How to create a big data frame in Python I have a sparse matrix, $X$, created by TfidfVectorizer and its size is $(500000, 200000)$. I want to convert $X$ to a data frame but I'm always getting a memory error.
I tried
pd.DataFrame(X.toarray(), columns=tokens)
and
pd.read_csv(X.toarray().astype(&quot;float32&quot;), columns=tokens, chunksize=...).
And it seems that when I convert $X$ to a numpy array using  X.toarray(), I get an error.
Can someone tell me what is an easy solution for this? Is there anyway I can create a sparse dataframe from $X$ without memory error?
I have been running my codes on Google Colab Pro and I think it provides me less than 100 GB Ram.
You can use pandas.Dataframe.sparse.from_spmatrix.  It will create a Dataframe populated by pd.arrays.SparseArray from a scipy sparse matrix.
Pandas used to have explicit sparse dataframes, but in more modern versions there is no such concept.  Only normal pd.Dataframe populated by sparse data.