Financial-Data-Analyis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial Data Analysis\n",
    "About: This is a module from Python A-Z™: Python For Data Science With Real Exercises! available at udemy.com\n",
    "       The code here is expressed based on my own interpretation of the lessons and guidance of the instructor.\n",
    "\n",
    "Copyright: These datasets were prepared using publicly available data. However, theses scripts are subject to Copyright Laws.\n",
    "\n",
    "Reference: Eremenko, 2018\n",
    "\n",
    "Retrieved: www.udemy.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments:\n",
    "\n",
    "Scenario:You are a Data Scientist working for a consulting firm. One of your colleagues from the Auditing department has asked you to help them assess the financial statement of organisation X.\n",
    "\n",
    "You have been supplied with two vectors of data: monthly revenue and monthly expenses for the financial year in question.Your task is to calculate the following financial metrics:\n",
    "\n",
    "- profit for each month\n",
    "- profit after tax for each month (the tax rate is 30%)\n",
    "- profit margin for each month - equals to profit after tax divided by revenue\n",
    "- good months - where the profit after tax was greater than the mean for the year - bad months - where the profit after tax was less than the mean for the year\n",
    "- the best month - where the profit after tax was max for the year\n",
    "- the worst month - where the profit after tax was min for the year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Financial Data Analyis and Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data \n",
    "months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] # included dates to map figures to acutal months\n",
    "revenue = [14574.49, 7606.46, 8611.41, 9175.41, 8058.65, 8105.44, 11496.28, 9766.09, 10305.32, 14379.96, 10713.97, 15433.50]\n",
    "expenses = [12051.82, 5695.07, 12319.20, 12089.72, 8658.57, 840.20, 3285.73, 5821.12, 6976.93, 16618.61, 10054.37, 3803.96]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create array for months\n",
    "mo = np.array(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  create array for revenue\n",
    "r = np.array(revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create array for expenses\n",
    "e = np.array(expenses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Profit\n",
    "p = np.array(np.subtract(r,e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monthly Profit after tax 30% using list comprehension\n",
    "#mp = [profit - (profit * 0.3) for profit in profit]\n",
    "mp = np.array([p - (p * 0.3) for p in p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Profit Margin \n",
    "# monthly profit / revenue\n",
    "pm = np.array([np.around(mp/r,2) * 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Good Months\n",
    "# m (mean), gm= good months\n",
    "m = np.mean(mp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "gm = [gm > m for gm in mp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bad Months\n",
    "# bm = bad months\n",
    "bm = [bm < m for bm in mp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Best Month\n",
    "# bmmax = best month maximum\n",
    "# bestm = best month\n",
    "bmmax = max(mp)\n",
    "bestm = [mp == bmmax for i in mp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Worst Month\n",
    "# wmmin = worst month minimum\n",
    "# worstm = worst month\n",
    "wmmin = min(mp)\n",
    "worstm = [mp == wmmin for wmmin in mp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert calculations to thousand dollars\n",
    "#pd.set_option('Precision',0)\n",
    "r1000 = [np.around(r/1000, decimals=2) for r in r]\n",
    "e1000 = [np.round(e/1000,2) for e in e]\n",
    "p1000 = [np.around(p/1000, decimals=2) for p in p]\n",
    "mp1000 = [np.around(mp, decimals=2) for mp in mp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "r1000 = [np.int(r) for r in r]\n",
    "e1000 = [np.int(e) for e in e]\n",
    "p1000 = [np.int(p) for p in p]\n",
    "mp1000 = [np.int(mp) for mp in mp]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revenue:\n",
      "[14574, 7606, 8611, 9175, 8058, 8105, 11496, 9766, 10305, 14379, 10713, 15433]\n",
      "Expenses:\n",
      "[12051, 5695, 12319, 12089, 8658, 840, 3285, 5821, 6976, 16618, 10054, 3803]\n",
      "Profit:\n",
      "[2522, 1911, -3707, -2914, -599, 7265, 8210, 3944, 3328, -2238, 659, 11629]\n",
      "Profit after tax:\n",
      "[1765, 1337, -2595, -2040, -419, 5085, 5747, 2761, 2329, -1567, 461, 8140]\n",
      "Profit margin:\n",
      "[[ 12.  18. -30. -22.  -5.  63.  50.  28.  23. -11.   4.  53.]]\n",
      "Good months:\n",
      "[True, False, False, False, False, True, True, True, True, False, False, True]\n",
      "Bad months:\n",
      "[False, True, True, True, True, False, False, False, False, True, True, False]\n",
      "Best month:\n",
      "[False False False False False False False False False False False  True]\n",
      "Worst month:\n",
