From e19c762d6605f32ad88d08f487dafc7df38f0c02 Mon Sep 17 00:00:00 2001 From: Yujin Kim Date: Tue, 21 Mar 2023 15:35:08 +0000 Subject: [PATCH] pandas --- pandas/pandas.html | 16367 ++++++++++++++++++++++++++++++++++++++++++ pandas/pandas.ipynb | 1536 ++++ pandas/td_v5.xlsx | Bin 0 -> 151620 bytes 3 files changed, 17903 insertions(+) create mode 100644 pandas/pandas.html create mode 100644 pandas/pandas.ipynb create mode 100644 pandas/td_v5.xlsx diff --git a/pandas/pandas.html b/pandas/pandas.html new file mode 100644 index 0000000..c1f3ae0 --- /dev/null +++ b/pandas/pandas.html @@ -0,0 +1,16367 @@ + + + + + +pandas + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + diff --git a/pandas/pandas.ipynb b/pandas/pandas.ipynb new file mode 100644 index 0000000..3ee3424 --- /dev/null +++ b/pandas/pandas.ipynb @@ -0,0 +1,1536 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "Pandas is Python package used for data manipulation and analysis. The name is derived from the term \"panel data\".\n", + "\n", + "To use pandas, you need to import it. And numpy is required as well.\n", + "It is generally imported using `np` and `pd` as abbreviations." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas objects\n", + "\n", + "## Series\n", + "\n", + "- One dimensional array of indexed data\n", + "- Can be used to convert a dictionary\n", + " - Like dictionaries, Series can be access using “keys” (index)\n", + " - Contrary to dictionaries, Series can be sliced" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "California 38332521\n", + "Texas 26448193\n", + "New York 19651127\n", + "Florida 19552860\n", + "Illinois 12882135\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "California 38332521\n", + "Texas 26448193\n", + "dtype: int64" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "population_dict = {'California': 38332521,\n", + " 'Texas': 26448193,\n", + " 'New York': 19651127,\n", + " 'Florida': 19552860,\n", + " 'Illinois': 12882135}\n", + "population = pd.Series(population_dict)\n", + "print(population)\n", + "population[\"California\":\"Texas\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataframes\n", + "\n", + "- Two dimensional array of indexed data but can be thought of as sequences of Series objects\n", + "- 2 distinct Index objects will define the rows and the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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populationarea
California38332521423967
Texas26448193695662
New York19651127141297
Florida19552860170312
Illinois12882135149995
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" + ], + "text/plain": [ + " population area\n", + "California 38332521 423967\n", + "Texas 26448193 695662\n", + "New York 19651127 141297\n", + "Florida 19552860 170312\n", + "Illinois 12882135 149995" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n", + " 'Florida': 170312, 'Illinois': 149995}\n", + "area = pd.Series(area_dict)\n", + "\n", + "states = pd.DataFrame({'population': population,\n", + " 'area': area})\n", + "states" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Index\n", + "\n", + "- Immutable array used to index the Series and Dataframes object" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['California', 'Texas', 'New York', 'Florida', 'Illinois'], dtype='object')\n", + "Index(['population', 'area'], dtype='object')\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(states.index)\n", + "print(states.columns)\n", + "print(type(states.index))\n", + "print(type(states.columns))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import data\n", + "\n", + "In our setting, you are usually going to be importing data from a csv/tsv or an excel. And you chose pandas because the data structure is a bit too complex or it is just an easy way to quickly import it.