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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# <center> Analysis of the presence of cysteines in mitochondrial proteins </center>\n",
-    "\n",
-    "## <center> Bioinformatics project fall/spring 2021: </center>\n",
-    "\n",
-    "### <center> Jouni Kettunen </center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Research questions:\n",
-    "<b> Question 1: </b> Could cysteines have a specific function in mitochondrial (MT) proteins, even if they would not form disulfides in the reducing environment inside the mitochondrion?\n",
-    "\n",
-    "<b> Question 2: </b> Is the amino acid frequency of cysteine in MT proteins different from frequency in cytoplasmic proteins or extracellular proteins?\n",
-    "\n",
-    "<b> Question 3: </b> Other than Q2's locations, what other location decribing GO's exist\n",
-    "\n",
-    "<b> Question 4: </b> Find non-overlapping GO's for proteins\n",
-    "\n",
-    "<b> Question 5: </b> Find and show distribution for every Protein Existence code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <center> Intro </center>\n",
-    "\n",
-    "<b> Determing the annotations </b>\n",
-    "\n",
-    "Protein descriptions includes annotations called Gene Ontology annotations - GO-terms - which are used to describe protein in: biological process, molecular function and cellular component. Each of the three main levels include several sub levels that are divided to sub levels and so forth. \n",
-    "\n",
-    "Cellular component describe - as name indicates - where in the cell the protein is located. Using the knowledge that goal is to identify proteins based on their location, other two main levels could be abandoned. Search could be then be refined to molecular components with using keywords “mitochondrial matrix” for mitochondrial proteins, “cytoplasm” for proteins located in cytoplasm. Finding proteins that are secreted required the knowledge that they are secreted outside the cell after synthesis in a cell; this means that keywords “extracellular region” and “extracellular space” would be relevant for searching secreted proteins. \n",
-    "\n",
-    "Conducting a Quick GO search with the keywords yielded following terms to be be used for the classification:\n",
-    "- Mitochondrial matrix: 'GO:0005759' or 'GO:0031980'\n",
-    "- Cytosol: ‘GO: 0005829’\n",
-    "- Secreted: 'GO:0005576' or 'GO:0005615'\n",
-    "\n",
-    "\n",
-    "<b> Finding the proteome </b>\n",
-    "\n",
-    "The classification was meant to be done proteome wide, meaning that one needs to find and access to the whole human proteome. Information for the Human proteome comes from a variety of sources (eg. Human Proteome Map, ProteomicsDB and The Human Proteome Project (HPP)) aiming to map the whole human proteome. The Human Proteome Organization (HUPO) announced in 2020 the human proteome to be 90.4% complete. HUPO doesn’t have the human proteome in its site, meaning that some other resource is needed to handle the data.\n",
-    "\n",
-    "To find the data one needs to access to the Universal Protein Resource (UniProt). UniProt is a database designed to hold protein sequence and annotation data and allow researchers access to the data. Accessing can be done by downloading designed datasets directly to personal use or programmatically coding the desired parts. The UniProt holds and maintains a dataset that includes the whole human proteome (https://www.uniprot.org/proteomes/UP000005640) this dataset can be downloaded as in FASTA (holding only the sequences) or in XML (holding the metadata including GO-terms). Other way to access the compressed version of the human proteome is from the FTP site that updates every eight weeks (https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/). \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <center> Materials & Methods </center>\n",
-    "\n",
-    "<b> Accessing the human proteome (Biopython)</b>\n",
-    "\n",
-    "Accessing to the GO-terms requires a programmatical approach; the files are: too big to run in Excel/other XML-readers or don’t include GO-terms (FASTA) and manual search is nearly impossibly laborious. For the purposes of this project an extension of Python program - Biopython - was selected. The Biopython makes possible to acces and manipulate the UniProt files by parsing them in human friendly matter. Biopython also includes several ready made scripts that one can use for biological computation; one of them being Bio.SeqUtils.ProtParam module that was used for counting protein frequencies.\n",
