Question: I want to create test data in a pyspark dataframe but I always get the same "tuple index out of range" error. I do not get this error when reading a csv. Would appreciate any thoughts on why I'm getting this error. The first thing I tried was create a pandas dataframe and convert it to a pyspark dataframe: columns = ["id","col_"] data = [("1", "blue"), ("2", "green"), ("3", "purple"), ("4", "red"), ("5", "yellow")] df = pd.DataFrame(data=data, columns=columns) sparkdf = spark.createDataFrame(df) sparkdf.show()  output: PicklingError: Could not serialize object: IndexError: tuple index out of range  I get the same error if I try to create the dataframe from RDD per SparkbyExamples.com instructions: rdd = spark.sparkContext.parallelize(data) sparkdf = spark.createDataFrame(rdd).toDF(*columns) sparkdf.show()  I also tried the following and got the same error: import pyspark.pandas as ps df1 = ps.from_pandas(df)  Here is the full error when running the above code: IndexError Traceback (most recent call last) File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\serializers.py:458, in CloudPickleSerializer.dumps(self, obj) 457 try: --> 458 return cloudpickle.dumps(obj, pickle_protocol) 459 except pickle.PickleError: File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle_fast.py:73, in dumps(obj, protocol, buffer_callback) 70 cp = CloudPickler( 71 file, protocol=protocol, buffer_callback=buffer_callback 72 ) ---> 73 cp.dump(obj) 74 return file.getvalue() File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle_fast.py:602, in CloudPickler.dump(self, obj) 601 try: --> 602 return Pickler.dump(self, obj) 603 except RuntimeError as e: File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle_fast.py:692, in CloudPickler.reducer_override(self, obj) 691 elif isinstance(obj, types.FunctionType): --> 692 return self._function_reduce(obj) 693 else: 694 # fallback to save_global, including the Pickler's 695 # dispatch_table File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle_fast.py:565, in CloudPickler._function_reduce(self, obj) 564 else: --> 565 return self._dynamic_function_reduce(obj) File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle_fast.py:546, in CloudPickler._dynamic_function_reduce(self, func) 545 newargs = self._function_getnewargs(func) --> 546 state = _function_getstate(func) 547 return (types.FunctionType, newargs, state, None, None, 548 _function_setstate) File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle_fast.py:157, in _function_getstate(func) 146 slotstate = { 147 "__name__": func.__name__, 148 "__qualname__": func.__qualname__, (...) 154 "__closure__": func.__closure__, 155 } --> 157 f_globals_ref = _extract_code_globals(func.__code__) 158 f_globals = {k: func.__globals__[k] for k in f_globals_ref if k in 159 func.__globals__} File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle.py:334, in _extract_code_globals(co) 331 # We use a dict with None values instead of a set to get a 332 # deterministic order (assuming Python 3.6+) and avoid introducing 333 # non-deterministic pickle bytes as a results. --> 334 out_names = {names[oparg]: None for _, oparg in _walk_global_ops(co)} 336 # Declaring a function inside another one using the "def ..." 337 # syntax generates a constant code object corresponding to the one 338 # of the nested function's As the nested function may itself need 339 # global variables, we need to introspect its code, extract its 340 # globals, (look for code object in it's co_consts attribute..) and 341 # add the result to code_globals File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\cloudpickle\cloudpickle.py:334, in <dictcomp>(.0) 331 # We use a dict with None values instead of a set to get a 332 # deterministic order (assuming Python 3.6+) and avoid introducing 333 # non-deterministic pickle bytes as a results. --> 334 out_names = {names[oparg]: None for _, oparg in _walk_global_ops(co)} 336 # Declaring a function inside another one using the "def ..." 337 # syntax generates a constant code object corresponding to the one 338 # of the nested function's As the nested function may itself need 339 # global variables, we need to introspect its code, extract its 340 # globals, (look for code object in it's co_consts attribute..) and 341 # add the result to code_globals IndexError: tuple index out of range During handling of the above exception, another exception occurred: PicklingError Traceback (most recent call last) Cell In [67], line 2 1 rdd = spark.sparkContext.parallelize(data) ----> 2 df1 = ps.from_pandas(df) 3 sparkdf = spark.createDataFrame(rdd).toDF(*columns) 4 #Create a dictionary from each row in col_ File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\pandas\namespace.py:153, in from_pandas(pobj) 151 return Series(pobj) 152 elif isinstance(pobj, pd.DataFrame): --> 153 return DataFrame(pobj) 154 elif isinstance(pobj, pd.Index): 155 return DataFrame(pd.DataFrame(index=pobj)).index File c:\Users\jonat\AppData\Local\Programs\Python\Python311\Lib\site-packages\pyspark\pandas\frame.py:450, in DataFrame.__init__(self, data, index, columns, dtype, copy) 448 else: 449 pdf = pd.DataFrame(data=data, index=index, columns=columns, dtype=dtype, copy=copy) --> 450 internal = InternalFrame.from_pandas(pdf) 452 object.__setattr__(self, "_internal_frame", internal) ... 466 msg = "Could not serialize object: %s: %s" % (e.__class__.__name__, emsg) 467 print_exec(sys.stderr) --> 468 raise pickle.PicklingError(msg) PicklingError: Could not serialize object: IndexError: tuple index out of range 

Answer: After doing some reading I checked https://pyreadiness.org/3.11 and it looks like the latest version of python is not supported by pyspark. I was able to resolve this problem by downgrading to python 3.9