From 1b76a6883839af35958c42bb217daaa0fa5c184a Mon Sep 17 00:00:00 2001
From: Emma Nechamkin <97977170+emma-nechamkin@users.noreply.github.com>
Date: Thu, 17 Feb 2022 16:53:04 -0500
Subject: [PATCH] FEMA data check (#1270)
we wanted to implement a slightly different FEMA AG LOSS indicator. Here, we take the 90th percentile only of tracts that have agvalue, and then we also floor the denominator of the rate calculation (loss/total value) at $408k
---
.../data_pipeline/etl/score/etl_score.py | 47 +-
.../etl/sources/national_risk_index/etl.py | 27 +-
.../ipython/agricultural_loss_indicator.ipynb | 996 +++++
.../ipython/scoring_comparison.ipynb | 3637 ++++++++++++++++-
.../data_pipeline/score/field_names.py | 1 +
.../national_risk_index/data/output.csv | 112 +-
.../national_risk_index/data/transform.csv | 112 +-
.../sources/national_risk_index/test_etl.py | 1 +
8 files changed, 4642 insertions(+), 291 deletions(-)
create mode 100644 data/data-pipeline/data_pipeline/ipython/agricultural_loss_indicator.ipynb
diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py
index facd8ef6..7af5cd28 100644
--- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py
+++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py
@@ -198,6 +198,7 @@ class ScoreETL(ExtractTransformLoad):
return df
+ # Just a note -- it would be faster (I think) to set geoid10_tract as the index and then concat all frames 1x
census_tract_df = functools.reduce(
merge_function,
census_tract_dfs,
@@ -286,11 +287,45 @@ class ScoreETL(ExtractTransformLoad):
something like "3rd grade reading proficiency" and `output_column_name_root`
may be something like "Low 3rd grade reading proficiency".
"""
- # Create the "basic" percentile.
- df[
- f"{output_column_name_root}"
- f"{field_names.PERCENTILE_FIELD_SUFFIX}"
- ] = df[input_column_name].rank(pct=True, ascending=ascending)
+ if (
+ output_column_name_root
+ != field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD
+ ):
+ # Create the "basic" percentile.
+ df[
+ f"{output_column_name_root}"
+ f"{field_names.PERCENTILE_FIELD_SUFFIX}"
+ ] = df[input_column_name].rank(pct=True, ascending=ascending)
+
+ else:
+ # For agricultural loss, we are using whether there is value at all to determine percentile
+ # This is not the most thoughtfully written code, but it works.
+
+ # Take only rows with agrivalue
+ tmp_df = df[df[field_names.AGRICULTURAL_VALUE_BOOL_FIELD] == 1][
+ [input_column_name, field_names.GEOID_TRACT_FIELD]
+ ].copy()
+
+ # Construct a percentile only among those tracts
+ tmp_df["temporary_ranking"] = tmp_df[input_column_name].transform(
+ lambda x: x.rank(pct=True, ascending=True)
+ )
+
+ # # Create a map for just those tracts and map it onto the df
+ temporary_ranking = tmp_df.set_index(field_names.GEOID_TRACT_FIELD)[
+ "temporary_ranking"
+ ].to_dict()
+
+ df[
+ f"{output_column_name_root}"
+ f"{field_names.PERCENTILE_FIELD_SUFFIX}"
+ ] = np.where(
+ df[field_names.AGRICULTURAL_VALUE_BOOL_FIELD].isna(),
+ np.nan,
+ df[field_names.GEOID_TRACT_FIELD]
+ .map(temporary_ranking)
+ .fillna(0),
+ )
# Create the urban/rural percentiles.
urban_rural_percentile_fields_to_combine = []
@@ -433,6 +468,8 @@ class ScoreETL(ExtractTransformLoad):
field_names.EXTREME_HEAT_FIELD,
field_names.HEALTHY_FOOD_FIELD,
field_names.IMPENETRABLE_SURFACES_FIELD,
+ # We have to pass this boolean here in order to include it in ag value loss percentiles.