-    "\n",
-    "Biopython doesn't have a GO-library (OBO parser), but one can install it using pip to make GO-parser a locally run library. Tjis project uses GOAtools by Tang et.al. https://github.com/tanghaibao/goatools. The GO-library can used for \"decrypting\" the GO-terms, but for this project GO-terms were taken directly from the Quick-GO so GO's didn't require any identification. Terms were collected with string mathching where a string parsed needs to match to the defined string (eg. item == 'GO:0031980') and valued with boolean condition <b> TRUE </b> to be taken into account. GO-module was used to visualize relationships between terms.\n",
-    "\n",
-    "<b> Accessing the datafile </b>\n",
-    "\n",
-    "The Biopython allows user to acces files directly over the internet or read/parse files locally. Accessing via internet requires user to determine the names of the proteins and in the case of this project, it would have been double the work. Also the human proteome is relatively large dataset to access and using online searches might have taken a long period. For these reasons and for local copy allowing creation of training set, a local approach was chosen. As the GO -searches were decided to be done locally, XML format was selected to be used as a source type for file downolad. \n",
-    "\n",
-    "XML-file is composed of a tree-like format where a main body contains several branches that branch further and have desired values as leaves. To answer the research questions, following values needed to be extracted from the XML-file: name of the protein, location described as GO-term, amino acid sequence, Uniprot ID and protein existence level.     \n",
-    "\n",
-    "<b> Storing data </b>\n",
-    "\n",
-    "To meet the data-analysis needs in Python, a library named Pandas has been built making possible to build, manipulate and analyse data structures. This project uses specially Pandas series for saving parsed data and data frames that are build from the saved series. The XML-file is as described like an onion and data extraction like peeling the onion; each time that a layer matching to condition was met, the described values were ectracted from it and saved as a series object to a Python list. Once the onion peeling was completed (ie. no more proteins that meet the condition), the list holding Pandas series objects was transformed to a Pandas data frame. Pandas also enables effective sorting and filtering of the data frame.\n",
-    "\n",
-    "As Python keeps data in memory only for the ongoing session, data frame was saved for safety reasons and for future purposes so that there wouldn't be need to do GO-search everytime the project file is opened. A compressed binary formatted parquet -file was chosen for its abilites to pack data compactly, be a robust storing/sharing format and 100 % Pandas compatible when read to Python. A downside is that the file isn't human readable and Python doesn't have inbuilt extension for Parquet; one must install a Pyarrow library to hande parquet files.\n",
-    "\n",
-    "<b> Visualizing data </b>\n",
-    "\n",
-    "Pandas dataframe enables filtering to conveniently analyze and visualize data by using libraries. The chosen visualization libraries - seaborn and pyplot - are based on matplotlib and allow flexible and versatile function for visualizing data (eg. frequencies).\n",
-    "\n",
-    "<b> Statistical analyzes </b>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <center> Results </center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### <center> Modules for the project </center>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# To make sure that project runs properly, determine the location of the Python packages.\n",
-    "import sys\n",
-    "\n",
-    "# Use the location of Python.\n",
-    "sys.path.append('C:\\\\Python\\\\lib\\\\site-packages')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Biopython, input and output assorted sequence file formats for handling SeqRecord objects \n",
-    "import Bio.SeqIO as BSqIO\n",
-    "\n",
-    "# Biopython protein analysis module\n",
-    "from Bio.SeqUtils.ProtParam import ProteinAnalysis\n",
-    "\n",