\n", + "In this session, i'll be taking you into a deep dive of the new test directory." + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['Explanatory note ', 'R&ID indications', 'Neuro STR routing'])" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_excel(\"td_v5.xlsx\", sheet_name=None, header=1)\n", + "df.keys()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous command, I'm importing the test directory but i know that the excel has multiple sheets and by default the `read_excel` cmd will use the first sheet and discard the rest.\n", + "The header option specifies which line is the header.\n", + "\n", + "https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Clinical indication IDTest IDClinical IndicationTarget/GenesTest MethodCommissioning categorySpecialist test groupChanges since October 2022 v4 publicationSingle national providerNorth East & Yorkshire GLHNorth West GLHEast GLHCentral & South GLHSouth West GLHNorth Thames GLHSouth East GLH
0R67R67.1Monogenic hearing lossHearing loss (126)WES or Large PanelSpecialisedAudiologyUpdate to gene content via PanelApp routeNoNorth West GLH - Manchester University FTNorth West GLH - Manchester University FTNorth West GLH- Manchester University FTNorth Thames GLH - Great Ormond Street FTNorth Thames GLH - Great Ormond Street FTNorth Thames GLH - Great Ormond Street FTNorth Thames GLH - Great Ormond Street FT
1R67R67.2Monogenic hearing lossHearing loss (126)MLPA or equivalentSpecialisedAudiologyNo changeNoNorth West GLH - Manchester University FTNorth West GLH - Manchester University FTNorth West GLH- Manchester University FTNorth Thames GLH - Great Ormond Street FTNorth Thames GLH - Great Ormond Street FTNorth Thames GLH - Great Ormond Street FTNorth Thames GLH - Great Ormond Street FT
2R125R125.1Thoracic aortic aneurysm or dissectionThoracic aortic aneurysm or dissection (700)WES or Medium PanelSpecialisedCardiologyUpdate to gene content via PanelApp routeNoNorth West GLH - Manchester University FTNorth West GLH - Manchester University FTSouth West GLH - North Bristol TrustCentral and South GLH - Salisbury FTSouth West GLH - North Bristol TrustSouth East GLH - Royal Brompton and Harefield FTSouth East GLH - Royal Brompton and Harefield FT
3R125R125.2Thoracic aortic aneurysm or dissectionThoracic aortic aneurysm or dissection (700)MLPA or equivalentSpecialisedCardiologyNo changeNoNorth West GLH - Manchester University FTNorth West GLH - Manchester University FTSouth West GLH - North Bristol TrustCentral and South GLH - Salisbury FTSouth West GLH - North Bristol TrustSouth East GLH - Royal Brompton and Harefield FTSouth East GLH - Royal Brompton and Harefield FT
4R127R127.1Long QT syndromeLong QT syndrome (76)Small panelSpecialisedCardiologyNo changeNoNorth West GLH - Manchester University FTNorth West GLH - Manchester University FTSouth West GLH - North Bristol TrustCentral and South GLH - Oxford University FTSouth West GLH - North Bristol TrustSouth East GLH - Royal Brompton and Harefield FTSouth East GLH - Royal Brompton and Harefield FT
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" + ], + "text/plain": [ + " Clinical indication ID Test ID Clinical Indication \\\n", + "0 R67 R67.1 Monogenic hearing loss \n", + "1 R67 R67.2 Monogenic hearing loss \n", + "2 R125 R125.1 Thoracic aortic aneurysm or dissection \n", + "3 R125 R125.2 Thoracic aortic aneurysm or dissection \n", + "4 R127 R127.1 Long QT syndrome \n", + "\n", + " Target/Genes Test Method \\\n", + "0 Hearing loss (126) WES or Large Panel \n", + "1 Hearing loss (126) MLPA or equivalent \n", + "2 Thoracic aortic aneurysm or dissection (700) WES or Medium Panel \n", + "3 Thoracic aortic aneurysm or dissection (700) MLPA or equivalent \n", + "4 Long QT syndrome (76) Small panel \n", + "\n", + " Commissioning category Specialist test group \\\n", + "0 Specialised Audiology \n", + "1 Specialised Audiology \n", + "2 Specialised Cardiology \n", + "3 Specialised Cardiology \n", + "4 Specialised Cardiology \n", + "\n", + " Changes since October 2022 v4 publication Single national provider \\\n", + "0 Update to gene content via PanelApp route No \n", + "1 No change No \n", + "2 Update to gene content via PanelApp route No \n", + "3 No change No \n", + "4 No change No \n", + "\n", + " North East & Yorkshire GLH \\\n", + "0 North West GLH - Manchester University FT \n", + "1 North West GLH - Manchester University FT \n", + "2 North West GLH - Manchester University FT \n", + "3 North West GLH - Manchester University FT \n", + "4 North West GLH - Manchester University FT \n", + "\n", + " North West GLH \\\n", + "0 North West GLH - Manchester University FT \n", + "1 North West GLH - Manchester University FT \n", + "2 North West GLH - Manchester University FT \n", + "3 North West GLH - Manchester University FT \n", + "4 North West GLH - Manchester University FT \n", + "\n", + " East GLH \\\n", + "0 North West GLH- Manchester University FT \n", + "1 North West GLH- Manchester University FT \n", + "2 South West GLH - North Bristol Trust \n", + "3 South West GLH - North Bristol Trust \n", + "4 South West GLH - North Bristol Trust \n", + "\n", + " Central & South GLH \\\n", + "0 North Thames GLH - Great Ormond Street FT \n", + "1 North Thames GLH - Great Ormond Street FT \n", + "2 Central and South GLH - Salisbury FT \n", + "3 Central and South GLH - Salisbury FT \n", + "4 Central and South GLH - Oxford University FT \n", + "\n", + " South West GLH \\\n", + "0 North Thames GLH - Great Ormond Street FT \n", + "1 North Thames GLH - Great Ormond Street FT \n", + "2 South West GLH - North Bristol Trust \n", + "3 South West GLH - North Bristol Trust \n", + "4 South West GLH - North Bristol Trust \n", + "\n", + " North Thames GLH \\\n", + "0 North Thames GLH - Great Ormond Street FT \n", + "1 North Thames GLH - Great Ormond Street FT \n", + "2 South East GLH - Royal Brompton and Harefield FT \n", + "3 South East GLH - Royal Brompton and Harefield FT \n", + "4 South East GLH - Royal Brompton and Harefield FT \n", + "\n", + " South East GLH \n", + "0 North Thames GLH - Great Ormond Street FT \n", + "1 North Thames GLH - Great Ormond Street FT \n", + "2 South East GLH - Royal Brompton and Harefield FT \n", + "3 South East GLH - Royal Brompton and Harefield FT \n", + "4 South East GLH - Royal Brompton and Harefield FT " + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df[\"R&ID indications\"]\n", + "df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can safely remove columns after the `Changes since October 2022 v4 publication` column." + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Clinical indication IDTest IDClinical IndicationTarget/GenesTest MethodCommissioning categorySpecialist test groupChanges since October 2022 v4 publication
0R67R67.1Monogenic hearing lossHearing loss (126)WES or Large PanelSpecialisedAudiologyUpdate to gene content via PanelApp route
1R67R67.2Monogenic hearing lossHearing loss (126)MLPA or equivalentSpecialisedAudiologyNo change
2R125R125.1Thoracic aortic aneurysm or dissectionThoracic aortic aneurysm or dissection (700)WES or Medium PanelSpecialisedCardiologyUpdate to gene content via PanelApp route
3R125R125.2Thoracic aortic aneurysm or dissectionThoracic aortic aneurysm or dissection (700)MLPA or equivalentSpecialisedCardiologyNo change
4R127R127.1Long QT syndromeLong QT syndrome (76)Small panelSpecialisedCardiologyNo change
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" + ], + "text/plain": [ + " Clinical indication ID Test ID Clinical Indication \\\n", + "0 R67 R67.1 Monogenic hearing loss \n", + "1 R67 R67.2 Monogenic hearing loss \n", + "2 R125 R125.1 Thoracic aortic aneurysm or dissection \n", + "3 R125 R125.2 Thoracic aortic aneurysm or dissection \n", + "4 R127 R127.1 Long QT syndrome \n", + "\n", + " Target/Genes Test Method \\\n", + "0 Hearing loss (126) WES or Large Panel \n", + "1 Hearing loss (126) MLPA or equivalent \n", + "2 Thoracic aortic aneurysm or dissection (700) WES or Medium Panel \n", + "3 Thoracic aortic aneurysm or dissection (700) MLPA or equivalent \n", + "4 Long QT syndrome (76) Small panel \n", + "\n", + " Commissioning category Specialist test group \\\n", + "0 Specialised Audiology \n", + "1 Specialised Audiology \n", + "2 Specialised Cardiology \n", + "3 Specialised Cardiology \n", + "4 Specialised Cardiology \n", + "\n", + " Changes