+ field_names.AGRICULTURAL_VALUE_BOOL_FIELD,
]
non_numeric_columns = [
diff --git a/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py b/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py
index 4876974d..cdf00af3 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py
@@ -19,6 +19,13 @@ class NationalRiskIndexETL(ExtractTransformLoad):
SOURCE_URL = "https://hazards.fema.gov/nri/Content/StaticDocuments/DataDownload//NRI_Table_CensusTracts/NRI_Table_CensusTracts.zip"
GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT
+ ## TEMPORARILY HERE
+ ## To get this value up in time for launch, we've hard coded it. We would like
+ ## to, in the future, have this pull the 10th percentile (or nth percentile)
+ ## from the agrivalue data for rural tracts.
+ # This is defined as roughly the 10th percentile for "rural tracts"
+ AGRIVALUE_LOWER_BOUND = 408000
+
def __init__(self):
self.INPUT_CSV = self.get_tmp_path() / "NRI_Table_CensusTracts.csv"
@@ -50,6 +57,7 @@ class NationalRiskIndexETL(ExtractTransformLoad):
self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME = (
"Expected population loss rate (Natural Hazards Risk Index)"
)
+ self.CONTAINS_AGRIVALUE = "Contains agricultural value"
self.COLUMNS_TO_KEEP = [
self.GEOID_TRACT_FIELD_NAME,
@@ -57,6 +65,7 @@ class NationalRiskIndexETL(ExtractTransformLoad):
self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME,
self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME,
self.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME,
+ self.CONTAINS_AGRIVALUE,
]
self.df: pd.DataFrame
@@ -150,10 +159,20 @@ class NationalRiskIndexETL(ExtractTransformLoad):
/ df_nri[self.POPULATION_INPUT_FIELD_NAME]
)
- # Agriculture EAL Rate = Eal Vala / Agrivalue
- df_nri[self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME] = (
- disaster_agriculture_sum_series
- / df_nri[self.AGRICULTURAL_VALUE_INPUT_FIELD_NAME]
+ # Agriculture EAL Rate = Eal Vala / max(Agrivalue, 408000)
+ ## FORMULA ADJUSTMENT 2/17
+ ## Because AGRIVALUE contains a lot of 0s, we are going to consider
+ ## 90th percentile only for places that have some agrivalue at all
+ df_nri[
+ self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME
+ ] = disaster_agriculture_sum_series / df_nri[
+ self.AGRICULTURAL_VALUE_INPUT_FIELD_NAME
+ ].clip(
+ lower=self.AGRIVALUE_LOWER_BOUND
+ )
+ # This produces a boolean that is True in the case of non-zero agricultural value
+ df_nri[self.CONTAINS_AGRIVALUE] = (
+ df_nri[self.AGRICULTURAL_VALUE_INPUT_FIELD_NAME] > 0
)
