Multi-Column Explode idea

multiexplode2.ipynb

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        "import pandas as pd"
      ],
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      "source": [
        "Toy Expample"
      ],
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        "d = {\"A\":[1,2,3],\n",
        "     \"B\":[\"a\",\"b\",\"c\"],\n",
        "     \"C\":[[1,2],[3,4],[5,6]],\n",
        "     \"D\":[[4,5],[6,7],[8,9]], }\n",
        "df = pd.DataFrame.from_dict(d)\n",
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 66,
          "data": {
            "text/plain": "   A  B       C       D\n0  1  a  [1, 2]  [4, 5]\n1  2  b  [3, 4]  [6, 7]\n2  3  c  [5, 6]  [8, 9]",
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    {
      "cell_type": "markdown",
      "source": [
        "Exploding one after the other does not yield the correct result"
      ],
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      "cell_type": "code",
      "source": [
        "df.explode(\"C\").explode(\"D\")"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 67,
          "data": {
            "text/plain": "   A  B  C  D\n0  1  a  1  4\n0  1  a  1  5\n0  1  a  2  4\n0  1  a  2  5\n1  2  b  3  6\n1  2  b  3  7\n1  2  b  4  6\n1  2  b  4  7\n2  3  c  5  8\n2  3  c  5  9\n2  3  c  6  8\n2  3  c  6  9",
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      "source": [
        "This code snippet mirrors the solution in the existing explode"
      ],
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      "source": [
        "def explode_multiple_columns(df, cols_to_explode):\n",
        "    result = pd.DataFrame({col: df[col].explode() for col in df[cols_to_explode]})\n",
        "    return (\n",
        "        df.drop(cols_to_explode, axis=1)\n",
        "        .join(result)\n",
        "    )"
      ],
      "outputs": [],
      "execution_count": 68,
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      "source": [
        "And it yields the correct result"
      ],
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      "source": [
        "cols_to_explode = [\"C\", \"D\"]\n",
        "explode_multiple_columns(df, cols_to_explode )"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/plain": "   A  B  C  D\n0  1  a  1  4\n0  1  a  2  5\n1  2  b  3  6\n1  2  b  4  7\n2  3  c  5  8\n2  3  c  6  9",
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    {
      "cell_type": "markdown",
      "source": [
        "So what if the lists are not the same length?"
      ],
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      "source": [
        "d = {\"A\":[1,2,3],\n",
        "     \"B\":[\"a\",\"b\",\"c\"],\n",
        "     \"C\":[[1,2],[3,4],[5,6]],\n",
        "     \"D\":[[4,5,9],[6,7],[8]], }\n",
        "df = pd.DataFrame.from_dict(d)\n",
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 70,
          "data": {
            "text/plain": "   A  B       C          D\n0  1  a  [1, 2]  [4, 5, 9]\n1  2  b  [3, 4]     [6, 7]\n2  3  c  [5, 6]        [8]",
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      "cell_type": "markdown",
      "source": [
        "We fill them up..."
      ],
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      "cell_type": "code",
      "source": [
        "def explode_multiple_columns2(df, cols_to_explode):\n",
        "    exploded_list = []\n",
        "    for col in df[cols_to_explode]:\n",
        "      df2 = df[col].explode().reset_index()\n",
        "      df2[\"ixix\"] = df2.groupby(['index']).cumcount()\n",
        "      exploded_list.append(df2)\n",
        "  \n",
        "    #exploded_list = [df[col].explode().reset_index().reset_index() for col in df[cols_to_explode]]\n",
        "    from functools import reduce\n",
        "    # this is a bit ugly because I'm using the default names as a hack...\n",
        "    result =  reduce(lambda x, y: pd.merge_ordered(x, y, on = ['index', \"ixix\"], how=\"outer\"), exploded_list).set_index(\"index\").drop(\"ixix\", axis=1)\n",
        "    return df.drop(cols_to_explode, axis=1).join(result)"
      ],
      "outputs": [],
      "execution_count": 71,
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      "cell_type": "code",
      "source": [
        "explode_multiple_columns2(df, cols_to_explode)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 72,
          "data": {
            "text/plain": "   A  B    C    D\n0  1  a    1    4\n0  1  a    2    5\n0  1  a  NaN    9\n1  2  b    3    6\n1  2  b    4    7\n2  3  c    5    8\n2  3  c    6  NaN",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Works for 3 Columns..."
      ],
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      "cell_type": "code",
      "source": [
        "d = {\"A\":[1,2,3],\n",
        "     \"B\":[\"a\",\"b\",\"c\"],\n",
        "     \"C\":[[1,2],[3,4],[5,6]],\n",
        "     \"D\":[[4,5,9],[6,7],[8]],\n",
        "    \"E\":[[49,59,99],[79],[89, 99]], }\n",
        "df = pd.DataFrame.from_dict(d)\n",
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 73,
          "data": {
            "text/plain": "   A  B       C          D             E\n0  1  a  [1, 2]  [4, 5, 9]  [49, 59, 99]\n1  2  b  [3, 4]     [6, 7]          [79]\n2  3  c  [5, 6]        [8]      [89, 99]",
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    {
      "cell_type": "code",
      "source": [
        "explode_multiple_columns2(df, [\"C\", \"D\", \"E\"])"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 74,
          "data": {
            "text/plain": "   A  B    C    D    E\n0  1  a    1    4   49\n0  1  a    2    5   59\n0  1  a  NaN    9   99\n1  2  b    3    6   79\n1  2  b    4    7  NaN\n2  3  c    5    8   89\n2  3  c    6  NaN   99",
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Lists with different length don't seem to explode and fill up NaN correctly

Damn, you're right! I can't figure out how to make it work without a loop then though...
I updated the notebook so that it does the thing now!