From b458fc6a4330147642ed7195a6ee0dbbf251138e Mon Sep 17 00:00:00 2001 From: Lillian Oostrom Date: Mon, 16 May 2022 17:23:06 +0200 Subject: [PATCH] voting poll count done --- jupyter/stembureau_data.ipynb | 54 ++++++++++++++++++++++++++++++++++- 1 file changed, 53 insertions(+), 1 deletion(-) diff --git a/jupyter/stembureau_data.ipynb b/jupyter/stembureau_data.ipynb index ce73ace..5537e72 100644 --- a/jupyter/stembureau_data.ipynb +++ b/jupyter/stembureau_data.ipynb @@ -6289,6 +6289,58 @@ "pd.DataFrame(open).pivot(index='dagen', columns=['staffel'], values=['aantal']).plot(kind=\"bar\", stacked=True, figsize=(13,13), log=True)" ] }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 266, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "amount = {\n", + " ' ': [],\n", + " 'aantal': [],\n", + " 'staffel': []\n", + " }\n", + "\n", + "def graph_count(df, label):\n", + " amount[' '].append(\"Stemlokaal\")\n", + " amount['aantal'].append(df[\"UUID\"].count())\n", + " amount['staffel'].append(label)\n", + "\n", + "def make_graph_count(label):\n", + " df_stemger_label = df_stemger_clean[df_stemger_clean['binned'].str.fullmatch(label)]\n", + " graph_count(filter_binned(gdf_wims_dedupe, df_stemger_label), label)\n", + "\n", + "for label in labels: \n", + " make_graph_count(label)\n", + "\n", + "\n", + "\n", + "pd.DataFrame(amount).pivot(index=' ', columns=['staffel'], values=['aantal']).plot(kind=\"bar\", stacked=True, figsize=(13,13))\n" + ] + }, { "cell_type": "code", "execution_count": 261, @@ -6362,7 +6414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 262, "metadata": {}, "outputs": [], "source": [