Graphs.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                "import seaborn as sns\r\n",
                "import pandas as pd\r\n",
                "import matplotlib.pyplot as plt"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "source": [
                "sns.set(style='ticks', palette='Set2')\r\n",
                "sns.despine()"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "source": [
                "ScoredMasterCSVtest = pd.read_csv('C:/Users/Jonathan/Documents/GitHub/ConservativeSentimentAnalysis/master_scored_queried.csv')"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "C:\\Users\\Jonathan\\Miniconda3\\envs\\ConservativeSentimentAnalysis\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3441: DtypeWarning: Columns (11,12,35) have mixed types.Specify dtype option on import or set low_memory=False.\n",
                        "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "source": [
                "# reverse one-hot encoding\r\n",
                "ScoredMasterCSVtest['Sex'] = (ScoredMasterCSVtest.iloc[:, 41:] == 1).idxmax(1)\r\n",
                "\r\n",
                "# actuals and predicted to one column\r\n",
                "df = ScoredMasterCSVtest.melt(id_vars ='Sex', value_vars=['Sentiment Score'])\r\n",
                "\r\n",
                "print(df.head(5))\r\n",
                "\r\n",
                "sns.boxplot(x='Sex', y='value', data=df)\r\n",
                "plt.show()\r\n",
                "\r\n",
                "sns.histplot(data=df, x='value', hue=\"Sex\", bins = 40)\r\n",
                "#sns.histplot(data=df, x='value', hue=\"Sex\", bins = 40, kde=True)\r\n",
                "plt.show()\r\n",
                "#sns.scatterplot(x='Sex', y='value', data=df)\r\n"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "   Sex         variable   value\n",
                        "0  she  Sentiment Score  0.5927\n",
                        "1  she  Sentiment Score  0.5927\n",
                        "2  she  Sentiment Score -0.5423\n",
                        "3  she  Sentiment Score  0.5927\n",
                        "4  she  Sentiment Score -0.5423\n"
                    ]
                },
                {
                    "output_type": "display_data",
                    "data": {
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"
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