since October 2022 v4 publication \n", + "0 Update to gene content via PanelApp route \n", + "1 No change \n", + "2 Update to gene content via PanelApp route \n", + "3 No change \n", + "4 No change " + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(df.columns[8:], axis=1, inplace=True)\n", + "df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's say that we want to get all the clinical indications that had changes i.e. remove the lines with the `No change`." + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Clinical indication IDTest IDClinical IndicationTarget/GenesTest MethodCommissioning categorySpecialist test groupChanges since October 2022 v4 publication
0R67R67.1Monogenic hearing lossHearing loss (126)WES or Large PanelSpecialisedAudiologyUpdate to gene content via PanelApp route
2R125R125.1Thoracic aortic aneurysm or dissectionThoracic aortic aneurysm or dissection (700)WES or Medium PanelSpecialisedCardiologyUpdate to gene content via PanelApp route
8R129R129.1Catecholaminergic polymorphic VTCatecholaminergic polymorphic VT (214)Small panelSpecialisedCardiologyUpdate to gene content via PanelApp route
12R131R131.1Hypertrophic cardiomyopathyHypertrophic cardiomyopathy - teen and adult (49)WES or Medium PanelSpecialisedCardiologyUpdate to gene content via PanelApp route
18R135R135.2Paediatric or syndromic cardiomyopathyCardiomyopathies - including childhood onset (...WGSSpecialisedCardiologyUpdate to gene content via PanelApp route
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" + ], + "text/plain": [ + " Clinical indication ID Test ID Clinical Indication \\\n", + "0 R67 R67.1 Monogenic hearing loss \n", + "2 R125 R125.1 Thoracic aortic aneurysm or dissection \n", + "8 R129 R129.1 Catecholaminergic polymorphic VT \n", + "12 R131 R131.1 Hypertrophic cardiomyopathy \n", + "18 R135 R135.2 Paediatric or syndromic cardiomyopathy \n", + "\n", + " Target/Genes Test Method \\\n", + "0 Hearing loss (126) WES or Large Panel \n", + "2 Thoracic aortic aneurysm or dissection (700) WES or Medium Panel \n", + "8 Catecholaminergic polymorphic VT (214) Small panel \n", + "12 Hypertrophic cardiomyopathy - teen and adult (49) WES or Medium Panel \n", + "18 Cardiomyopathies - including childhood onset (... WGS \n", + "\n", + " Commissioning category Specialist test group \\\n", + "0 Specialised Audiology \n", + "2 Specialised Cardiology \n", + "8 Specialised Cardiology \n", + "12 Specialised Cardiology \n", + "18 Specialised Cardiology \n", + "\n", + " Changes since October 2022 v4 publication \n", + "0 Update to gene content via PanelApp route \n", + "2 Update to gene content via PanelApp route \n", + "8 Update to gene content via PanelApp route \n", + "12 Update to gene content via PanelApp route \n", + "18 Update to gene content via PanelApp route " + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_with_changes = df[df[\"Changes since October 2022 v4 publication\"].str.contains(\"No change\")==False]\n", + "df_with_changes.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's break down the command line:" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Update to gene content via PanelApp route\n", + "1 No change\n", + "2 Update to gene content via PanelApp route\n", + "3 No change\n", + "4 No change\n", + "Name: Changes since October 2022 v4 publication, dtype: object" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Changes since October 2022 v4 publication\"].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(df[\"Changes since October 2022 v4 publication\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "3 True\n", + "4 True\n", + " ... \n", + "623 True\n", + "624 True\n", + "625 False\n", + "626 True\n", + "627 False\n", + "Name: Changes since October 2022 v4 publication, Length: 628, dtype: bool" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Changes since October 2022 v4 publication\"].str.contains(\"No change\")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(127, 8)" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_with_changes.shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have