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yanofsky/h2-analysis.ipynb

Created August 11, 2022 21:46
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h2-analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib\n",
"from scipy import stats as st\n",
"from scipy.stats import kruskal\n",
"from scipy.stats import mannwhitneyu\n",
"\n",
"from IPython.display import display, Markdown, Latex\n",
"import itertools\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load the story and traffic data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def words_in_str(word_list, string):\n",
" return list(filter(lambda word: word in string, word_list))\n",
"\n",
"df = (pd.read_json(\"h2-raw.json\")\n",
"\n",
" # combine h2 and h3 lists remove duplicates\n",
" .assign(header_words=lambda x: \n",
" x.apply(lambda row: list(\n",
" set(row.h2_words + row.h3_words)\n",
" ),\n",
" axis=1\n",
" )\n",
" )\n",
"\n",
" # list words from headers that are in the title\n",
" .assign(header_words_in_title=lambda x: \n",
" x.apply(lambda row: words_in_str(row.header_words, row.title), axis=1)\n",
" )\n",
"\n",
" # list words from headers in SEO title\n",
" .assign(header_words_in_seoTitle=lambda x: \n",
" x.apply(lambda row: words_in_str(row.header_words, row.seoTitle), axis=1)\n",
" )\n",
"\n",
" # count the number of title and SEO word matches\n",
" .assign(header_words_in_title_count=lambda x: x.header_words_in_title.str.len())\n",
" .assign(header_words_in_seoTitle_count=lambda x: x.header_words_in_seoTitle.str.len())\n",
"\n",
" .assign(pub_date=lambda x: pd.to_datetime(x.date))\n",
"\n",
" .set_index(\"link\")\n",
"\n",
" .join(\n",
" pd.read_csv(\"normalized_traffic_data.csv\")\n",
" .set_index(\"link\")\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define the universe of stories to consider"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"column = \"one_month_views\"\n",
"date_limit = (df.pub_date.max() - pd.Timedelta(days=1))\n",
"min_date = pd.datetime(2020,1,1)\n",
"\n",
"universe = (\n",
" df\n",
" # only use stories that have traffic data\n",
" .query(\"one_day_views > 0\")\n",
"\n",
" # only use stories that are old enough for comparison\n",
" .query(\"pub_date < @date_limit\")\n",
"\n",
" # exclude items that are members-only\n",
" .query(\"paywalled != True\")\n",
" .query(\"guides == ''\")\n",
" .query(f\"date > @min_date\")\n",
")\n",
"\n",
"# mask = f\"topics == '{df.topics.unique()[5]}'\"\n",
"# mask = None\n",
"top_traffic_bound = universe[column].quantile(0.95)\n",
"\n",
"universe = (\n",
" universe\n",
" # exclude the top performing 5% of stories \n",
" .query(f\"{column} < {top_traffic_bound}\")\n",
" # .query(\"word_count < 300\")\n",
" # .query(mask)\n",
"\n",
"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4103"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(universe)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2020-01-01 06:00:36')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"universe.date.min()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define the subsets to compare"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# test for the effect of the number of headers\n",
"num_headers = {\n",
" \"name\": \"Number of headers\",\n",
" \"data\" : pd.concat([\n",
" (\n",
" universe\n",
" .query(\"h2_count == 0 and h3_count == 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"No headers\"\n",
" })\n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count == 1 or h3_count == 1\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"1 header\"\n",
" })\n",
" \n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count == 2 or h3_count == 2\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"2 header\"\n",
" })\n",
" \n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count == 3 or h3_count == 3\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"3 header\"\n",
" })\n",
" \n",
" )\n",
" ])\n",
"}\n",
"\n",
"# test for the effect of just having headers\n",
"headers_exist = {\n",
" \"name\": \"Existence of headers\",\n",
" \"data\": pd.concat([(\n",
" universe\n",
" .query(\"h2_count == 0 and h3_count == 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"No headers\"\n",
" })\n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count > 0 or h3_count > 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"Has headers\"\n",
" })\n",
" )\n",
" ])\n",
"}\n",
"\n",
"headers_contain_keywords = {\n",
" \"name\": \"Headers exist, and contain keywords\",\n",
" \"data\": pd.concat([\n",
" (\n",
" universe\n",
" .query(\"h2_count > 0 or h3_count > 0\")\n",
" .query(\"header_words_in_title_count == 0\")\n",
" .query(\"header_words_in_seoTitle_count == 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"no keywords\"\n",
" })\n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count > 0 or h3_count > 0\")\n",
" .query(\"header_words_in_title_count > 0\")\n",
" .query(\"header_words_in_seoTitle_count == 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"only hed keywords\"\n",
" })\n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count > 0 or h3_count > 0\")\n",