# divide EAL_VALB (Expected Annual Loss - Building Value) by BUILDVALUE (Building Value ($)).
diff --git a/data/data-pipeline/data_pipeline/ipython/agricultural_loss_indicator.ipynb b/data/data-pipeline/data_pipeline/ipython/agricultural_loss_indicator.ipynb
new file mode 100644
index 00000000..b7a0efb1
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/ipython/agricultural_loss_indicator.ipynb
@@ -0,0 +1,996 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "dc8a46ce-3dbf-49ee-a0ab-1449fd6d176d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "import sys\n",
+ "from pathlib import Path\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "sys.path.append(\"../../data_pipeline/\")\n",
+ "\n",
+ "%load_ext lab_black"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ebdcdf20-08b6-48e6-b28b-4bebbb3655c2",
+ "metadata": {},
+ "source": [
+ "# Examining agricultural loss indicator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "78cf4d99-6096-43a8-95e2-4e8328a78b18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DATA_PATH = Path.cwd().parent / \"data\"\n",
+ "\n",
+ "urban_rural_from_geocorr = pd.read_csv(\n",
+ " DATA_PATH / \"dataset/geocorr/usa.csv\",\n",
+ " dtype={\"GEOID10_TRACT\": str},\n",
+ ")\n",
+ "\n",
+ "score_m = pd.read_csv(\n",
+ " DATA_PATH / \"score/csv/full/usa.csv\",\n",
+ " dtype={\"GEOID10_TRACT\": str},\n",
+ " usecols=[\n",
+ " \"Expected agricultural loss rate (Natural Hazards Risk Index)\",\n",
+ " \"Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)\",\n",
+ " \"Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students?\",\n",
+ " \"Urban Heuristic Flag\",\n",
+ " \"Is low income and has a low percent of higher ed students?\",\n",
+ " \"GEOID10_TRACT\",\n",
+ " \"Total threshold criteria exceeded\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# Note that I downloaded this fresh because I am paranoid; this is not going to load on other computers and I am sorry!\n",
+ "nri_full = pd.read_csv(\n",
+ " \"/Users/emmausds/Desktop/current-work/NRI_Table_CensusTracts.csv\",\n",
+ " dtype={\"TRACTFIPS\": str},\n",
+ " usecols=[\n",
+ " \"TRACTFIPS\",\n",
+ " \"AGRIVALUE\",\n",
+ " \"CWAV_EALA\",\n",
+ " \"DRGT_EALA\",\n",
+ " \"HAIL_EALA\",\n",
+ " \"HWAV_EALA\",\n",
+ " \"HRCN_EALA\",\n",
+ " \"RFLD_EALA\",\n",
+ " \"SWND_EALA\",\n",
+ " \"TRND_EALA\",\n",
+ " \"WFIR_EALA\",\n",
+ " \"WNTW_EALA\",\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04ed5585-e7e5-4209-8e55-75a3e1fce633",
+ "metadata": {},
+ "source": [
+ "## Understanding our current implementation\n",
+ "\n",
+ "In our current implementation, on average, urban areas have a higher NRI (scaled) and a higher percentile of the loss rate. The share of Rural and Urban tracts identified by this threshold is roughly equal. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "48250479-4074-4b04-9f8b-56a6105e8225",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students? \n",
+ " \n",
+ " \n",
+ " Urban Heuristic Flag \n",
+ " \n",
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+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "tmp = sns.FacetGrid(data=score_m, col=\"Urban Heuristic Flag\", col_wrap=2, height=7)\n",
+ "tmp.map(\n",
+ " sns.distplot,\n",
+ " \"Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)\",\n",
+ " bins=20,\n",
+ " kde=True,\n",
+ " color=\"#62acef\",\n",
+ ")\n",
+ "tmp.set(xlim=(0, 1.0))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bff0654-6a88-4511-b17f-492d41023d9f",
+ "metadata": {},
+ "source": [
+ "But, if we look at just the raw loss rates, we see a very different distribution. This suggests to me that we are perhaps elevating the urban areas in our wealth-neutral metric. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b10adc64-3dae-429b-a0d3-b54f701587df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nri_with_flag = (\n",
+ " nri_full.set_index(\"TRACTFIPS\")\n",
+ " .merge(\n",
+ " urban_rural_from_geocorr.set_index(\"GEOID10_TRACT\"),\n",
+ " left_index=True,\n",
+ " right_index=True,\n",
+ " how=\"left\",\n",
+ " )\n",
+ " .reset_index()\n",
+ ")\n",
+ "\n",
+ "nri_with_flag[\"total_ag_loss\"] = nri_with_flag.filter(like=\"EALA\").sum(axis=1)\n",
+ "nri_with_flag[\"total_ag_loss_pctile\"] = nri_with_flag[\"total_ag_loss\"].rank(pct=True)\n",
+ "\n",
+ "nri_with_flag.groupby(\"Urban Heuristic Flag\")[\"total_ag_loss_pctile\"].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae3bd6f6-431a-4b73-a81d-a3e4eb14d708",
+ "metadata": {},
+ "source": [
+ "When we look at the distribution of agricultural value in census tracts that are urban and rural, we see that the agricultural value for urban tracts is quite skewed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "38a8e0a0-0cc4-43c9-b0bb-2062da59a639",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "tmp = sns.FacetGrid(\n",