-    "# non-interactive utility to download remote files from the internet (GO-parser)\n",
-    "import wget\n",
-    "\n",
-    "# for using operating system dependent functionality (GO-parser, file existence)\n",
-    "import os\n",
-    "\n",
-    "# Explain better why this module\n",
-    "import requests as R\n",
-    "\n",
-    "# creating series and data frames\n",
-    "import pandas as pd\n",
-    "\n",
-    "# data visualization\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# data visualization\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# data handling\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### <center> GO-parser"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# GOAtools is used in this project for handling the obo file\n",
-    "# for GOAtools use - pip install goatools - to install tols locally run library\n",
-    "# for more info see https://github.com/tanghaibao/goatools\n",
-    "\n",
-    "go_obo_url = 'http://purl.obolibrary.org/obo/go/go-basic.obo'\n",
-    "data_folder = os.getcwd() + '/data'\n",
-    "\n",
-    "# makes a local data folder\n",
-    "\n",
-    "# Check if we have the ./data directory already\n",
-    "if(not os.path.isfile(data_folder)):\n",
-    "    # Emulate mkdir -p (no error if folder exists)\n",
-    "    try:\n",
-    "        os.mkdir(data_folder)\n",
-    "    except OSError as e:\n",
-    "        if(e.errno != 17):\n",
-    "            raise e\n",
-    "else:\n",
-    "    raise Exception('Data path (' + data_folder + ') exists as a file. '\n",
-    "                   'Please rename, remove or change the desired location of the data path.')\n",
-    "\n",
-    "# Check if the file exists already\n",
-    "if(not os.path.isfile(data_folder+'/go-basic.obo')):\n",
-    "    go_obo = wget.download(go_obo_url, data_folder+'/go-basic.obo')\n",
-    "else:\n",
-    "    go_obo = data_folder+'/go-basic.obo'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\JK\\Desktop\\BimePro/data/go-basic.obo: fmt(1.2) rel(2021-10-26) 47,197 GO Terms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import the OBO parser from GOATools\n",
-    "from goatools import obo_parser\n",
-    "# create a parser\n",
-    "go = obo_parser.GODag(go_obo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 21 GO-terms of interest to exclude the rest\n",
-    "# 'GO:0005576', \"GO:0005829\", 'GO:0005759', 'GO:0031980' to the BP\n",
-    "\n",
-    "goterms = [\"GO:0032588\", \"GO:0031965\", \"GO:0031966\", \"GO:0005789\", \"GO:0012506\", \"GO:0000139\",\n",
-    "      \"GO:0032580\",\"GO:0005802\", \"GO:0005796\", \"GO:0043202\", \"GO:0005765\", \"GO:0005778\",\n",
-    "      \"GO:0005777\", \"GO:0005741\", \"GO:0005743\", 'GO:0005576', \"GO:0005829\", 'GO:0005759', \n",
-    "           'GO:0031980', 'GO:0005634', 'GO:0005801']\n",
-    "\n",
-    "# dictionary to re-code the proteinExistence labels, corresbond to the Uniprot labels\n",
-    "prex = {\"evidence at protein level\": 1, \"evidence at transcript level\": 2, \"inferred from homology\": 3, \"predicted\": 4, \"uncertain\": 5 }\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# empty list to hold the series objects\n",
-    "interests = []\n",
-    "\n",
-    "# iterate first layer from the XML file holding the proteome\n",
-    "for rec in BSqIO.parse(\"UP000005640_9606.xml\", \"uniprot-xml\"):\n",
-    "\n",
-    "    # start 2nd iteration from the dbxref section that contains\n",
-    "    # the additional info, including GO terms\n",
-    "    for ref in rec.dbxrefs:\n",
-    "\n",
-    "           # see if the iteratable item starts with GO: (in a format GO:GO)\n",
-    "            # start from 3rd index and see if the term is in the list \n",
-    "            if ref.startswith(\"GO:\") and ref[3:] in goterms:\n",
-    "\n",
-    "                    # convert sequence containing seq object to string\n",
-    "                    T = str(rec.seq)\n",
-    "                    # attach variable holding the string formatted sequence to ProteinAnalysis\n",
-    "                    X = ProteinAnalysis(T)\n",
-    "                    # count AA-frequencies and make dictionary d hold them\n",
-    "                    d = X.get_amino_acids_percent()\n",
-    "                    # keys are d[] and values come after =\n",
-    "                    d['Location'] = go[ref[3:]].name\n",
-    "                    d['Length'] = len(T)\n",
