Can you post an example that we can copy/paste to replicate the behavior

I already posted the example in quotes. It is:

Import pandas

p_csv= pandas.read_csv(("/myDir/myFile.csv"), index_col=0)
pandas.get_dummies(p_csv, prefix="SEPARATOR_", columns="SEPARATOR")
p_csv.to_csv("/myDir/myNew.csv")

That code sample is not self-contained, so no one can copy / paste to replicate your issue. The below link might help:

http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

alright. here is a copy-pastable version with random data

###CODE

import pandas, numpy as np

columns = np.random.rand(145)
columns = columns.astype(str)
columns[110] = "SEPARATOR"
columns[0] = "INSTANCES"

separators = ["and", "clique", "gomory", "gomory1", "gomory2", "gomory3", "gomory4", "zerohalf"]
instances = ["021erkdjfiejrk", "5484ierdkj", "5487ehej"]

d = {}

for i in range(145):
    d[columns[i]]= np.random.rand(145000)

d["SEPARATOR"] = [np.random.choice(instances) for i in range(145000)]
d["INSTANCES"] = [np.random.choice(instances) for i in range(145000)]

p_csv= pd.DataFrame(d)
p_csv["SEPARATOR"]
print("getting dummies", p_csv.shape)
pd.get_dummies(p_csv, prefix="SEPARATOR_", columns="SEPARATOR")
print("success")

That is liable to produce a DataFrame with shape on the order of (145000, 145000), which I think is on the order of 80GB, which certainly won't fit on my laptop. SIGKILL is likely coming from the OOM killer.

that is what I guessed what probably happened. But why would the api function do that when there are only 6 different values for separator?

(edited your comment to make it copy/paste more neatly, LMK if you object and I'll revert)

do you get the expected result if you pass columns=["SEPARATOR"] instead of columns="SEPARATOR"?

Yes! That worked, very fast too. Now that you mention it, I do see that the expected parameter is "list-like". Didn't expect that to be a problem, since I didn't get any error and also do get an error if I pass 'columns="fake_column"'

I think this should raise if columns is a string to avoid confusion like this. @TonyCongqianWang any interest in submitting a PR for that?

I will take a shot at this.

@WillAyd I created a pull request for this validation, but I wanted to confirm one thing. If columns is not list_like then should it raise error or warning?
I referred to the code in reshape/utils.py and it raised error there. Would like to verify if I am correctly interpreting it.

Yea I think that would be good to emulate - nice find!