another dataframe I have also a problem when using st.write. but if you wanted to for example, sort datetimes, it avoids unecessary casting: Additionally, PyMongoArrow supports Pandas extension types. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. EDIT: for some reason, this does not work without the azureml-sdk dependency either. I realize that this has been closed for a while now, but as I'm revisiting this error, I wanted to share a possible hack around it (not that it's an ideal approach): I cast all my categorical columns into 'str' before writing as parquet (instead of specifying each column by name which can get cumbersome for 500 columns). ArrowInvalid: ("Could not convert 'training' with type str: tried to convert to int64", 'Conversion failed for column Label with type object') 1 Like mariosasko February 28, 2022, 3:07pm 2 Hi! numexpr: None pyarrow.lib.ArrowInvalid: ("Could not convert '5' with type str: tried to convert to int", 'Conversion failed for column [name of column] with type object') I checked the data frame the table is initialized with and the columns are all type int. xarray: None I was getting this error: pyarrow.lib.ArrowTypeError: ('Did not pass numpy.dtype object', 'Conversion failed for column IN_MU_user_fee with type bool'). The text was updated successfully, but these errors were encountered: it looks like pyarrow==3.0.0 released last week, could that be the issue? I can confirm reverting to numpy<1.20.0 fixes the issue (pandas==1.1.3 has as requirement numpy>=1.15.4, this is why the new version 1.20.0 released this last Saturday was now picked). When you write to_parquet(), make sure to pass the argument low_memory = False. It is available for Linux only. to_parquet can't handle mixed type columns, pyarrow.lib.ArrowTypeError: "Expected a string or bytes object, got a 'int' object", https://stackoverflow.com/questions/29376026/whats-a-good-strategy-to-find-mixed-types-in-pandas-columns, https://stackoverflow.com/questions/50876505/does-any-python-library-support-writing-arrays-of-structs-to-parquet-files, TypeError: ufunc 'isnan' not supported for the input types. If there any issues, contact us on - htfyc dot hows dot tech\r \r#Pandas:pyarrowlibArrowInvalid:(CouldnotconvertXwithtypeY:didnotrecognizePythonvaluetypewheninferringanArrowdatatype) #Pandas #: #pyarrow.lib.ArrowInvalid: #('Could #not #convert #X #with #type #Y: #did #not #recognize #Python #value #type #when #inferring #an #Arrow #data #type')\r \rGuide : [ Pandas : pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type') ] However, the problem is that the arrow functions that convert numpy arrays to arrow arrays still give errors for mixed string / integer types, even if you indicate that it should be strings, eg: So unless that is something arrow would want to change (but personally I would not do that), this would not help for the specific example case in this issue. https://stackoverflow.com/questions/50876505/does-any-python-library-support-writing-arrays-of-structs-to-parquet-files. The primary benefit that PyMongoArrow gives is support for BSON types through Arrow/Pandas Extension Types. Details Type:Improvement Status:Resolved Priority:Major Resolution:Fixed Affects Version/s:None Fix Version/s: 6.0.0 Component/s:Python Labels: pull-request-available Description See current behavior Alternatively you can convert the column types to object before running the export. pyarrow.lib.ArrowInvalid: ('Casting from timestamp[ns] to timestamp[ms] would lose data: 1532015191753713000', 'Conversion failed for column modified with type datetime64[ns]'). Related Questions . pytz: 2018.4 python machine-learning python-imaging-library huggingface-datasets. Saved searches Use saved searches to filter your results more quickly This is limited in utility for non-numeric extension . Sign in byteorder: little What would be the expected type when writing this column? does the job ( .str.zfill(2) is to prevent the 1 10 11 12 3 4 etc order), The error in the summary i mentioned before was the wrong one.