" .query(\"header_words_in_title_count == 0\")\n",
" .query(\"header_words_in_seoTitle_count > 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"only seoHed keywords\"\n",
" })\n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count > 0 or h3_count > 0\")\n",
" .query(\"header_words_in_title_count > 0\")\n",
" .query(\"header_words_in_seoTitle_count > 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"Keywords in both\"\n",
" })\n",
" )\n",
" ])\n",
"}\n",
"\n",
"\n",
"\n",
"to_test = [\n",
" num_headers,\n",
" headers_exist,\n",
" headers_contain_keywords\n",
"]\n",
"\n",
"for i in range(len(to_test)):\n",
" data = to_test[i][\"data\"]\n",
" to_test[i][\"data_as_lists\"] = [data[col].dropna().to_list() for col in data]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Perform a kruskal wallace h test"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Number of headers**<br>Statistics=12.97, p=0.005\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"**Existence of headers**<br>Statistics=14.04, p=0.000\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"🚨 **Headers exist, and contain keywords**<br>Statistics=5.42, p=0.144\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for t in to_test:\n",
" stat, p = kruskal(*t[\"data_as_lists\"])\n",
" display(Markdown(f'{\"🚨 \" if p>0.05 else \"\"}**{t[\"name\"]}**<br>Statistics={stat:.2f}, p={p:.3f}\\n'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Perform mann whitney u test on each pair "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"## Number of headers"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>p-value</th>\n",
" <th>reject</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No headers</td>\n",
" <td>1 header</td>\n",
" <td>0.07869</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No headers</td>\n",
" <td>2 header</td>\n",
" <td>0.00489</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No headers</td>\n",
" <td>3 header</td>\n",
" <td>0.01784</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1 header</td>\n",
" <td>2 header</td>\n",
" <td>0.69302</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1 header</td>\n",
" <td>3 header</td>\n",
" <td>0.78039</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2 header</td>\n",
" <td>3 header</td>\n",
" <td>0.89517</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B p-value reject\n",
"0 No headers 1 header 0.07869 False\n",
"1 No headers 2 header 0.00489 True\n",
"2 No headers 3 header 0.01784 True\n",
"3 1 header 2 header 0.69302 False\n",
"4 1 header 3 header 0.78039 False\n",
"5 2 header 3 header 0.89517 False"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"## Existence of headers"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>p-value</th>\n",
" <th>reject</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No headers</td>\n",
" <td>Has headers</td>\n",
" <td>0.00018</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B p-value reject\n",
"0 No headers Has headers 0.00018 True"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"## Headers exist, and contain keywords"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Kruskal p value is too high: 0.144"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def mannwhitneyu_table(group_names , *args ):\n",
" \"\"\"\n",
" Conduct a mann whitney u test on every pair of data subsets\n",
"\n",
" Parameters:\n",
" group_names (list): A list of the subsets' names\n",
" args: All of the subsets' data \n",
"\n",
" Returns:\n",
" (DataFrame): A table showing every permutation of the test, \n",
" the p-value, and a boolean specifying if the null \n",
" hypothesis should be rejected.\n",
" \"\"\"\n",
" results = []\n",
" pairs_list = list(itertools.combinations(args,2))\n",
" names_list = list(itertools.combinations(group_names,2))\n",
" for i in range(len(pairs_list)):\n",
" i_list, j_list = pairs_list[i]\n",
" i_group, j_group = names_list[i]\n",
" if i_group == j_group:\n",
" continue\n",
" stat, p = mannwhitneyu(i_list, j_list, alternative=\"two-sided\")\n",
" results.append({\n",
" \"A\": i_group,\n",
" \"B\": j_group,\n",
" \"p-value\": round(p,5),\n",
" \"reject\": p < 0.05\n",
" })\n",
" result_df = pd.DataFrame(results)\n",
" \n",
" return result_df#.style.apply(lambda x: [\"color: white; font-weight: bold\"] * len(x) if x.reject == True else \"\", axis=1)\n",
"\n",
"for t in to_test:\n",
" display(Markdown(f\"## {t['name']}\"))\n",
" stats, p = kruskal(*t[\"data_as_lists\"])\n",
" if p < 0.05:\n",
" display(mannwhitneyu_table(t[\"data\"].columns, *t[\"data_as_lists\"]))\n",
" else:\n",
" display(Markdown(f\"Kruskal p value is too high: {p:.3f}\"))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"## Number of headers"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.004700240273627675\n"
]
},
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
"#T_9dadc_row1_col0,#T_9dadc_row1_col1,#T_9dadc_row1_col2,#T_9dadc_row1_col3{\n",
" background-color: #587800;\n",
