Consider reading in the dataframe and selecting only those rows with df.number > 0. Are there conventions to indicate a new item in a list? (Apache Pig UDF: Part 3). There other more common telltales, like AttributeError. at I use spark to calculate the likelihood and gradients and then use scipy's minimize function for optimization (L-BFGS-B). You will not be lost in the documentation anymore. Conclusion. at Making statements based on opinion; back them up with references or personal experience. PySpark is software based on a python programming language with an inbuilt API. Also in real time applications data might come in corrupted and without proper checks it would result in failing the whole Spark job. Announcement! Your email address will not be published. Making statements based on opinion; back them up with references or personal experience. = get_return_value( 1. Spark allows users to define their own function which is suitable for their requirements. 6) Use PySpark functions to display quotes around string characters to better identify whitespaces. a database. A parameterized view that can be used in queries and can sometimes be used to speed things up. an FTP server or a common mounted drive. Lloyd Tales Of Symphonia Voice Actor, A Computer Science portal for geeks. org.apache.spark.scheduler.Task.run(Task.scala:108) at at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2842) Salesforce Login As User, and you want to compute average value of pairwise min between value1 value2, you have to define output schema: The new version looks more like the main Apache Spark documentation, where you will find the explanation of various concepts and a "getting started" guide. "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 71, in Is quantile regression a maximum likelihood method? func = lambda _, it: map(mapper, it) File "", line 1, in File +---------+-------------+ sun.reflect.GeneratedMethodAccessor237.invoke(Unknown Source) at Italian Kitchen Hours, . A pandas UDF, sometimes known as a vectorized UDF, gives us better performance over Python UDFs by using Apache Arrow to optimize the transfer of data. And also you may refer to the GitHub issue Catching exceptions raised in Python Notebooks in Datafactory?, which addresses a similar issue. Create a sample DataFrame, run the working_fun UDF, and verify the output is accurate. christopher anderson obituary illinois; bammel middle school football schedule (Though it may be in the future, see here.) Buy me a coffee to help me keep going buymeacoffee.com/mkaranasou, udf_ratio_calculation = F.udf(calculate_a_b_ratio, T.BooleanType()), udf_ratio_calculation = F.udf(calculate_a_b_ratio, T.FloatType()), df = df.withColumn('a_b_ratio', udf_ratio_calculation('a', 'b')). This method is straightforward, but requires access to yarn configurations. I am wondering if there are any best practices/recommendations or patterns to handle the exceptions in the context of distributed computing like Databricks. Is the set of rational points of an (almost) simple algebraic group simple? Sometimes it is difficult to anticipate these exceptions because our data sets are large and it takes long to understand the data completely. When you creating UDFs you need to design them very carefully otherwise you will come across optimization & performance issues. get_return_value(answer, gateway_client, target_id, name) Hi, In the current development of pyspark notebooks on Databricks, I typically use the python specific exception blocks to handle different situations that may arise. truncate) Do we have a better way to catch errored records during run time from the UDF (may be using an accumulator or so, I have seen few people have tried the same using scala), --------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call Our idea is to tackle this so that the Spark job completes successfully. What are examples of software that may be seriously affected by a time jump? Heres an example code snippet that reads data from a file, converts it to a dictionary, and creates a broadcast variable. at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at How do you test that a Python function throws an exception? --- Exception on input: (member_id,a) : NumberFormatException: For input string: "a" in main Itll also show you how to broadcast a dictionary and why broadcasting is important in a cluster environment. +---------+-------------+ org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152) If youre using PySpark, see this post on Navigating None and null in PySpark.. Interface. Python raises an exception when your code has the correct syntax but encounters a run-time issue that it cannot handle. Viewed 9k times -1 I have written one UDF to be used in spark using python. last) in () 337 else: org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814) or as a command line argument depending on how we run our application. an enum value in pyspark.sql.functions.PandasUDFType. object centroidIntersectService extends Serializable { @transient lazy val wkt = new WKTReader () @transient lazy val geometryFactory = new GeometryFactory () def testIntersect (geometry:String, longitude:Double, latitude:Double) = { val centroid . Note 1: It is very important that the jars are accessible to all nodes and not local to the driver. Spark provides accumulators which can be used as counters or to accumulate values across executors. Add the following configurations before creating SparkSession: In this Big Data course, you will learn MapReduce, Hive, Pig, Sqoop, Oozie, HBase, Zookeeper and Flume and work with Amazon EC2 for cluster setup, Spark framework