-    "                    # Re-code the protein existence, get value with key\n",
-    "                    d['PE'] = prex.get(\n",
-    "                        rec.annotations.get('proteinExistence')[0])\n",
-    "                    d['UniprotID'] = rec.id\n",
-    "\n",
-    "                    # append Series object to a list, object formed from the dictionary\n",
-    "                    interests.append(pd.Series(d, name=rec.name))\n",
-    "\n",
-    "# make the list of series objects to a dataframe\n",
-    "interestDF = pd.DataFrame(interests)\n",
-    "\n",
-    "# write the dfs to parquet form (not human readable) and compress to save space\n",
-    "\n",
-    "interestDF.to_parquet('interestDF.parquet.gzip', compression='gzip')\n",
-    "# restDF.to_parquet('restDF.parquet.gzip', compression='gzip')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# additional run to collect those proteins that aren't on the list \n",
-    "rest = []\n",
-    "\n",
-    "for rec in BSqIO.parse(\"UP000005640_9606.xml\", \"uniprot-xml\"):      \n",
-    "        # proceed from here only if the id hasn't been added previously\n",
-    "    if rec.id not in interestDF['UniprotID'].values:\n",
-    "            # empty dict to collect the DF data\n",
-    "            p = {}\n",
-    "            # prevent dublicates: add to list only if the key isn't in the list of dictionaries\n",
-    "            #if not any(p['UniprotID'] == rec.id for p in rest):\n",
-    "            p['proteinExistence'] = prex.get(\n",
-    "            rec.annotations.get('proteinExistence')[0])\n",
-    "            p['UniprotID'] = rec.id\n",
-    "            rest.append(pd.Series(p, name=rec.name))\n",
-    "\n",
-    "restDF = pd.DataFrame(rest)\n",
-    "\n",
-    "restDF.to_parquet('restDF.parquet.gzip', compression='gzip')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <center> Prosessing data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openInterest = pd.read_parquet('allInterest.parquet.gzip')\n",
-    "openRest = pd.read_parquet('restDF.parquet.gzip')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#\"labels\"\n",
-    "AAs = openInterest.columns.values.tolist()\n",
-    "AAs = AAs[0:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Count occurences vs. protein\n",
-    "\n",
-    "countLoc = openInterest.groupby(['UniprotID']).count()[\n",
-    "    'Location'].reset_index(name='Size')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Location\n",
-       "vesicle membrane                    29\n",
-       "peroxisomal membrane                65\n",
-       "cis-Golgi network                   66\n",
-       "Golgi cisterna membrane             93\n",
-       "lysosomal lumen                     94\n",
-       "trans-Golgi network membrane        99\n",
-       "Golgi lumen                        102\n",
-       "peroxisome                         106\n",
-       "mitochondrial membrane             118\n",
-       "mitochondrial outer membrane       174\n",
-       "trans-Golgi network                178\n",
-       "nuclear membrane                   232\n",
-       "lysosomal membrane                 327\n",
-       "mitochondrial matrix               375\n",
-       "mitochondrial inner membrane       401\n",
-       "Golgi membrane                     583\n",
-       "endoplasmic reticulum membrane     970\n",
-       "extracellular region              1928\n",
-       "cytosol                           5271\n",
-       "nucleus                           5339\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "groupedInterest = openInterest.groupby('Location')\n",
-    "groupedInterest.size().sort_values()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define the condtion to filter the original DF\n",
-    "mimat = openInterest['Location'] == 'mitochondrial matrix'\n",
-    "minme = openInterest['Location'] == 'mitochondrial inner membrane'\n",
-    "gome = openInterest['Location'] == 'Golgi membrane'\n",
-    "ermem = openInterest['Location'] == 'endoplasmic reticulum membrane'\n",
-    "ereg = openInterest['Location'] == 'extracellular region'\n",
-    "cyto = openInterest['Location'] == 'cytosol'\n",
-    "nuc = openInterest['Location'] == 'nucleus'\n",
-    "\n",
-    "# make a new df by condition\n",
-    "toKeep = openInterest[mimat | minme | gome | ermem | ereg | cyto | nuc]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <center> Boxplotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Melt the dataframe to make it Seaborn friendly.\n",