all the clinical indications that are going to require some work. We can see what type of work by looking at the same column." + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Update to gene content via PanelApp route',\n", + " 'Addition of semi-rapid testing pathway for WGS indications',\n", + " 'Test type reinstated',\n", + " 'New clinical indication added, panel line',\n", + " 'New clinical indication added, CNV line', 'Removed STR line',\n", + " 'Added Confirmatory STR testing ', 'Gene target addition',\n", + " 'New Clinical Indication added',\n", + " 'Addition of Semi-rapid testing pathway for WGS indications',\n", + " 'New clinical indication added',\n", + " 'POT1 gene target added, moved to small panel ',\n", + " 'POT1 gene target added', 'New test type added ',\n", + " 'Test type added for deep sequencing',\n", + " 'Removed STR line as STR to be analysed off of WGS',\n", + " 'Removed STR line as STRs to be analysed off of WGS',\n", + " 'RFC1 STR added', 'no change ',\n", + " 'Update to gene content via PanelApp route, Changed CI name',\n", + " 'Addition of semi-rapid testing pathway for WGS indications '],\n", + " dtype=object)" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_with_changes[\"Changes since October 2022 v4 publication\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21,)" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_with_changes[\"Changes since October 2022 v4 publication\"].unique().shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "21 \"different\" types of changes. It's still quite a lot.\n", + "\n", + "We can trim it down by filtering on the test method." + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(7,)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_27596/2676225020.py:11: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + " df_test_method_filtered = df_with_changes[df_with_changes[\"Test Method\"].str.contains(\"|\".join(filtering_string))]\n" + ] + }, + { + "data": { + "text/html": [ + "
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Clinical indication IDTest IDClinical IndicationTarget/GenesTest MethodCommissioning categorySpecialist test groupChanges since October 2022 v4 publication
0R67R67.1Monogenic hearing lossHearing loss (126)WES or Large PanelSpecialisedAudiologyUpdate to gene content via PanelApp route
2R125R125.1Thoracic aortic aneurysm or dissectionThoracic aortic aneurysm or dissection (700)WES or Medium PanelSpecialisedCardiologyUpdate to gene content via PanelApp route
8R129R129.1Catecholaminergic polymorphic VTCatecholaminergic polymorphic VT (214)Small panelSpecialisedCardiologyUpdate to gene content via PanelApp route
12R131R131.1Hypertrophic cardiomyopathyHypertrophic cardiomyopathy - teen and adult (49)WES or Medium PanelSpecialisedCardiologyUpdate to gene content via PanelApp route
18R135R135.2Paediatric or syndromic cardiomyopathyCardiomyopathies - including childhood onset (...WGSSpecialisedCardiologyUpdate to gene content via PanelApp route
...........................
595R139R139.1Laterality disorders and isomerismLaterality disorders and isomerism (549)WES or Medium PanelSpecialisedRespiratoryUpdate to gene content via PanelApp route
601R189R189.1Respiratory ciliopathies including non-CF bron...Respiratory ciliopathies including non-CF bron...WES or Medium PanelSpecialisedRespiratoryUpdate to gene content via PanelApp route
613R434R434.1Recurrent episodic apnoeaRecurrent episodic apnoea (1219)Small panelSpecialisedRespiratoryNew clinical indication added
625R412R412.1Fetal anomalies with a likely genetic cause - ...Fetal anomalies (478)WES or Large PanelSpecialisedSpecialisedUpdate to gene content via PanelApp route
627R441R441.1Unexplained death in infancy and sudden unexp...Unexplained death in infancy and sudden unexpl...WGSSpecialisedSpecialisedNew clinical indication added
\n", + "

