Untitled5.ipynb

Untitled5.ipynb

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          "end_time": "2022-06-01T11:49:06.030822Z"
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      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-06-01T11:49:06.033663Z",
          "end_time": "2022-06-01T11:49:06.041484Z"
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      "cell_type": "code",
      "source": "nbetat=961",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
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          "start_time": "2022-06-01T11:49:06.045060Z",
          "end_time": "2022-06-01T11:49:06.114820Z"
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      },
      "cell_type": "code",
      "source": "NE_1=np.arange(0,100,1)\nNE=np.arange(0,100,1)\nmatr = pd.DataFrame(index=NE)\nfor b1 in NE_1:\n    matr[b1]=[0 for b2 in NE]\nprint(matr)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "    0   1   2   3   4   5   6   7   8   9   ...  90  91  92  93  94  95  96  \\\n0    0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n1    0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n2    0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n3    0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n4    0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..  ..   \n95   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n96   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n97   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n98   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n99   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n\n    97  98  99  \n0    0   0   0  \n1    0   0   0  \n2    0   0   0  \n3    0   0   0  \n4    0   0   0  \n..  ..  ..  ..  \n95   0   0   0  \n96   0   0   0  \n97   0   0   0  \n98   0   0   0  \n99   0   0   0  \n\n[100 rows x 100 columns]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-06-01T11:49:06.120291Z",
          "end_time": "2022-06-01T11:49:06.703146Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "NE_1=np.arange(0,nbetat,1)\nNE=np.arange(0,100,1)\nmatr = pd.DataFrame(index=NE)\nfor b1 in NE_1:\n    matr[b1]=[0 for b2 in NE]\nprint(matr)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "    0    1    2    3    4    5    6    7    8    9    ...  951  952  953  954  \\\n0     0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n1     0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n2     0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n3     0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n4     0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n..  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n95    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n96    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n97    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n98    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n99    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n\n    955  956  957  958  959  960  \n0     0    0    0    0    0    0  \n1     0    0    0    0    0    0  \n2     0    0    0    0    0    0  \n3     0    0    0    0    0    0  \n4     0    0    0    0    0    0  \n..  ...  ...  ...  ...  ...  ...  \n95    0    0    0    0    0    0  \n96    0    0    0    0    0    0  \n97    0    0    0    0    0    0  \n98    0    0    0    0    0    0  \n99    0    0    0    0    0    0  \n\n[100 rows x 961 columns]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-06-01T11:49:06.707266Z",
          "end_time": "2022-06-01T11:49:06.859412Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "NE_1=np.arange(0,100,1)\nNE=np.arange(0,nbetat,1)\nmatr = pd.DataFrame(index=NE)\nfor b1 in NE_1:\n    matr[b1]=[0 for b2 in NE]\nprint(matr)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "     0   1   2   3   4   5   6   7   8   9   ...  90  91  92  93  94  95  96  \\\n0     0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n1     0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n2     0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n3     0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n4     0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..  ..   \n956   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n957   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n958   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n959   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n960   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n\n     97  98  99  \n0     0   0   0  \n1     0   0   0  \n2     0   0   0  \n3     0   0   0  \n4     0   0   0  \n..   ..  ..  ..  \n956   0   0   0  \n957   0   0   0  \n958   0   0   0  \n959   0   0   0  \n960   0   0   0  \n\n[961 rows x 100 columns]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
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      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
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      "display_name": "Python 3",
      "language": "python"
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    "language_info": {
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