Solved with:

$ pip install --upgrade pandas

Please check here to write pandas dataframe as parquet

df.to_parquet('df.parquet.gzip',
              compression='gzip') 

Your code as it stands is trying to create a parquet file from a dataframe called df_name which is passed as an argument. What it receives instead of a dataframe is a string, so it fails. The ideal scenario here is that instead of passing in the string name of a dataframe, you pass in the object, like so:

df = ... # define a dataframe
filename = ... # some filename
parquet_create(df, filename)

It sounds like that's not an option for you for some reason, so there are a couple of workarounds. You can create a dictionary relating string dataframe names to dataframe objects like this:

df = ... # define a dataframe
df2 = ... # define another dataframe
filename = ... # some filename
name_map = {'df': df, 'df2': df2}
parquet_create('df', filename)

and define parquet_create like this:

def parquet_create(df_name, file_name):
    name_map[df_name].write.mode('overwrite').parquet(file_name+".parquet")

You will have to ensure that name_map is defined in the scope of parquet_create.

The only other option I can think of is using eval:

df = ... # define a dataframe
filename = ... # some filename
parquet_create(df, filename)

def parquet_create(df_name, file_name):
    eval(df_name).write.mode('overwrite').parquet(file_name+".parquet")

Note that you will also have to make sure that df is in the scope of parquet_create for this solution as well.

Both of these are really ugly solutions in my mind, and I honestly can't think of a reason not to just pass in the dataframe object itself, but there you go.

Indeed, one of the causes of the issue appears to be dependent on incorrect file access path. Providing correct path solves it.

The other one seems to depend on mismatch between pyarrow and fastparquet load/save versions. This behavior however is not consistent (or I was not able to pin-point it across different versions) and depends on the version of the packages (very annoying). For example: at some point save had to occur with pyarrow and load with fastparquet. At some point, after package updates, both load and save started relying on the same version. Strange.

Additionally, I found there seems to be a GitHub issues that refers to the same issue, but different root-cause. Either way, if the path is correct, the update of the packages to "matching" versions should solve the issue.

That way of merging is creating a Pandas DataFrame. Try this:

import geopandas as gpd

df = gpd.read_file(r"/home/bera/Drives/data1/data/LMV/LMV-data_2021-10/ok_riks_Sweref_99_TM_shape/oversikt/riks/ak_riks.shp")

gdf1 = df.head(10)
gdf2 = df.tail(10)

print(type(gdf1.merge(gdf2, on="LANSKOD", how="outer"))) #This is creating a pandas df
#pandas.core.frame.DataFrame

merged = gpd.pd.merge(gdf1, gdf2, on="LANSKOD", how="outer")
print(type(merged))
#<class 'geopandas.geodataframe.GeoDataFrame'> #Now it is a geopandas df

merged["geometry"] = merged["geometry_x"].combine_first(merged["geometry_y"]) #Create a complete geometry column
merged = merged.drop(columns=["geometry_x", "geometry_y"])
merged.to_file(r"/home/bera/Desktop/GIStest/testmerge.shp")

I've just run into the same issue, but I assume you've resolved yours. In case you haven't or someone else comes across this with a similar issue, try creating a pyarrow table from the dataframe first.

import pyarrow as pa
import pyarrow.parquet as pq
    
df = {some dataframe}
table = pa.Table.from_pandas(df)
pq.write_table(table, '{path}')

Pandas has a core function to_parquet(). Just write the dataframe to parquet format like this:

df.to_parquet('myfile.parquet')

You still need to install a parquet library such as fastparquet. If you have more than one parquet library installed, you also need to specify which engine you want pandas to use, otherwise it will take the first one to be installed (as in the documentation). For example:

df.to_parquet('myfile.parquet', engine='fastparquet')