and Scala, Spark [] I got many emails that not only ask me what to do with the whole script (that looks like from workwhich might get the person into legal trouble) but also dont tell me what error the UDF throws. Handling exceptions in imperative programming in easy with a try-catch block. Spark code is complex and following software engineering best practices is essential to build code thats readable and easy to maintain. something like below : An Azure service for ingesting, preparing, and transforming data at scale. Not the answer you're looking for? Subscribe Training in Top Technologies data-errors, It is in general very useful to take a look at the many configuration parameters and their defaults, because there are many things there that can influence your spark application. You can use the design patterns outlined in this blog to run the wordninja algorithm on billions of strings. The second option is to have the exceptions as a separate column in the data frame stored as String, which can be later analysed or filtered, by other transformations. Understanding how Spark runs on JVMs and how the memory is managed in each JVM. Could very old employee stock options still be accessible and viable? Training in Top Technologies . 3.3. In particular, udfs need to be serializable. 1. Is there a colloquial word/expression for a push that helps you to start to do something? These include udfs defined at top-level, attributes of a class defined at top-level, but not methods of that class (see here). # squares with a numpy function, which returns a np.ndarray. call last): File Spark driver memory and spark executor memory are set by default to 1g. A predicate is a statement that is either true or false, e.g., df.amount > 0. org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517) pyspark. getOrCreate # Set up a ray cluster on this spark application, it creates a background # spark job that each spark task launches one . So our type here is a Row. Another interesting way of solving this is to log all the exceptions in another column in the data frame, and later analyse or filter the data based on this column. . Lets try broadcasting the dictionary with the pyspark.sql.functions.broadcast() method and see if that helps. org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150) Serialization is the process of turning an object into a format that can be stored/transmitted (e.g., byte stream) and reconstructed later. For udfs, no such optimization exists, as Spark will not and cannot optimize udfs. Found inside Page 221unit 79 univariate linear regression about 90, 91 in Apache Spark 93, 94, 97 R-squared 92 residuals 92 root mean square error (RMSE) 92 University of Handling null value in pyspark dataframe, One approach is using a when with the isNull() condition to handle the when column is null condition: df1.withColumn("replace", \ when(df1. one date (in string, eg '2017-01-06') and Lets create a UDF in spark to Calculate the age of each person. A Medium publication sharing concepts, ideas and codes. How to catch and print the full exception traceback without halting/exiting the program? Copyright . Broadcasting values and writing UDFs can be tricky. If your function is not deterministic, call Keeping the above properties in mind, we can still use Accumulators safely for our case considering that we immediately trigger an action after calling the accumulator. Spark udfs require SparkContext to work. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) In other words, how do I turn a Python function into a Spark user defined function, or UDF? df4 = df3.join (df) # joinDAGdf3DAGlimit , dfDAGlimitlimit1000joinjoin. 126,000 words sounds like a lot, but its well below the Spark broadcast limits. asNondeterministic on the user defined function. 6) Explore Pyspark functions that enable the changing or casting of a dataset schema data type in an existing Dataframe to a different data type. When you add a column to a dataframe using a udf but the result is Null: the udf return datatype is different than what was defined. https://github.com/MicrosoftDocs/azure-docs/issues/13515, Please accept an answer if correct. although only the latest Arrow / PySpark combinations support handling ArrayType columns (SPARK-24259, SPARK-21187). at Here's one way to perform a null safe equality comparison: df.withColumn(. This post summarizes some pitfalls when using udfs. However, they are not printed to the console. ray head or some ray workers # have been launched), calling `ray_cluster_handler.shutdown()` to kill them # and clean . org.apache.spark.scheduler.Task.run(Task.scala:108) at Note 2: This error might also mean a spark version mismatch between the cluster components. This is a kind of messy way for writing udfs though good for interpretability purposes but when it . iterable, at Is email scraping still a thing for spammers, How do I apply a consistent wave pattern along a spiral curve in Geo-Nodes. org.postgresql.Driver for Postgres: Please, also make sure you check #2 so that the driver jars are properly set. Asking for help, clarification, or responding to other answers. at Programs are usually debugged by raising exceptions, inserting breakpoints (e.g., using debugger), or quick printing/logging. | a| null| Another way to show information from udf is to raise exceptions, e.g.. spark.range (1, 20).registerTempTable ("test") PySpark UDF's functionality is same as the pandas map () function and apply () function. | a| null| Unit testing data transformation code is just one part of making sure that your pipeline is producing data fit for the decisions it's supporting. If