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Merged
wingertkm-NIH merged 2 commits into from
May 27, 2026
Merged

Kristen edit stat tutorial #85

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395b838
Update STAT-tutorial.ipynb
wingertkm-NIH May 27, 2026
06b60be
Github Action: Lint Notebooks
May 27, 2026
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200 changes: 200 additions & 0 deletions notebooks/ncbi-stat-tutorial/STAT-tutorial.ipynb
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Expand Up @@ -264,6 +264,206 @@
"If you want to experiment a bit, rerun the query with a different tax id, modify the total_count, and modify the time Interval and see how your results change. Or, we can run a few more example queries from the [NCBI STAT page](https://www.ncbi.nlm.nih.gov/sra/docs/sra-cloud-based-taxonomy-analysis-table/). "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "geo-graph-code",
"metadata": {},
"outputs": [],
"source": [
"# -----------------------------------------------------------------------------\n",
"# Install dependencies if needed\n",
"# -----------------------------------------------------------------------------\n",
"# !pip install pandas plotly numpy\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# Imports\n",
"# -----------------------------------------------------------------------------\n",
"import ast\n",
"import re\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# Load data\n",
"# -----------------------------------------------------------------------------\n",
"df.columns = [c.strip().lower() for c in df.columns]\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# Type cleanup\n",
"# -----------------------------------------------------------------------------\n",
"if \"releasedate\" in df.columns:\n",
" df[\"releasedate\"] = pd.to_datetime(df[\"releasedate\"], errors=\"coerce\")\n",
"\n",
"for col in [\"total_count\", \"self_count\"]:\n",
" if col in df.columns:\n",
" df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# Robust geography parser\n",
"# -----------------------------------------------------------------------------\n",
"MISSING_LIKE = {\n",
" \"\", \"[]\", \"['']\", \"['missing']\", \"['not applicable']\",\n",
" \"['not collected']\", \"['not provided']\", \"missing\", \"not applicable\",\n",
" \"not collected\", \"not provided\", \"nan\", \"none\"\n",
"}\n",
"\n",
"COUNTRY_ALIASES = {\n",
" \"USA\": \"United States\",\n",
" \"U.S.A.\": \"United States\",\n",
" \"US\": \"United States\",\n",
" \"UK\": \"United Kingdom\",\n",
" \"Korea\": \"South Korea\",\n",
" \"South Korea\": \"South Korea\",\n",
" \"North Korea\": \"North Korea\",\n",
" \"Russian Federation\": \"Russia\",\n",
"}\n",
"\n",
"def parse_geo_loc(value):\n",
" if value is None:\n",
" return None, None, None\n",
"\n",
" if isinstance(value, float) and pd.isna(value):\n",
" return None, None, None\n",
"\n",
" if isinstance(value, np.ndarray):\n",
" if value.size == 0:\n",
" return None, None, None\n",
" if value.size == 1:\n",
" value = value.item()\n",
" else:\n",
" value = value.tolist()\n",
"\n",
" s = str(value).strip()\n",
"\n",
" if not s or s.lower() in MISSING_LIKE:\n",
" return None, None, None\n",
"\n",
" try:\n",
" parsed = ast.literal_eval(s)\n",
" if isinstance(parsed, list):\n",
" if len(parsed) == 0:\n",
" return None, None, None\n",
" s = str(parsed[0]).strip()\n",
" else:\n",
" s = str(parsed).strip()\n",
" except Exception:\n",
" s = s.strip(\"[]\").strip(\"'\").strip('\"').strip()\n",
"\n",
" if not s or s.lower() in MISSING_LIKE:\n",
" return None, None, None\n",
"\n",
" s = re.sub(r\"\\s+\", \" \", s).strip()\n",
"\n",
" if \":\" in s:\n",
" country, region = [x.strip() for x in s.split(\":\", 1)]\n",
