pandas_combining_tables.ipynb

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      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "games1 = {\n",
        "   \"type\": [\"Puzzle\",\"casino\",\"stratergy\"],\n",
        "   \"downloads\":[12300,10000,9800]\n",
        "}\n",
        "\n",
        "games2 = {\n",
        "    \"type\":[\"RPG\",\"simulation\",\"shooter\"],\n",
        "    \"downloads\":[17920,9245,10600]\n",
        "}"
      ],
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      "execution_count": 5,
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      "source": [
        "df1 = pd.DataFrame(games1)\n",
        "df2 = pd.DataFrame(games2)"
      ],
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        "id": "m1ka2SbDLLHV"
      },
      "execution_count": 8,
      "outputs": []
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      "source": [
        "df = pd.concat([df1,df2],axis = 0)"
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      "execution_count": 10,
      "outputs": []
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        "df.head()"
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            "text/plain": [
              "         type  downloads\n",
              "0      Puzzle      12300\n",
              "1      casino      10000\n",
              "2   stratergy       9800\n",
              "0         RPG      17920\n",
              "1  simulation       9245"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>type</th>\n",
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        "games3 = {\n",
        "    \"type\":[\"Puzzle\",\"casino\",\"stratergy\",\"RPG\",\"simulation\",\"shooter\"],\n",
        "    \"revenue\":[12900,190000,993800,179290,924353,103600  ]\n",
        "}"
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        "df3 = pd.DataFrame(games3)"
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        "new_df = pd.merge(df,df3,on = \"type\")"
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            "text/plain": [
              "         type  downloads  revenue\n",
              "0      Puzzle      12300    12900\n",
              "1      casino      10000   190000\n",
              "2   stratergy       9800   993800\n",
              "3         RPG      17920   179290\n",
              "4  simulation       9245   924353\n",
              "5     shooter      10600   103600"
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