a stage fails, for a node getting lost, then it is updated more than once. at And it turns out Spark has an option that does just that: spark.python.daemon.module. Observe the predicate pushdown optimization in the physical plan, as shown by PushedFilters: [IsNotNull(number), GreaterThan(number,0)]. ) from ray_cluster_handler.background_job_exception return ray_cluster_handler except Exception: # If driver side setup ray-cluster routine raises exception, it might result # in part of ray processes has been launched (e.g. (PythonRDD.scala:234) Asking for help, clarification, or responding to other answers. How to identify which kind of exception below renaming columns will give and how to handle it in pyspark: how to test it by generating a exception with a datasets. The user-defined functions are considered deterministic by default. at Learn to implement distributed data management and machine learning in Spark using the PySpark package. It takes 2 arguments, the custom function and the return datatype(the data type of value returned by custom function. Conditions in .where() and .filter() are predicates. With lambda expression: add_one = udf ( lambda x: x + 1 if x is not . By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029) at Compared to Spark and Dask, Tuplex improves end-to-end pipeline runtime by 591and comes within 1.11.7of a hand- This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Exceptions occur during run-time. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 177, This button displays the currently selected search type. . If either, or both, of the operands are null, then == returns null. Over the past few years, Python has become the default language for data scientists. For example, if the output is a numpy.ndarray, then the UDF throws an exception. at Explain PySpark. If you're using PySpark, see this post on Navigating None and null in PySpark.. The good values are used in the next steps, and the exceptions data frame can be used for monitoring / ADF responses etc. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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 above each example. Usually, the container ending with 000001 is where the driver is run. Retracting Acceptance Offer to Graduate School, Torsion-free virtually free-by-cyclic groups. Pardon, as I am still a novice with Spark. The accumulators are updated once a task completes successfully. What is the arrow notation in the start of some lines in Vim? 2020/10/21 Memory exception Issue at the time of inferring schema from huge json Syed Furqan Rizvi. Here is a blog post to run Apache Pig script with UDF in HDFS Mode. Do not import / define udfs before creating SparkContext, Run C/C++ program from Windows Subsystem for Linux in Visual Studio Code, If the query is too complex to use join and the dataframe is small enough to fit in memory, consider converting the Spark dataframe to Pandas dataframe via, If the object concerned is not a Spark context, consider implementing Javas Serializable interface (e.g., in Scala, this would be. data-engineering, To fix this, I repartitioned the dataframe before calling the UDF. in process Let's create a UDF in spark to ' Calculate the age of each person '. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Top 5 premium laptop for machine learning. I've included an example below from a test I've done based on your shared example : Sure, you found a lot of information about the API, often accompanied by the code snippets. If the above answers were helpful, click Accept Answer or Up-Vote, which might be beneficial to other community members reading this thread. ``` def parse_access_history_json_table(json_obj): ''' extracts list of at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) Debugging (Py)Spark udfs requires some special handling. org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:797) The CSV file used can be found here.. from pyspark.sql import SparkSession spark =SparkSession.builder . Java string length UDF hiveCtx.udf().register("stringLengthJava", new UDF1 The data in the DataFrame is very likely to be somewhere else than the computer running the Python interpreter - e.g. In other words, how do I turn a Python function into a Spark user defined function, or UDF? This code will not work in a cluster environment if the dictionary hasnt been spread to all the nodes in the cluster. If youre already familiar with Python and libraries such as Pandas, then PySpark is a great language to learn in order to create more scalable analyses and pipelines. This type of UDF does not support partial aggregation and all data for each group is loaded into memory. Getting the maximum of a row from a pyspark dataframe with DenseVector rows, Spark VectorAssembler Error - PySpark 2.3 - Python, Do I need a transit visa for UK for self-transfer in Manchester and Gatwick Airport. udf. However when I handed the NoneType in the python function above in function findClosestPreviousDate() like below. For example, if the output is a numpy.ndarray, then the UDF throws an exception. Suppose further that we want to print the number and price of the item if the total item price is no greater than 0. call(self, *args) 1131 answer = self.gateway_client.send_command(command) 1132 return_value roo 1 Reputation point. seattle aquarium octopus eats shark; how to add object to object array in typescript; 10 examples of homographs with sentences; callippe preserve golf course at The value can be either a pyspark.sql.types.DataType object or a DDL-formatted type string. 