86 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " Clinical indication ID Test ID \\\n", + "0 R67 R67.1 \n", + "2 R125 R125.1 \n", + "8 R129 R129.1 \n", + "12 R131 R131.1 \n", + "18 R135 R135.2 \n", + ".. ... ... \n", + "595 R139 R139.1 \n", + "601 R189 R189.1 \n", + "613 R434 R434.1 \n", + "625 R412 R412.1 \n", + "627 R441 R441.1 \n", + "\n", + " Clinical Indication \\\n", + "0 Monogenic hearing loss \n", + "2 Thoracic aortic aneurysm or dissection \n", + "8 Catecholaminergic polymorphic VT \n", + "12 Hypertrophic cardiomyopathy \n", + "18 Paediatric or syndromic cardiomyopathy \n", + ".. ... \n", + "595 Laterality disorders and isomerism \n", + "601 Respiratory ciliopathies including non-CF bron... \n", + "613 Recurrent episodic apnoea \n", + "625 Fetal anomalies with a likely genetic cause - ... \n", + "627 Unexplained death in infancy and sudden unexp... \n", + "\n", + " Target/Genes Test Method \\\n", + "0 Hearing loss (126) WES or Large Panel \n", + "2 Thoracic aortic aneurysm or dissection (700) WES or Medium Panel \n", + "8 Catecholaminergic polymorphic VT (214) Small panel \n", + "12 Hypertrophic cardiomyopathy - teen and adult (49) WES or Medium Panel \n", + "18 Cardiomyopathies - including childhood onset (... WGS \n", + ".. ... ... \n", + "595 Laterality disorders and isomerism (549) WES or Medium Panel \n", + "601 Respiratory ciliopathies including non-CF bron... WES or Medium Panel \n", + "613 Recurrent episodic apnoea (1219) Small panel \n", + "625 Fetal anomalies (478) WES or Large Panel \n", + "627 Unexplained death in infancy and sudden unexpl... WGS \n", + "\n", + " Commissioning category Specialist test group \\\n", + "0 Specialised Audiology \n", + "2 Specialised Cardiology \n", + "8 Specialised Cardiology \n", + "12 Specialised Cardiology \n", + "18 Specialised Cardiology \n", + ".. ... ... \n", + "595 Specialised Respiratory \n", + "601 Specialised Respiratory \n", + "613 Specialised Respiratory \n", + "625 Specialised Specialised \n", + "627 Specialised Specialised \n", + "\n", + " Changes since October 2022 v4 publication \n", + "0 Update to gene content via PanelApp route \n", + "2 Update to gene content via PanelApp route \n", + "8 Update to gene content via PanelApp route \n", + "12 Update to gene content via PanelApp route \n", + "18 Update to gene content via PanelApp route \n", + ".. ... \n", + "595 Update to gene content via PanelApp route \n", + "601 Update to gene content via PanelApp route \n", + "613 New clinical indication added \n", + "625 Update to gene content via PanelApp route \n", + "627 New clinical indication added \n", + "\n", + "[86 rows x 8 columns]" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtering_string = [\n", + " \"Medium panel\", \"Single gene sequencing <=10 amplicons\",\n", + " \"Single gene sequencing <10 amplicons\",\n", + " \"Single gene sequencing >=10 amplicons\",\n", + " \"Single gene testing (<10 amplicons)\", \"small panel\", \"Small panel\",\n", + " \"Small panel - deep sequencing\", \"WES or Large panel\", \"WES or Large Panel\",\n", + " \"WES or Large penel\", \"WES or Medium panel\", \"WES or Medium Panel\",\n", + " \"WES or Small Panel\", \"WGS\"\n", + "]\n", + "\n", + "df_test_method_filtered = df_with_changes[df_with_changes[\"Test Method\"].str.contains(\"|\".join(filtering_string))]\n", + "print(df_test_method_filtered[\"Changes since October 2022 v4 publication\"].unique().shape)\n", + "df_test_method_filtered" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing I'm interested in is if the targets for the clinical indications are panelapp panels or single genes.\n", + "Let's check that out." + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Clinical indication IDClinical IndicationTarget/Genes
158R144Congenital hyperinsulinismABCC8;KCNJ11
\n", + "
