##Features:

• Create an ARFF dump file from an existing pandas dataframe
• Imports column names straight from the pandas dataframe
• 'Object' dtype in pandas is treated as 'nominal'
• If your dataframe has a column called "Class" / "class" / "CLASS", then this code automatically makes it a nominal value
• Cleans up NaNs, dirty strings, etc

##Usage:

pandas2arff(df,filename,wekaname = "pandasdata",cleanstringdata=True,cleannan=True)

where,

• df: dataframe in pandas format (flattened, no groupings)
• filename: the filename you want the weka compatible file to be in
• wekaname: the name you want to give to the weka dataset (this will be visible to you when you open it in Weka)
• cleanstringdata: clean up data which may have spaces and replace with "_", special characters etc which seem to annoy Weka. To suppress this, set this to False.
• cleannan: replaces all nan values with "?" which is Weka's standard for missing values. To suppress this, set this to False.

##Quick Example: Using the sample files,

df = pd.read_csv("foo.txt",na_values="NA")
pandas2arff(df,"foo.arff")

Literally just these two lines!

##What's not supported (yet):

• dates as variables

• any other popular use-cases you might suggest!