Create SciPy sparse matrix from pandas SparseDataFrame

sparseDF_to_scipy.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.sparse as sps"
   ]
  },
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   "execution_count": 2,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_a</th>\n",
       "      <th>A_b</th>\n",
       "      <th>A_c</th>\n",
       "      <th>A_d</th>\n",
       "      <th>B_x</th>\n",
       "      <th>B_y</th>\n",
       "      <th>B_z</th>\n",
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      "text/plain": [
       "   A_a  A_b  A_c  A_d  B_x  B_y  B_z\n",
       "0    1    0    0    0    1    0    0\n",
       "1    0    1    0    0    1    0    0\n",
       "2    0    1    0    0    0    1    0\n",
       "3    1    0    0    0    0    1    0\n",
       "4    0    0    1    0    0    0    1\n",
       "5    0    0    0    1    0    0    1\n",
       "6    0    0    1    0    1    0    0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "df = pd.get_dummies(pd.DataFrame(dict(A=list('abbacdc'), B=list('xxyyzzx'))), sparse=True)\n",
    "df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.sparse.frame.SparseDataFrame"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "type(df)"
   ]
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   "source": [
    "all_series = [df[col] for col in df]\n",
    "# Each column is a SparseSeries\n",
    "data = np.concatenate([s.sp_values for s in all_series])\n",
    "indices = np.concatenate([s.sp_index.indices for s in all_series])\n",
    "indptr = np.cumsum([0] + [s.sp_index.indices.shape[0] for s in all_series])\n",
    "M = sps.csc_matrix((data, indices, indptr))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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     "data": {
      "text/plain": [
       "(7, 7)"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "M.shape"
   ]
  },
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    {
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       "14"
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     "execution_count": 6,
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   "source": [
    "M.nnz"
   ]
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   "execution_count": 7,
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     "data": {
      "text/plain": [
       "matrix([[1, 0, 0, 0, 1, 0, 0],\n",
       "        [0, 1, 0, 0, 1, 0, 0],\n",
       "        [0, 1, 0, 0, 0, 1, 0],\n",
       "        [1, 0, 0, 0, 0, 1, 0],\n",
       "        [0, 0, 1, 0, 0, 0, 1],\n",
       "        [0, 0, 0, 1, 0, 0, 1],\n",
       "        [0, 0, 1, 0, 1, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "M.todense()"
   ]
  }
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