" + ], + "text/plain": [ + " Clinical indication ID Clinical Indication Target/Genes\n", + "158 R144 Congenital hyperinsulinism ABCC8;KCNJ11" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test_method_filtered[df_test_method_filtered[\"Test ID\"].str.contains(\"R144\")].loc[:, [\"Clinical indication ID\", \"Clinical Indication\", \"Target/Genes\"]]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These genes will need to be checked.\n", + "Let's extract them." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'BRCA1, BRCA2, MLH1, MSH2, MSH6, ATM, PALB2, CHEK2\\xa0'" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_single_genes = df_test_method_filtered[df_test_method_filtered[\"Target/Genes\"].str.contains(\"\\(\")==False]\n", + "df_single_genes.loc[:, \"Target/Genes\"].iloc[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like the encoding of the excel file is wrong. Let's fix that." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'BRCA1, BRCA2, MLH1, MSH2, MSH6, ATM, PALB2, CHEK2'" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_normalized = df_test_method_filtered.apply(lambda x: x.str.normalize(\"NFKD\").str.strip())\n", + "df_single_genes = df_normalized[df_normalized[\"Target/Genes\"].str.contains(\"\\(\")==False]\n", + "df_single_genes.loc[:, \"Target/Genes\"].iloc[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I also need to look at the panelapp panels and their content to see if any work on the panel database is necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['126',\n", + " '700',\n", + " '214',\n", + " '49',\n", + " '749',\n", + " '478',\n", + " '486',\n", + " '285',\n", + " '490',\n", + " '553',\n", + " '555',\n", + " '77',\n", + " '565',\n", + " '293',\n", + " '473',\n", + " '650',\n", + " '130',\n", + " '546',\n", + " '483',\n", + " '514',\n", + " '1217',\n", + " '508',\n", + " '508',\n", + " '525',\n", + " '157',\n", + " '519',\n", + " '518',\n", + " '398',\n", + " '526',\n", + " '528',\n", + " '529',\n", + " '467',\n", + " '533',\n", + " '534',\n", + " '535',\n", + " '536',\n", + " '537',\n", + " '538',\n", + " '539',\n", + " '168',\n", + " '196',\n", + " '309',\n", + " '269',\n", + " '496',\n", + " '147',\n", + " '465',\n", + " '466',\n", + " '488',\n", + " '540',\n", + " '847',\n", + " '474',\n", + " '402',\n", + " '567',\n", + " '568',\n", + " '579',\n", + " '22',\n", + " '207',\n", + " '225',\n", + " '258',\n", + " '488',\n", + " '78',\n", + " '179',\n", + " '491',\n", + " '162',\n", + " '230',\n", + " '307',\n", + " '509',\n", + " '186',\n", + " '3',\n", + " '487',\n", + " '106',\n", + " '292',\n", + " '548',\n", + " '149',\n", + " '678',\n", + " '549',\n", + " '550',\n", + " '1219',\n", + " '478',\n", + " '749']" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import regex\n", + "\n", + "df_panels = df_normalized[df_normalized[\"Target/Genes\"].str.contains(\"\\(\")==True]\n", + "\n", + "panelapp_panel_ids = []\n", + "\n", + "for panel in df_panels[\"Target/Genes\"]:\n", + " match = regex.search(r\"[0-9]+\", panel)\n", + " panelapp_panel_ids.append(match.group(0))\n", + "\n", + "panelapp_panel_ids" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More attributes of a Dataframe\n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lots of functions that can be applied to a Dataframe\n", + "\n", + "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples in our Github\n", + "\n", + "https://github.com/eastgenomics/test_directory_parser/blob/64dc19309af501bb980717ecc1dc19df3a12deda/test_directory_parser/rare_disease.py#L22\n", + "\n", + "https://github.com/eastgenomics/Ploutos/blob/be32e9313648c1bec52c24d6f8ab790f803cb690/Ploutos/scripts/leaderboard_plots.py#L1291\n", + "\n", + "https://github.com/eastgenomics/athena/blob/a6371df55390b030d03682de3abe5bedf6399077/bin/load_data.py#L85" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resources\n", + "\n", + "https://pandas.pydata.org/docs/user_guide/10min.html \n", + "\n", + "https://jakevdp.github.io/PythonDataScienceHandbook/03.01-introducing-pandas-objects.html" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pandas/td_v5.xlsx b/pandas/td_v5.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..287223bd92c620000439e7bc1cc747be68489951 GIT binary patch literal 151620 zcmeFZXIK+k+cs?5c0@r@KtK=_sY;O!Dos#8h0p^ky%R|2Wh){gO{vm_2nY$ihqh6Q zv|xmQQWXRO2uPQjZw>72ZNakd_jumt$NO;{Ycg5M%w*17<-E>og)5h~ZacVX`=*_n 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