From 60ffe12db45305ab6dee112069d00b33cc62cfc5 Mon Sep 17 00:00:00 2001 From: Saran Ahluwalia Date: Mon, 13 Dec 2021 09:26:59 -0500 Subject: [PATCH] without not computed fields --- ...een_methodologies-ranking-percentile.ipynb | 86 +++++++++++++++++-- 1 file changed, 81 insertions(+), 5 deletions(-) diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb index 11222854..424c5649 100644 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb @@ -767,6 +767,18 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", + " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", + " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", + ")" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -1095,13 +1107,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1',\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" + " 'current_summed_methodology', 'T8_est1', \n", + " \"current_methodology_denominator_sans_not_computed\",\n", + " 'current_methodology_denominator', 'current_methodology_percent']]" ] }, { @@ -1113,14 +1126,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/0m/ppxy6yr56jx1mk52p_9sf2sw0000gn/T/ipykernel_69620/1675757007.py:1: SettingWithCopyWarning: \n", + "/var/folders/0m/ppxy6yr56jx1mk52p_9sf2sw0000gn/T/ipykernel_69620/476531487.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1135,6 +1148,30 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/0m/ppxy6yr56jx1mk52p_9sf2sw0000gn/T/ipykernel_69620/394307455.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n" + ] + } + ], + "source": [ + "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", + " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", + ")" + ] + }, { "cell_type": "code", "execution_count": 15, @@ -1174,6 +1211,45 @@ "sns.histplot(final_df[\"differences_aggregate_denominator\"])" ] }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", + "# Set x-axis label\n", + "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", + "# Set y-axis label\n", + "plt.ylabel('Relative Frequency in Support')\n", + "\n", + "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" + ] + }, { "cell_type": "markdown", "metadata": {},