pd.get_dummies() slow on large levels

I'm unsure if this is already the fastest possible method, or if I'm doing this inefficiently.

I want to hot encode a particular categorical column which has 27k+ possible levels. The column has different values in 2 different datasets, so I combined the levels first before using get_dummies()

def hot_encode_column_in_both_datasets(column_name,df,df2,sparse=True):
    col1b = set(df2[column_name].unique())
    col1a = set(df[column_name].unique())
    combined_cats = list(col1a.union(col1b))
    df[column_name] = df[column_name].astype('category', categories=combined_cats)
    df2[column_name] = df2[column_name].astype('category', categories=combined_cats)

    df = pd.get_dummies(df, columns=[column_name],sparse=sparse)
    df2 = pd.get_dummies(df2, columns=[column_name],sparse=sparse)
    try:
        del df[column_name]
        del df2[column_name]
    except:
        pass
    return df,df2

However, Its been running for more than 2 hours and it's still stuck hot encoding.

Could I be doing something wrongly here? Or is it just the nature of running it on large datasets?

Df has 6.8m rows and 27 columns, Df2 has 19990 rows and 27 columns before hot encoding the column that I wanted to.

Advice appreciated, thank you! :)

I reviewed the get_dummies source code briefly, and I think it may not be taking full advantage of the sparsity for your use case. The following approach may be faster, but I did not attempt to scale it all the way up to the 19M records you have:

import numpy as np
import pandas as pd
import scipy.sparse as ssp

np.random.seed(1)
N = 10000

dfa = pd.DataFrame.from_dict({
    'col1': np.random.randint(0, 27000, N)
    , 'col2b': np.random.choice([1, 2, 3], N)
    , 'target': np.random.choice([1, 2, 3], N)
    })

# construct an array of the unique values of the column to be encoded
vals = np.array(dfa.col1.unique())
# extract an array of values to be encoded from the dataframe
col1 = dfa.col1.values
# construct a sparse matrix of the appropriate size and an appropriate,
# memory-efficient dtype
spmtx = ssp.dok_matrix((N, len(vals)), dtype=np.uint8)
# do the encoding. NB: This is only vectorized in one of the two dimensions.
# Finding a way to vectorize the second dimension may yield a large speed up
for idx, val in enumerate(vals):
    spmtx[np.argwhere(col1 == val), idx] = 1

# Construct a SparseDataFrame from the sparse matrix and apply the index
# from the original dataframe and column names.
dfnew = pd.SparseDataFrame(spmtx, index=dfa.index,
                           columns=['col1_' + str(el) for el in vals])
dfnew.fillna(0, inplace=True)

UPDATE

Borrowing insights from other answers here and here, I was able to vectorize the solution in both dimensions. In my limited testing, I noted that constructing the SparseDataFrame seems to increase the execution time several fold. So, if you don't need to return a DataFrame-like object, you can save a lot of time. This solution also handles the case where you need to encode 2+ DataFrames into 2-d arrays with equal numbers of columns.

import numpy as np
import pandas as pd
import scipy.sparse as ssp

np.random.seed(1)
N1 = 10000
N2 = 100000

dfa = pd.DataFrame.from_dict({
    'col1': np.random.randint(0, 27000, N1)
    , 'col2a': np.random.choice([1, 2, 3], N1)
    , 'target': np.random.choice([1, 2, 3], N1)
    })

dfb = pd.DataFrame.from_dict({
    'col1': np.random.randint(0, 27000, N2)
    , 'col2b': np.random.choice(['foo', 'bar', 'baz'], N2)
    , 'target': np.random.choice([1, 2, 3], N2)
    })

# construct an array of the unique values of the column to be encoded
# taking the union of the values from both dataframes.
valsa = set(dfa.col1.unique())
valsb = set(dfb.col1.unique())
vals = np.array(list(valsa.union(valsb)), dtype=np.uint16)


def sparse_ohe(df, col, vals):
    """One-hot encoder using a sparse ndarray."""
    colaray = df[col].values
    # construct a sparse matrix of the appropriate size and an appropriate,
    # memory-efficient dtype
    spmtx = ssp.dok_matrix((df.shape[0], vals.shape[0]), dtype=np.uint8)
    # do the encoding
    spmtx[np.where(colaray.reshape(-1, 1) == vals.reshape(1, -1))] = 1

    # Construct a SparseDataFrame from the sparse matrix
    dfnew = pd.SparseDataFrame(spmtx, dtype=np.uint8, index=df.index,
                               columns=[col + '_' + str(el) for el in vals])
    dfnew.fillna(0, inplace=True)
    return dfnew

dfanew = sparse_ohe(dfa, 'col1', vals)
dfbnew = sparse_ohe(dfb, 'col1', vals)