arrowinvalid could not convert with type str ubuntu

I ran into the same error using the snowflake-connector-python package, which was converting to parquet files under the hood, and likewise was able to fix it by reverting to numpy<1.20.0 (converting all the types to object while maintaining numpy==1.20.0 did not work for me however). I don't know what the exact cause of the issue is, but it appears to cause an incompatibility within pyarrow. ArrowInvalid: Could not convert with type Image: did not recognize Python value type when inferring an Arrow data type . Well occasionally send you account related emails. numexpr: None 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. What would be the expected type when writing this column? The following measurements were taken with PyMongoArrow 1.0 and PyMongo 4.4. I know this issue is closed but I found the quick fix. 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. OK, finally got to experiment on Linux server. The solution's upgrading to Jupyter 5. Your Answer. bs4: None lxml: None privacy statement. When I try to map the tokenize_and_align_labels function, i get the following error: ArrowInvalid: Could not convert ' [' with type str: tried to convert to int64. LC_ALL: None The error seems to be related / probably has the same root cause, so I don't think there's a need to open a second issue. I'll try to experiment on Linux server but it may take some time. This cannot be saved to Parquet as Parquet is language-agnostic, thus Python objects are not a valid type. pandas_gbq: None 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. This tutorial is intended as a comparison between using just PyMongo, versus This is limited in utility for non-numeric extension . Apache Arrow; ARROW-7986 [Python] pa.Array.from_pandas cannot convert pandas.Series containing pyspark.ml.linalg.SparseVector Powered by a free Atlassian Jira open source license for Apache Software Foundation. And it also lets Arrow correctly identify the type! Possibly due to some of the depreciated types in NumPy. pymysql: None So the column Antecedent,Consequent is causing issues because it's a tuple. Do we need to also add "coerce_timestamps" and "allow_truncated_timestamps" parameters found in write_table() to from_pandas()? 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? is that it will iterate over the arrow table/ data frame / numpy array st.write (df) gives problem when df is a pivot table, Showing a dataframe gives an error ArrowInvalid: ('Could not convert All with type str: tried to convert to int', 'Conversion failed for column MM with type object'), Expected behavior: tables: None [Code]-pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type')-pandas score:0 In my understanding there is problem with 'type' because of repr Try this approach (it works): 1 2 3 4Apache Arrow Pandas pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type') Pandas pyarrow.lib.ArrowInvalid Apache Arrow Pandas Apache Arrow blosc: None https://github.com/apache/arrow/issues/20520. html5lib: 0.9999999 (conventional), and uses the same amount of memory. still gives ArrowTypeError: an integer is required (got type str). with PyMongoArrow. matplotlib: 2.2.2 pyarrow.lib.ArrowInvalid: ("Could not convert ' 10188018' with type str: tried to convert to int64", 'Conversion failed for column 1064 TEC serial with type object') I have tried looking online and found some that had close to the same problem. Go to our Self serve sign up page to request an account. 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 I received the error message: arrow_table = pa.Table.from_pandas (df)"): Error converting to Python objects to String/UTF8 I couldn't find anything useful on the internet to troubleshoot this issue. To see all available qualifiers, see our documentation. 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. @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). what code are you calling that produces this? Make software development more efficient, Also welcome to join our telegram. Daniel Vera Asks: ArrowInvalid: Could not convert . Sign In to Databricks Community Edition. can you provide the full traceback? We have started to get runtime errors when saving model predictions as parquet files in AzureML compute instances. Parameters: i int Index to place the column at. DTREX-670 :: feat (storage): Adds amora.storage.cache decorator to cache functions that returns a pandas.DataFrame mundipagg/amora-data-build-tool#144. IMHO we should close this since it's giving people the wrong impression that parquet "can't handle mixed type columns", e.g. Have a question about this project? Thank you @crmcpherson for the heads up, good catch! python-bits: 64 @xhochy Returns: Table Recently we have received many complaints from users about site-wide blocking of their own and blocking of their own activities please go to the settings off state, please visit We read every piece of feedback, and take your input very seriously. jinja2: 2.10 Some read in as float and others as string. This will automatically handle the mixed types columns error. score:1 Accepted answer I'm not too familiar with streamlit and st.dataframe but it looks like it's trying to convert precedence_df to a pyarrow.Table. This is a "remote dev environment" based on Ubuntu that can only be accessed via ssh and is wiped when restarted - so I run these commands on initial ssh login (in an interactive shell). Submit Answer. The most basic way to read data using PyMongo is: This works, but we have to exclude the _id field because otherwise we get this error: The workaround gets ugly (especially if youre using more than ObjectIds): Even though this avoids the error, an unfortunate drawback is that Arrow cannot identify that it is an ObjectId, Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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. For insertions, the library performs about the same as when using PyMongo print(df) doesn't throw an error. Do I have to open a new issue for that or is it related with this one @vdonato ? as noted by the schema showing _id is a string. 