+ " data=nri_with_flag, col=\"Urban Heuristic Flag\", col_wrap=1, height=4\n",
+ ")\n",
+ "tmp.map(\n",
+ " sns.distplot,\n",
+ " \"Expected agricultural loss rate (Natural Hazards Risk Index) (percentile, adjusted)\",\n",
+ " bins=20,\n",
+ " kde=True,\n",
+ " color=\"#62acef\",\n",
+ ")\n",
+ "tmp.set(xlim=(0, 1.0))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a273e5b1-c4a0-45b3-8f57-3be116685c10",
+ "metadata": {},
+ "source": [
+ "We can also look at the distribution of actually exceeding the threshold -- so also adding in the socioeconomic information from Score M. Note that *some tracts* are not shared between score M and the NRI data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "36670c86-230a-491b-bf44-767c1e1cd779",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nri_with_flag[\"low_inc_low_highed\"] = (\n",
+ " nri_with_flag[\"TRACTFIPS\"]\n",
+ " .map(\n",
+ " score_m.set_index(\"GEOID10_TRACT\")[\n",
+ " \"Is low income and has a low percent of higher ed students?\"\n",
+ " ].to_dict()\n",
+ " )\n",
+ " .fillna(False)\n",
+ ")\n",
+ "\n",
+ "nri_with_flag[\"would_exceed_threshold\"] = (\n",
+ " nri_with_flag[\"low_inc_low_highed\"]\n",
+ " & nri_with_flag[\n",
+ " \"Expected agricultural loss rate (Natural Hazards Risk Index) exceeds 90th percentile, adjusted\"\n",
+ " ]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "bdb9a4b3-954a-4389-86d0-84d896c1679c",
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(11, 7))\n",
+ "sns.lineplot(\n",
+ " x=\"Left clip value\",\n",
+ " y=\"Average percentile\",\n",
+ " hue=\"Tract classification\",\n",
+ " data=graph_data,\n",
+ " palette=\"colorblind\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c898c542-1dad-43b7-b20b-ee065f96c227",
+ "metadata": {},
+ "source": [
+ "Note -- some tracts have a missing urban / rural flag. They get left out from the means. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "adfcdbe0-fdcc-4ec7-8788-5628641bed8b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.0 58429\n",
+ "0.0 14101\n",
+ "NaN 209\n",
+ "Name: Urban Heuristic Flag, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nri_with_flag[\"Urban Heuristic Flag\"].value_counts(dropna=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0fba097c-b5e0-4511-b072-05511906dd9b",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb b/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb
index ddf05c3e..bb66c5c7 100644
--- a/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb
+++ b/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb
@@ -7,28 +7,7 @@
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "ename": "ModuleNotFoundError",
- "evalue": "No module named 'lab_black'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/var/folders/lx/xmq8p65j71v9xq2bhsd2j5w40000gp/T/ipykernel_29987/670980058.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdata_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfield_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lab_black'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0;31m# Turn on TQDM for pandas so that we can have progress bars when running `apply`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2349\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_local_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2350\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2351\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2352\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 233\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__file__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m print((\"Loading extensions from {dir} is deprecated. \"\n",
- "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
- "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
- "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
- "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'lab_black'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import collections\n",
"import functools\n",
@@ -71,7 +50,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "2ce3170c",
"metadata": {
"scrolled": true
@@ -106,12 +85,290 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "8bd39090",
"metadata": {
"scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/Cellar/jupyterlab/3.2.8/libexec/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3251: DtypeWarning: Columns (1) 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"
+ ]
+ },
+ {
+ "data": {
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"source": [
"# Analyze one field at a time (useful for setting thresholds)\n",
"\n",
@@ -218,40 +834,354 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "8c3e462c",
"metadata": {
"scrolled": true
},
- "outputs": [],
+ "outputs": [
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5 rows × 661 columns
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