-    "melted_toKeep = pd.melt(toKeep, id_vars=['Location'],\n",
-    "              value_vars=AAs, var_name='Amino acids')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2880x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set the background theme such that the gridlines are visible.\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "\n",
-    "# Adjust the figure size 40 ppt in width, 10 in heigth.\n",
-    "plt.figure(figsize=(40, 10))\n",
-    "\n",
-    "# Make the boxplot: set labels, data from the melted data frame, group by (hue) location, \n",
-    "# show outliers, select the color and the width of the outlier line.\n",
-    "ax = sns.boxplot(x='Amino acids', y='value', data=melted_toKeep,\n",
-    "                 hue='Location', showfliers=True, color='tomato', fliersize=5)\n",
-    "\n",
-    "# Create the legend and attach it to the desired spot on the box plot. \n",
-    "plt.legend(bbox_to_anchor=(1.04, 0.5), loc=\"center left\", borderaxespad=0)\n",
-    "\n",
-    "# Limit the y-axis to 0.4 to make plot more scalable.\n",
-    "ax.set(ylim=(0, 0.4))\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Arange the AA's by hydrophobicity.\n",
-    "\n",
-    "# Non-polar AA labels aka. hydrophobic.\n",
-    "nopolab = ['A', 'I', 'L', 'M', 'F', 'W', 'Y', 'V']\n",
-    "\n",
-    "# Polar labels aka. hydrophilic.\n",
-    "polab = ['S', 'T', 'N', 'Q']\n",
-    "\n",
-    "# Get the nonpolar AA's and the location column from the original dataframe. \n",
-    "nonpolar = toKeep[['A', 'I', 'L', 'M', 'F', 'W', 'Y', 'V', 'Location']]\n",
-    "\n",
-    "# Get the polar AA's and the location column from the original dataframe.\n",
-    "polar = toKeep[['S', 'T', 'N', 'Q', 'Location']]\n",
-    "\n",
-    "melted_nonpolar = pd.melt(nonpolar, id_vars=['Location'],\n",
-    "                        value_vars= nopolab, var_name='Amino acids')\n",
-    "\n",
-    "melted_polar = pd.melt(polar, id_vars=['Location'],\n",
-    "                        value_vars=polab, var_name='Amino acids')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 5760x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,2, figsize=(80, 10))\n",
-    "\n",
-    "fig.suptitle('AAs by polarity')\n",
-    "\n",
-    "ax1 = sns.boxplot(ax=axes[0], x='Amino acids', y='value', data=melted_nonpolar,\n",
-    "                  hue='Location', showfliers=True, color='tomato', fliersize=5).set(\n",
-    "                      ylim=(0, 0.4), title='Non-polar AAs')\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.04, 1.5), loc=\"center right\", borderaxespad=0)\n",
-    "\n",
-    "ax2 = sns.boxplot(ax=axes[1], x='Amino acids', y='value', data=melted_polar,\n",
-    "                  hue='Location', showfliers=True, color='tomato', fliersize=5).set(\n",
-    "                      ylim=(0, 0.4), title='Polar AAs')\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.04, 0.5), loc=\"center left\", borderaxespad=0)\n",
-    "\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Filter those proteins that has any AA frequency larger than 40%.\n",
-    "a = openInterest['A'] >= 0.4\n",
-    "c = openInterest['C'] >= 0.4\n",
-    "d = openInterest['D'] >= 0.4\n",
-    "e = openInterest['E'] >= 0.4\n",
-    "f = openInterest['F'] >= 0.4\n",
-    "g = openInterest['G'] >= 0.4\n",
-    "h = openInterest['H'] >= 0.4\n",
-    "i = openInterest['I'] >= 0.4\n",
-    "k = openInterest['K'] >= 0.4\n",
-    "l = openInterest['L'] >= 0.4\n",
-    "m = openInterest['M'] >= 0.4\n",
-    "n = openInterest['N'] >= 0.4\n",
-    "p = openInterest['P'] >= 0.4\n",
-    "q = openInterest['Q'] >= 0.4\n",
-    "r = openInterest['R'] >= 0.4\n",
-    "s = openInterest['S'] >= 0.4\n",
-    "t = openInterest['T'] >= 0.4\n",
-    "v = openInterest['V'] >= 0.4\n",
-    "w = openInterest['W'] >= 0.4\n",
-    "y = openInterest['Y'] >= 0.4\n",
-    "\n",
-    "\n",
-    "Non_AA = openInterest[a | c | d | e | f | g | h | i |\n",
-    "                      k | l | m | n | p | q | r | s | t | v | w | y]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <center> Statistics"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "38740d3277777e2cd7c6c2cc9d8addf5118fdf3f82b1b39231fd12aeac8aee8b"
-  },
-  "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": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}