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)) . field_ str or Field If a string is passed then the type is deduced from the column data. Question / answer owners are mentioned in the video. As in the above is stated, this problem often occurs while reading in different dataframes and concatenating them with pd.concat. 1 Answer Sorted by: 1 The new Jupyter, apparently, has changed some of the pandas related libraries. pyarrow: 0.9.0 pyarrow.lib.ArrowInvalid: ('Casting from timestamp[ns] to timestamp[ms] would lose data: 1532015191753713000', 'Conversion failed for column modified with type datetime64[ns]'). 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'). Share Improve this answer Follow answered Oct 19, 2022 at 4:02 Olivia Rodrigo Stan All rights reserved. https://github.com/apache/arrow/issues/21014. The workaround gets ugly (especially if you're using more than ObjectIds): . We could of course still do a conversion on the pandas side, but that would need to be rather custom logic (and a user can do df.astype({'col': str}).to_parquet(..) themselves before writing to parquet). Recently we have received many complaints from users about site-wide blocking of their own and blocking of The problem here is that you have partly strings, partly integer values. 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). I am pretty sure it has to do with all of the columns having a dtype of string. I want to state clear that this is not a problem for the pd.DataFrame.to_parquet function. privacy statement. Downgraded to 1.19.1 and it worked. s3fs: None I'll reopen this given that the way that this case comes about (creating a pivot table) is one that's likely to be very common, so we'll want to have this work without needing to change the type of a column manually. to your account. Could any new AzureML release break something? By clicking Sign up for GitHub, you agree to our terms of service and Therefore for object columns one must look at the actual data and infer a more specific type. If it helps: Email. This happens when using either engine but is clearly seen when using data.to_parquet('example.parquet', engine='fastparquet'). Try Jira - bug tracking software for your team. The problem with mixed type columns still exists in pyarrow-0.9.0+254, diogommartins mentioned this issue on Jul 5, 2022. Your Name. So in that case at least, it may be more an issue with concat() than with to_parquet(). Have a question about this project? python machine-learning python-imaging-library huggingface-datasets. pyarrow==2.0.0 time str ( time time [ astype float Schema from_pandas ( df=df [ [ "c0" ]]) which then generates the desired schema. What I fail to understand is why this worked before and now it does not. 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. Well occasionally send you account related emails. with type DataFrame: did not recognize Python value type when inferring an Arrow data type . sqlalchemy: None Sign In column Array, list of Array, or values coercible to arrays Column data. To see all available qualifiers, see our documentation. xarray: None Already on GitHub? This allows you to avoid the ugly workaround: And it also lets Arrow correctly identify the type! byteorder: little You signed in with another tab or window. Go to our Self serve sign up page to request an account. By clicking Sign up for GitHub, you agree to our terms of service and githubmemory 2021. 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. Related Questions . The primary benefit that PyMongoArrow gives is support for BSON types through Arrow/Pandas Extension Types. PipEnv? inorder to preserve the dtype but when it comes to typecasting and writing it into array (from list) pyarrow.array(data, type=type) it gives the following error: pyarrow.lib.ArrowInvalid: Could not convert [0 0 0] with type numpy.ndarray: tried to convert to int. Not all Pandas tables coerce to Arrow tables, and when they fail, not in a way that is conducive to automation: Sample: mixed_df = pd.DataFrame({'mixed': [1, 'b']}) pa.Table.from_pandas(mixed_df) => ArrowInvalid: ('Could not convert b with type str: tried to convert to double', 'Conversion failed for column mixed with type object') fastparquet: 0.1.5 When I load it back into pandas, the type of the str column would be object again. 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). Powered by a free Atlassian Jira open source license for Apache Software Foundation. Public signup for this instance is disabled. The reader is assumed to be familiar with basic https://stackoverflow.com/questions/50876505/does-any-python-library-support-writing-arrays-of-structs-to-parquet-files. LANG: None 0 Answer . LOCALE: None.None, pandas: 0.23.0 Edit: If you happen to hit an error with NA's being hardcoded into 'None' after you convert your object columns into str, make sure to convert these NA's into np.nan before converting into str (stackoverflow link), First, find out the mixed type column and convert them to string. . The text was updated successfully, but these errors were encountered: Using the latest pyarrow master, this may already been fixed. and not convert the entire object to a list. Subscribe to the mailing list . Trademarks are property of respective owners and stackexchange. Powered by a free Atlassian Jira open source license for Apache Software Foundation. xlwt: 1.3.0 My initial intention was to test if databricks.koala's functionality is implemented, which took me to error coming from pyarrow: while pd.Series on the SparseVector works fine, the last line errors as: https://github.com/databricks/koalas/issues/1323. It appears when you want to print the dtypes of a pivoted dataframe with mixed datatypes in a column. EDIT: for some reason, this does not work without the azureml-sdk dependency either. : For reads, the library is somewhat slower for small documents and nested machine: AMD64 xlsxwriter: 1.0.4 Disclaimer: All information is provided as it is with no warranty of any kind. processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel There appears to have been a regression introduced in 0.11.0 such that we can no longer create a Decimal128 array using integers. https://koalas.readthedocs.io/en/latest/development/contributing.html, https://github.com/apache/arrow/issues/17073. ArrowInvalid: Could not convert ObjectId ('642f2f4720d92a85355671b3') with type ObjectId: did not recognize Python value type when inferring an Arrow data type. pip: 10.0.1 Expected result: Behavior same as 0.10.0 and earlier; a Decimal128 array would be created with no problems. We did not change anything from our side so it seems some non-pinned dependency is now resolved differently. 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') ] dateutil: 2.7.3 but if you wanted to for example, sort datetimes, it avoids unecessary casting: Additionally, PyMongoArrow supports Pandas extension types. Cython: None feather: None There is still a weird issue with nightly builds. When you take this approach it'll convert all pd.NaN to just a string of "nan", which in my case is quite awful. 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 Pandas pyarrow.lib.ArrowInvalid Apache Arrow, Apache Arrow, Pandas pyarrow.lib.ArrowInvalid Apache ArrowPandasApache Arrow, ParquetPandas, Pandas pyarrow.lib.ArrowInvalid, PandasPandas, PandasPandas pyarrow.lib.ArrowInvalid, Pandas pyarrow.lib.ArrowInvalidPandasPython, PandasApache ArrowPandasPyarrow, Apache ArrowPandasApache Arrow, Pandas pyarrow.lib.ArrowInvalidApache ArrowApache Arrow, Pandas pyarrow.lib.ArrowInvalid: (Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type), PandasSeaborn.relplot()hue, Pandas ValueErrorpandas.read_json, Pandas TypeError: cannot convert the series to, PythonPandas DataFrameGoogle Sheets, Pandas statsmodelsRlmPandas, Pandas MultiIndex pandas dataframe, Pandas pyarrow.lib.ArrowInvalid: ('Could not convert X with type Y: did not recognize Python value type when inferring an Arrow data type'), Pandas pandas.read_csv FileNotFoundError: File b'\xe2\x80\xaa, Pandasn * obj.freq, Pandas Python Pandas PerformanceWarning, PandasSpark 2.2row_number()PySpark DataFrame, Pandas Python df.to_excel()Excel, Pandas Pythonpandas to_excel'utf8' codec can't decode byte. Is there any way to avoid this issue? The new dataframe serialization format that we use, Arrow, requires that all entries in a column have the same type. bottleneck: None The crash doesn't occur if we use a decimal.Decimal object instead. psycopg2: None I just want to point out something I encountered with the solution astype. 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. 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? xlrd: 1.1.0 It is a table with expenses, quit simple (date, category, amount), I already converted the columnnames into float and removed the totals. While in pandas you can have arbitrary objects as the data type of your column, in pyarrow it's not possible. pandas_datareader: None. setuptools: 39.1.0 openpyxl: None One thing that could be done here would be to cast the integers in the MM to all have type str. For now, the workaround that I mentioned in my previous comment should be enough to help with this, but hopefully we can make the process a bit easier in a release in the near future. privacy-policy | terms | Advertise | Contact us | About Forgot Password? This is not the case for my example - column B can't have integer type. This is limited in utility for non-numeric extension types, IPython: 6.4.0 When you write to_parquet(), make sure to pass the argument low_memory = False. You can see that it is a mixed type column issue if you use to_csv and read_csv to load data from csv file instead - you get the following warning on import: Specifying dtype option solves the issue but it isn't convenient that there is no way to set column types after loading the data. 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 Try Jira - bug tracking software for your team. pytest: 3.5.1 scipy: 1.1.0 their own activities please go to the settings off state, please visit, Using Conda? 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. You switched accounts on another tab or window. I believe this issue is caused by an update in numpy=1.20.0 released a few days ago. numpy==1.20.1 patsy: 0.5.0 sphinx: None Sign in Hm, on second thought. "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'). 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). numpy: 1.14.3 Saved searches Use saved searches to filter your results more quickly documents, but faster for large documents . You switched accounts on another tab or window. 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. PyMongo and Upgraded pyarrow to 3.0.0 and numpy 1.20.1 also worked well. Try Jira - bug tracking software for your team. Apperently the total column is a single object? Note that Arrow and Pandas can only have columns of a single type. :), pyarrow.lib.ArrowInvalid: ('Could not convert int64 with type numpy.dtype: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column 0 with type object'). Description There appears to have been a regression introduced in 0.11.0 such that we can no longer create a Decimal128 array using integers. I would expect it to be a string. OS-release: 10 Sign in The solution that's the best imo is to look which columns cause problems and add it as a dtype in your pd.read_csv.
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