" }</style><table id=\"T_9dadc_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >mean</th> <th class=\"col_heading level0 col1\" >25%</th> <th class=\"col_heading level0 col2\" >50%</th> <th class=\"col_heading level0 col3\" >75%</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_9dadc_level0_row0\" class=\"row_heading level0 row0\" >No headers</th>\n",
" <td id=\"T_9dadc_row0_col0\" class=\"data row0 col0\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row0_col1\" class=\"data row0 col1\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row0_col2\" class=\"data row0 col2\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row0_col3\" class=\"data row0 col3\" >XXXXXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_9dadc_level0_row1\" class=\"row_heading level0 row1\" >2 header</th>\n",
" <td id=\"T_9dadc_row1_col0\" class=\"data row1 col0\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row1_col1\" class=\"data row1 col1\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row1_col2\" class=\"data row1 col2\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row1_col3\" class=\"data row1 col3\" >XXXXXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_9dadc_level0_row2\" class=\"row_heading level0 row2\" >change</th>\n",
" <td id=\"T_9dadc_row2_col0\" class=\"data row2 col0\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row2_col1\" class=\"data row2 col1\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row2_col2\" class=\"data row2 col2\" >XXXXXX</td>\n",
" <td id=\"T_9dadc_row2_col3\" class=\"data row2 col3\" >XXXXXX</td>\n",
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" </tbody></table>"
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" background-color: #587800;\n",
" }</style><table id=\"T_89e89_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >mean</th> <th class=\"col_heading level0 col1\" >25%</th> <th class=\"col_heading level0 col2\" >50%</th> <th class=\"col_heading level0 col3\" >75%</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_89e89_level0_row0\" class=\"row_heading level0 row0\" >No headers</th>\n",
" <td id=\"T_89e89_row0_col0\" class=\"data row0 col0\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row0_col1\" class=\"data row0 col1\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row0_col2\" class=\"data row0 col2\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row0_col3\" class=\"data row0 col3\" >XXXXXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_89e89_level0_row1\" class=\"row_heading level0 row1\" >3 header</th>\n",
" <td id=\"T_89e89_row1_col0\" class=\"data row1 col0\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row1_col1\" class=\"data row1 col1\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row1_col2\" class=\"data row1 col2\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row1_col3\" class=\"data row1 col3\" >XXXXXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_89e89_level0_row2\" class=\"row_heading level0 row2\" >change</th>\n",
" <td id=\"T_89e89_row2_col0\" class=\"data row2 col0\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row2_col1\" class=\"data row2 col1\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row2_col2\" class=\"data row2 col2\" >XXXXXX</td>\n",
" <td id=\"T_89e89_row2_col3\" class=\"data row2 col3\" >XXXXXX</td>\n",
" </tr>\n",
" </tbody></table>"
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"metadata": {},
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{
"data": {
"text/markdown": [
"## Existence of headers"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.00017910862295345388\n"
]
},
{
"data": {
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"<style type=\"text/css\" >\n",
"#T_c5328_row1_col0,#T_c5328_row1_col1,#T_c5328_row1_col2,#T_c5328_row1_col3{\n",
" background-color: #587800;\n",
" }</style><table id=\"T_c5328_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >mean</th> <th class=\"col_heading level0 col1\" >25%</th> <th class=\"col_heading level0 col2\" >50%</th> <th class=\"col_heading level0 col3\" >75%</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_c5328_level0_row0\" class=\"row_heading level0 row0\" >No headers</th>\n",
" <td id=\"T_c5328_row0_col0\" class=\"data row0 col0\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row0_col1\" class=\"data row0 col1\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row0_col2\" class=\"data row0 col2\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row0_col3\" class=\"data row0 col3\" >XXXXXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c5328_level0_row1\" class=\"row_heading level0 row1\" >Has headers</th>\n",
" <td id=\"T_c5328_row1_col0\" class=\"data row1 col0\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row1_col1\" class=\"data row1 col1\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row1_col2\" class=\"data row1 col2\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row1_col3\" class=\"data row1 col3\" >XXXXXX</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c5328_level0_row2\" class=\"row_heading level0 row2\" >change</th>\n",