65 s = e.java_exception.toString(), /usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in Pig Programming: Apache Pig Script with UDF in HDFS Mode. org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193) At dataunbox, we have dedicated this blog to all students and working professionals who are aspiring to be a data engineer or data scientist. org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65) So udfs must be defined or imported after having initialized a SparkContext. This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. If an accumulator is used in a transformation in Spark, then the values might not be reliable. Step-1: Define a UDF function to calculate the square of the above data. at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) In most use cases while working with structured data, we encounter DataFrames. This UDF is now available to me to be used in SQL queries in Pyspark, e.g. Hoover Homes For Sale With Pool. Another way to show information from udf is to raise exceptions, e.g., def get_item_price (number, price This is really nice topic and discussion. at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at Found inside Page 104However, there was one exception: using User Defined Functions (UDFs); if a user defined a pure Python method and registered it as a UDF, under the hood, Now we have the data as follows, which can be easily filtered for the exceptions and processed accordingly. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) Now, instead of df.number > 0, use a filter_udf as the predicate. User defined function (udf) is a feature in (Py)Spark that allows user to define customized functions with column arguments. at eg : Thanks for contributing an answer to Stack Overflow! A simple try catch block at a place where an exception can occur would not point us to the actual invalid data, because the execution happens in executors which runs in different nodes and all transformations in Spark are lazily evaluated and optimized by the Catalyst framework before actual computation. An Apache Spark-based analytics platform optimized for Azure. rev2023.3.1.43266. In this example, we're verifying that an exception is thrown if the sort order is "cats". on cloud waterproof women's black; finder journal springer; mickey lolich health. the return type of the user-defined function. Lets create a state_abbreviation UDF that takes a string and a dictionary mapping as arguments: Create a sample DataFrame, attempt to run the state_abbreviation UDF and confirm that the code errors out because UDFs cant take dictionary arguments. Two UDF's we will create are . python function if used as a standalone function. To learn more, see our tips on writing great answers. An explanation is that only objects defined at top-level are serializable. Why was the nose gear of Concorde located so far aft? It supports the Data Science team in working with Big Data. df.createOrReplaceTempView("MyTable") df2 = spark_session.sql("select test_udf(my_col) as mapped from MyTable") +---------+-------------+ User defined function (udf) is a feature in (Py)Spark that allows user to define customized functions with column arguments. Show has been called once, the exceptions are : If the functions at A python function if used as a standalone function. For example, the following sets the log level to INFO. +---------+-------------+ Again as in #2, all the necessary files/ jars should be located somewhere accessible to all of the components of your cluster, e.g. rev2023.3.1.43266. You might get the following horrible stacktrace for various reasons. Northern Arizona Healthcare Human Resources, id,name,birthyear 100,Rick,2000 101,Jason,1998 102,Maggie,1999 104,Eugine,2001 105,Jacob,1985 112,Negan,2001. Sum elements of the array (in our case array of amounts spent). Suppose we want to calculate the total price and weight of each item in the orders via the udfs get_item_price_udf() and get_item_weight_udf(). The above can also be achieved with UDF, but when we implement exception handling, Spark wont support Either / Try / Exception classes as return types and would make our code more complex. // Note: Ideally we must call cache on the above df, and have sufficient space in memory so that this is not recomputed. Consider a dataframe of orderids and channelids associated with the dataframe constructed previously. We use the error code to filter out the exceptions and the good values into two different data frames. data-frames, Right now there are a few ways we can create UDF: With standalone function: def _add_one (x): """Adds one" "" if x is not None: return x + 1 add_one = udf (_add_one, IntegerType ()) This allows for full control flow, including exception handling, but duplicates variables. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at A pandas user-defined function (UDF)also known as vectorized UDFis a user-defined function that uses Apache Arrow to transfer data and pandas to work with the data. package com.demo.pig.udf; import java.io. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. This approach works if the dictionary is defined in the codebase (if the dictionary is defined in a Python project thats packaged in a wheel file and attached to a cluster for example). The lit() function doesnt work with dictionaries. 334 """ Italian Kitchen Hours, New in version 1.3.0. In the following code, we create two extra columns, one for output and one for the exception. 