Working.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "474819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Read in the combined.csv file\n",
    "df = pd.read_csv('combined.csv')\n",
    "\n",
    "#remove the unnamed column\n",
    "df = df.drop('Unnamed: 0', axis=1)\n",
    "\n",
    "#Count the number of 0s per column\n",
    "zero_sum = df.isnull().sum()\n",
    "\n",
    "#Count the number of rows in the dataframe\n",
    "num_rows = df.shape[0]\n",
    "print(num_rows)\n",
    "\n",
    "#Sort the values in descending order\n",
    "zero_sum = zero_sum.sort_values(ascending=True)\n",
    "\n",
    "#Print the values\n",
    "#print(zero_sum)\n",
    "\n",
    "#Cut the list in half\n",
    "zero_sum = zero_sum[0:37]\n",
    "\n",
    "#Plot the values\n",
    "zero_sum.plot(kind='barh', figsize=(10,10))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "502\n",
      "sobject_id    0\n",
      "Li_fe         0\n",
      "C_fe          0\n",
      "O_fe          0\n",
      "Na_fe         0\n",
      "Mg_fe         0\n",
      "Al_fe         0\n",
      "Si_fe         0\n",
      "K_fe          0\n",
      "Ca_fe         0\n",
      "Sc_fe         0\n",
      "Ti_fe         0\n",
      "Ti2_fe        0\n",
      "V_fe          0\n",
      "Cr_fe         0\n",
      "Mn_fe         0\n",
      "Co_fe         0\n",
      "Ni_fe         0\n",
      "Cu_fe         0\n",
      "Zn_fe         0\n",
      "Y_fe          0\n",
      "Zr_fe         0\n",
      "Ba_fe         0\n",
      "La_fe         0\n",
      "Ce_fe         0\n",
      "Nd_fe         0\n",
      "Sm_fe         0\n",
      "Eu_fe         0\n",
      "e_Li_fe       0\n",
      "e_C_fe        0\n",
      "e_O_fe        0\n",
      "e_Na_fe       0\n",
      "e_Mg_fe       0\n",
      "e_Al_fe       0\n",
      "e_Si_fe       0\n",
      "e_K_fe        0\n",
      "e_Ca_fe       0\n",
      "e_Sc_fe       0\n",
      "e_Ti_fe       0\n",
      "e_Ti2_fe      0\n",
      "e_V_fe        0\n",
      "e_Cr_fe       0\n",
      "e_Mn_fe       0\n",
      "e_Co_fe       0\n",
      "e_Ni_fe       0\n",
      "e_Cu_fe       0\n",
      "e_Zn_fe       0\n",
      "e_Y_fe        0\n",
      "e_Zr_fe       0\n",
      "e_Ba_fe       0\n",
      "e_La_fe       0\n",
      "e_Ce_fe       0\n",
      "e_Nd_fe       0\n",
      "e_Sm_fe       0\n",
      "e_Eu_fe       0\n",
      "z_Rzphi       0\n",
      "R_Rzphi       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Slice the dataframe to only include the columns with less than 120000 0s\n",
    "df = df.loc[:, (df.isnull().sum() < 400000)]\n",
    "\n",
    "#Remove the rows with nan values\n",
    "df = df.dropna()\n",
    "\n",
    "#Print the number of rows in the dataframe\n",
    "print(df.shape[0])\n",
    "\n",
    "#Count the number of 0s per column\n",
    "zero_sum = df.isnull().sum()\n",
    "print(zero_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sobject_id', 'Li_fe', 'C_fe', 'O_fe', 'Na_fe', 'Mg_fe', 'Al_fe', 'Si_fe', 'K_fe', 'Ca_fe', 'Sc_fe', 'Ti_fe', 'Ti2_fe', 'V_fe', 'Cr_fe', 'Mn_fe', 'Co_fe', 'Ni_fe', 'Cu_fe', 'Zn_fe', 'Y_fe', 'Zr_fe', 'Ba_fe', 'La_fe', 'Ce_fe', 'Nd_fe', 'Sm_fe', 'Eu_fe', 'e_Li_fe', 'e_C_fe', 'e_O_fe', 'e_Na_fe', 'e_Mg_fe', 'e_Al_fe', 'e_Si_fe', 'e_K_fe', 'e_Ca_fe', 'e_Sc_fe', 'e_Ti_fe', 'e_Ti2_fe', 'e_V_fe', 'e_Cr_fe', 'e_Mn_fe', 'e_Co_fe', 'e_Ni_fe', 'e_Cu_fe', 'e_Zn_fe', 'e_Y_fe', 'e_Zr_fe', 'e_Ba_fe', 'e_La_fe', 'e_Ce_fe', 'e_Nd_fe', 'e_Sm_fe', 'e_Eu_fe', 'z_Rzphi', 'R_Rzphi']\n"
     ]
    }
   ],
   "source": [
    "#Create a list of the column names\n",
    "col_names = list(df.columns.values)\n",
    "\n",
    "print(col_names)\n",
    "\n",
    "#Create a list of the abundances that will be used\n",
    "abundances = ['Li_fe', 'C_fe', 'O_fe', 'Na_fe', 'Mg_fe', 'Al_fe', 'Si_fe', 'K_fe', 'Ca_fe', 'Sc_fe', 'Ti_fe', 'Ti2_fe', 'V_fe', 'Cr_fe', 'Mn_fe', 'Co_fe', 'Ni_fe', 'Cu_fe', 'Zn_fe', 'Y_fe', 'Zr_fe', 'Ba_fe', 'La_fe', 'Ce_fe', 'Nd_fe', 'Sm_fe', 'Eu_fe']\n",
    "errors = ['e_Li_fe', 'e_C_fe', 'e_O_fe', 'e_Na_fe', 'e_Mg_fe', 'e_Al_fe', 'e_Si_fe', 'e_K_fe', 'e_Ca_fe', 'e_Sc_fe', 'e_Ti_fe', 'e_Ti2_fe', 'e_V_fe', 'e_Cr_fe', 'e_Mn_fe', 'e_Co_fe', 'e_Ni_fe', 'e_Cu_fe', 'e_Zn_fe', 'e_Y_fe', 'e_Zr_fe', 'e_Ba_fe', 'e_La_fe', 'e_Ce_fe', 'e_Nd_fe', 'e_Sm_fe', 'e_Eu_fe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove rows with abundances/errors that are less than 1\n",
    "for element in abundances:\n",
    "    df = df[df[element] > 1]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.9.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c6b8f4b41fd226cd8a00405a3866a3016371f69023e07bdde4d32b57757cce56"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}