" else:\n",
" country, region = s, None\n",
"\n",
" country = COUNTRY_ALIASES.get(country, country)\n",
"\n",
" return country, region, s\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# Apply parser\n",
"# -----------------------------------------------------------------------------\n",
"if \"geo_loc_name_sam\" not in df.columns:\n",
" raise KeyError(\"The file does not contain a 'geo_loc_name_sam' column.\")\n",
"\n",
"geo = df[\"geo_loc_name_sam\"].apply(parse_geo_loc)\n",
"\n",
"df[\"country\"] = geo.apply(lambda x: x[0])\n",
"df[\"region\"] = geo.apply(lambda x: x[1])\n",
"df[\"geo_clean\"] = geo.apply(lambda x: x[2])\n",
"\n",
"geo_df = df.dropna(subset=[\"country\"]).copy()\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# Country-level aggregation\n",
"# -----------------------------------------------------------------------------\n",
"country_counts = (\n",
" geo_df.groupby(\"country\", as_index=False)\n",
" .size()\n",
" .rename(columns={\"size\": \"submissions\"})\n",
" .sort_values(\"submissions\", ascending=False)\n",
")\n",
"\n",
"top20_countries = country_counts.head(20).copy()\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 1) Choropleth map: submissions by country\n",
"# -----------------------------------------------------------------------------\n",
"fig_choropleth = px.choropleth(\n",
" country_counts,\n",
" locations=\"country\",\n",
" locationmode=\"country names\",\n",
" color=\"submissions\",\n",
" color_continuous_scale=\"Viridis\",\n",
" title=\"Submissions by Country\",\n",
")\n",
"\n",
"fig_choropleth.update_layout(\n",
" geo=dict(showframe=False, showcoastlines=True, projection_type=\"natural earth\"),\n",
" margin=dict(l=0, r=0, t=50, b=0),\n",
")\n",
"\n",
"fig_choropleth.show()\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 2) Bar chart: top countries\n",
"# -----------------------------------------------------------------------------\n",
"fig_top_countries = px.bar(\n",
" top20_countries.sort_values(\"submissions\", ascending=True),\n",
" x=\"submissions\",\n",
" y=\"country\",\n",
" orientation=\"h\",\n",
" title=\"Top 20 Countries by Submissions\",\n",
" labels={\"submissions\": \"Submissions\", \"country\": \"Country\"},\n",
")\n",
"\n",
"fig_top_countries.update_layout(\n",
" yaxis=dict(categoryorder=\"total ascending\"),\n",
" margin=dict(l=20, r=20, t=50, b=20),\n",
")\n",
"\n",
"fig_top_countries.show()\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 3) Bar chart: top countries/regions\n",
"# -----------------------------------------------------------------------------\n",
"geo_df[\"location_label\"] = geo_df.apply(\n",
" lambda r: f\"{r['country']}: {r['region']}\" if pd.notna(r[\"region\"]) and r[\"region\"] else r[\"country\"],\n",
" axis=1\n",
")\n",
"\n",
"location_counts = (\n",
" geo_df.groupby(\"location_label\", as_index=False)\n",
" .size()\n",
" .rename(columns={\"size\": \"submissions\"})\n",
" .sort_values(\"submissions\", ascending=False)\n",
")\n",
"\n",
"top20_locations = location_counts.head(20).copy()\n",
"\n",
"fig_top_locations = px.bar(\n",
" top20_locations.sort_values(\"submissions\", ascending=True),\n",
" x=\"submissions\",\n",
" y=\"location_label\",\n",
" orientation=\"h\",\n",
" title=\"Top 20 Countries / Regions by Submissions\",\n",
" labels={\"submissions\": \"Submissions\", \"location_label\": \"Country / Region\"},\n",
")\n",
"\n",
"fig_top_locations.update_layout(\n",
" yaxis=dict(categoryorder=\"total ascending\"),\n",
" margin=dict(l=20, r=20, t=50, b=20),\n",
")\n",
"\n",
"fig_top_locations.show()\n"
]
},
{
"cell_type": "markdown",
"id": "2f8d42ae",
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