transform scipy sparse csr to pandas?

If A is csr_matrix, you can use .toarray() (there's also .todense() that produces a numpy matrix, which is also works for the DataFrame constructor):

df = pd.DataFrame(A.toarray())

You can then use this with pd.concat().

A = csr_matrix([[1, 0, 2], [0, 3, 0]])
    
  (0, 0)    1
  (0, 2)    2
  (1, 1)    3

<class 'scipy.sparse.csr.csr_matrix'>

pd.DataFrame(A.todense())

   0  1  2
0  1  0  2
1  0  3  0

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 3 columns):
0    2 non-null int64
1    2 non-null int64
2    2 non-null int64

In version 0.20, pandas introduced sparse data structures, including the SparseDataFrame.

In pandas 1.0, SparseDataFrame was removed:

In older versions of pandas, the SparseSeries and SparseDataFrame classes were the preferred way to work with sparse data. With the advent of extension arrays, these subclasses are no longer needed. Their purpose is better served by using a regular Series or DataFrame with sparse values instead.

The migration guide shows how to use these new data structures.

For instance, to create a DataFrame from a sparse matrix:

from scipy.sparse import csr_matrix

A = csr_matrix([[1, 0, 2], [0, 3, 0]])

df = pd.DataFrame.sparse.from_spmatrix(A, columns=['A', 'B', 'C'])

df

   A  B  C
0  1  0  2
1  0  3  0

df.dtypes
A    Sparse[float64, 0]
B    Sparse[float64, 0]
C    Sparse[float64, 0]
dtype: object

Alternatively, you can pass sparse matrices to sklearn to avoid running out of memory when converting back to pandas. Just convert your other data to sparse format by passing a numpy array to the scipy.sparse.csr_matrix constructor and use scipy.sparse.hstack to combine (see docs).

UPDATE for Pandas 1.0+

Per the Pandas Sparse data structures documentation, SparseDataFrame and SparseSeries have been removed.

Sparse Pandas Dataframes

Previous Way

pd.SparseDataFrame({"A": [0, 1]})

New Way

pd.DataFrame({"A": pd.arrays.SparseArray([0, 1])})

Working with SciPy sparse csr_matrix

Previous Way

from scipy.sparse import csr_matrix
matrix = csr_matrix((3, 4), dtype=np.int8)
df = pd.SparseDataFrame(matrix, columns=['A', 'B', 'C'])

New Way

from scipy.sparse import csr_matrix
import numpy as np
import pandas as pd

matrix = csr_matrix((3, 4), dtype=np.int8)
df = pd.DataFrame.sparse.from_spmatrix(matrix, columns=['A', 'B', 'C', 'D'])
df.dtypes

Output:

A    Sparse[int8, 0]
B    Sparse[int8, 0]
C    Sparse[int8, 0]
D    Sparse[int8, 0]
dtype: object

Conversion from Sparse to Dense

df.sparse.to_dense()                                                                                                                                                                            

Output:

   A  B  C  D
0  0  0  0  0
1  0  0  0  0
2  0  0  0  0

Sparse Properties

df.sparse.density                                                                                                                                                                           

Output:

0.0

You could also avoid getting back a sparse matrix in the first place by setting the parameter sparse to False when creating the Encoder.

The documentation of the OneHotEncoder states:

sparse : boolean, default=True

Will return sparse matrix if set True else will return an array.

Then you can again call the DataFrame constructor to transform the numpy array to a DataFrame.