" <td id=\"T_c5328_row2_col0\" class=\"data row2 col0\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row2_col1\" class=\"data row2 col1\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row2_col2\" class=\"data row2 col2\" >XXXXXX</td>\n",
" <td id=\"T_c5328_row2_col3\" class=\"data row2 col3\" >XXXXXX</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x141d00978>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"## Headers exist, and contain keywords"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Kruskal p value is too high: 0.144"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def compareResults(t, row):\n",
" \"\"\"\n",
" Create a table for pairwise comparisons of quartiles and display it,\n",
" highlighting the max value in each row\n",
"\n",
" Parameters:\n",
" t (dict): A dictionary with the prepared subset information and data\n",
" row (Series): The row of a mannwhitneyu_table result\n",
"\n",
" Return:\n",
" None\n",
" \"\"\"\n",
" return (\n",
" pd.concat([\n",
" t[\"data\"][row.A].describe(),\n",
" t[\"data\"][row.B].describe(),\n",
" ], axis=1)\n",
" .transpose()\n",
" [[\"mean\", \"25%\", \"50%\", \"75%\"]]\n",
" .transpose()\n",
" .assign(change=lambda x: (x[row.B] - x[row.A]) / x[row.A] * 100)\n",
" .transpose()\n",
" .style.highlight_max(color=\"#587800\", axis=0)\n",
" )\n",
"\n",
"for t in to_test:\n",
" display(Markdown(f\"## {t['name']}\"))\n",
" stats, p = kruskal(*t[\"data_as_lists\"])\n",
" if p < 0.05:\n",
" print(p)\n",
" results = mannwhitneyu_table(t[\"data\"].columns, *t[\"data_as_lists\"])\n",
" results.apply(lambda x: display(compareResults(t, x)) if x.reject == True else None, axis=1)\n",
" else:\n",
" display(Markdown(f\"Kruskal p value is too high: {p:.3f}\"))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create histograms of data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def create_histogram(datalist, val):\n",
" return (\n",
" pd.DataFrame(\n",
" pd.np.histogram(\n",
" datalist, \n",
" bins=range(0,75000,5000)\n",
" )\n",
"\n",
" ).transpose()\n",
" .rename(columns={\n",
" 0:\"val\",\n",
" 1:\"bin\"\n",
" })\n",
" .assign(val=lambda x: x.val / len(datalist) * 100)\n",
" .rename(columns={\n",
" \"val\": val\n",
" })\n",
" .assign(bin=lambda x: x.bin.map(lambda n: f\"{round(n):,d}\"))\n",
" .set_index(\"bin\")\n",
" )\n",
"\n",
"for_chart = (\n",
" create_histogram(\n",
" to_test[1][\"data\"][\"No headers\"].dropna().to_list(), \"No headers\"\n",
" )\n",
" .join(create_histogram(\n",
" to_test[1][\"data\"][\"Has headers\"].dropna().to_list(), \"Has headers\"\n",
" ))\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Copy the dat for charting"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# the difference between the curves\n",
"for_chart.assign(diff=lambda x: x[\"Has headers\"] - x[\"No headers\"])[[\"diff\"]].to_clipboard()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# the curves\n",
"for_chart.to_clipboard()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Randomize data to see range of effect"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"subset = pd.concat([(\n",
" universe\n",
" .query(\"h2_count == 0 and h3_count == 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"No headers\"\n",
" })\n",
" ),\n",
" (\n",
" universe\n",
" .query(\"h2_count > 0 or h3_count > 0\")\n",
" [[column]]\n",
" .rename(columns={\n",
" column: \"Has headers\"\n",
" })\n",
" )\n",
" ])\n",
"\n",
"def simulateRandomSampleRange(frame, data_col, analysis_col, sampleSize, numIterations):\n",
" raw_results = []\n",
"\n",
" # take a sample and describe the analysis column\n",
" for i in range(numIterations):\n",
" raw_results.append(\n",
" frame.sample(sampleSize)\n",
" [data_col]\n",
" .describe()\n",
" )\n",
"\n",
" # describe the joined analysis columns\n",
" summary = pd.concat(raw_results, axis=1).describe().transpose()\n",
" \n",
"\n",
" summaryLow = summary[analysis_col].quantile(0.025)\n",
" summaryHigh = summary[analysis_col].quantile(0.975)\n",
" return (\n",
" # remove the 5% most extreme outcomes\n",
" summary.query(f\"`{analysis_col}` > {summaryLow}\")\n",
" .query(f\"`{analysis_col}` < {summaryHigh}\")\n",
" # get the expected range of 95% of results\n",
" .describe()\n",
" [analysis_col]\n",
" .transpose()[[\"min\",\"max\"]]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<AxesSubplot:title={'center':'mean_diff'}>,\n",
" <AxesSubplot:title={'center':'median_diff'}>]], dtype=object)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"numIterations = 5000\n",
"sampleSize = 500\n",
"results = []\n",
"\n",
"def pct_change(then, now):\n",
" return (now - then) / then * 100\n",
"\n",
"for i in range(numIterations):\n",
" comp = {}\n",
" no_hed, yes_hed = (subset[col].dropna().sample(sampleSize).describe().to_dict() for col in subset)\n",
" results.append({\n",
" \"iteration\": i,\n",
" \"mean_diff\": pct_change(no_hed[\"mean\"], yes_hed[\"mean\"]),\n",
" \"median_diff\": pct_change(no_hed[\"50%\"], yes_hed[\"50%\"])\n",
"\n",
" })\n",
"\n",
"pd.DataFrame(results).drop(\"iteration\", axis=1).hist(bins=50)"
]
},
{
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