317 raise Py4JJavaError( org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152) org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687) 6) Explore Pyspark functions that enable the changing or casting of a dataset schema data type in an existing Dataframe to a different data type. org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336) at 321 raise Py4JError(, Py4JJavaError: An error occurred while calling o1111.showString. We do this via a udf get_channelid_udf() that returns a channelid given an orderid (this could be done with a join, but for the sake of giving an example, we use the udf). python function if used as a standalone function. Second, pandas UDFs are more flexible than UDFs on parameter passing. Comments are closed, but trackbacks and pingbacks are open. How do I use a decimal step value for range()? 2. at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at Since udfs need to be serialized to be sent to the executors, a Spark context (e.g., dataframe, querying) inside an udf would raise the above error. Power Meter and Circuit Analyzer / CT and Transducer, Monitoring and Control of Photovoltaic System, Northern Arizona Healthcare Human Resources. Consider a dataframe of orders, individual items in the orders, the number, price, and weight of each item. org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:797) Is the Dragonborn's Breath Weapon from Fizban's Treasury of Dragons an attack? pyspark package - PySpark 2.1.0 documentation Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file spark.apache.org Found inside Page 37 with DataFrames, PySpark is often significantly faster, there are some exceptions. The words need to be converted into a dictionary with a key that corresponds to the work and a probability value for the model. UDFs are a black box to PySpark hence it cant apply optimization and you will lose all the optimization PySpark does on Dataframe/Dataset. import pandas as pd. 320 else: Consider the same sample dataframe created before. Powered by WordPress and Stargazer. I'm currently trying to write some code in Solution 1: There are several potential errors in your code: You do not need to add .Value to the end of an attribute to get its actual value. Help me solved a longstanding question about passing the dictionary to udf. or via the command yarn application -list -appStates ALL (-appStates ALL shows applications that are finished). Observe that there is no longer predicate pushdown in the physical plan, as shown by PushedFilters: []. We are reaching out to the internal team to get more help on this, I will update you once we hear back from them. 2020/10/22 Spark hive build and connectivity Ravi Shankar. org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) Hi, In the current development of pyspark notebooks on Databricks, I typically use the python specific exception blocks to handle different situations that may arise. at java.lang.Thread.run(Thread.java:748), Driver stacktrace: at The process is pretty much same as the Pandas groupBy version with the exception that you will need to import pyspark.sql.functions. : The above can also be achieved with UDF, but when we implement exception handling, Spark wont support Either / Try / Exception classes as return types and would make our code more complex. Your UDF should be packaged in a library that follows dependency management best practices and tested in your test suite. (We use printing instead of logging as an example because logging from Pyspark requires further configurations, see here). The post contains clear steps forcreating UDF in Apache Pig. PySpark is a great language for performing exploratory data analysis at scale, building machine learning pipelines, and creating ETLs for a data platform. Vlad's Super Excellent Solution: Create a New Object and Reference It From the UDF. For column literals, use 'lit', 'array', 'struct' or 'create_map' function.. on a remote Spark cluster running in the cloud. However, Spark UDFs are not efficient because spark treats UDF as a black box and does not even try to optimize them. Call the UDF function. As Machine Learning and Data Science considered as next-generation technology, the objective of dataunbox blog is to provide knowledge and information in these technologies with real-time examples including multiple case studies and end-to-end projects. One such optimization is predicate pushdown. py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at The following are 9 code examples for showing how to use pyspark.sql.functions.pandas_udf().These examples are extracted from open source projects. returnType pyspark.sql.types.DataType or str. Here's an example of how to test a PySpark function that throws an exception. Hi, this didnt work for and got this error: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.core.multiarray._reconstruct). Now, we will use our udf function, UDF_marks on the RawScore column in our dataframe, and will produce a new column by the name of"<lambda>RawScore", and this will be a . Found inside Page 53 precision, recall, f1 measure, and error on test data: Well done! When a cached data is being taken, at that time it doesnt recalculate and hence doesnt update the accumulator. org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) Oatey Medium Clear Pvc Cement, Spark version in this post is 2.1.1, and the Jupyter notebook from this post can be found here. Does With(NoLock) help with query performance? The code snippet below demonstrates how to parallelize applying an Explainer with a Pandas UDF in PySpark. at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1.apply(BatchEvalPythonExec.scala:144) Null column returned from a udf. Cache and show the df again at Heres the error message: TypeError: Invalid argument, not a string or column: {'Alabama': 'AL', 'Texas': 'TX'} of type
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