      "[ True False False False False False False False False False False False]\n"
     ]
    }
   ],
   "source": [
    "# Print Results\n",
    "print (\"Revenue:\") \n",
    "print (r1000)\n",
    "print (\"Expenses:\") \n",
    "print (e1000)\n",
    "print (\"Profit:\")\n",
    "print(p1000)\n",
    "print (\"Profit after tax:\")\n",
    "print (mp1000)\n",
    "print (\"Profit margin:\")\n",
    "print (pm)\n",
    "print (\"Good months:\")\n",
    "print (gm)\n",
    "print (\"Bad months:\")\n",
    "print (bm)\n",
    "print (\"Best month:\")\n",
    "print(bestm[0])\n",
    "print (\"Worst month:\")\n",
    "print (worstm[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analyses (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objective: \n",
    "\n",
    "Simulate a dataframe build from the vectors created for months, revenues, and expenses\n",
    "\n",
    "Dump the dataframe to a csv file as the data type (object) resulting from the dataframe can not induce operations\n",
    "\n",
    "Create visualisations via seaborn package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: to build dataframe, Merge Months(m), Revenue(r), Expenses(e), Profit(p), MonthlyProfit(mp)\n",
    "#d = np.concatenate((m,r,e,p,mp))\n",
    "d = np.concatenate((mo,r1000,e1000,p1000,mp1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: to build dataframe, Create two-dimensional matrix for m,r,e,p,mp\n",
    "# 12 x 5 dimension, order=Fortran (Verticaly populated)\n",
    "re = np.reshape(d, (12,5), order='F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/rmph/Desktop/Projects - current/udemy/sds/Python/Section3\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Construct the dataframe and dump to csv\n",
    "import os\n",
    "print(os.getcwd())\n",
    "fda = (pd.DataFrame(re, columns=['Months','Revenue', 'Expenses', 'ProfitAfterTax', 'MonthlyProfit']))\n",
    "#fda.to_csv('/Users/rmph/Desktop/Projects - current/udemy/sds/Python/Section3/fda.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Months            int64\n",
       "Revenue           int64\n",
       "Expenses          int64\n",
       "ProfitAfterTax    int64\n",
       "MonthlyProfit     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4: load dataset and check the data types\n",
    "fda = pd.read_csv('fda.csv')\n",
    "fda.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Months</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>ProfitAfterTax</th>\n",
       "      <th>MonthlyProfit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14574</td>\n",
       "      <td>12051</td>\n",
       "      <td>2522</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7606</td>\n",
       "      <td>5695</td>\n",
       "      <td>1911</td>\n",
       "      <td>1337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8611</td>\n",
       "      <td>12319</td>\n",
       "      <td>-3707</td>\n",
       "      <td>-2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9175</td>\n",
       "      <td>12089</td>\n",
       "      <td>-2914</td>\n",
       "      <td>-2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8058</td>\n",
       "      <td>8658</td>\n",
       "      <td>-599</td>\n",
       "      <td>-419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>8105</td>\n",
       "      <td>840</td>\n",
       "      <td>7265</td>\n",
       "      <td>5085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>11496</td>\n",
       "      <td>3285</td>\n",
       "      <td>8210</td>\n",
       "      <td>5747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>9766</td>\n",
       "      <td>5821</td>\n",
       "      <td>3944</td>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>10305</td>\n",
       "      <td>6976</td>\n",
       "      <td>3328</td>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>14379</td>\n",
       "      <td>16618</td>\n",
       "      <td>-2238</td>\n",
       "      <td>-1567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>10713</td>\n",
       "      <td>10054</td>\n",
       "      <td>659</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15433</td>\n",
       "      <td>3803</td>\n",
       "      <td>11629</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Months  Revenue  Expenses  ProfitAfterTax  MonthlyProfit\n",
       "0        1    14574     12051            2522           1765\n",
       "1        2     7606      5695            1911           1337\n",
       "2        3     8611     12319           -3707          -2595\n",
       "3        4     9175     12089           -2914          -2040\n",
       "4        5     8058      8658            -599           -419\n",
       "5        6     8105       840            7265           5085\n",
       "6        7    11496      3285            8210           5747\n",
       "7        8     9766      5821            3944           2761\n",
       "8        9    10305      6976            3328           2329\n",
       "9       10    14379     16618           -2238          -1567\n",
       "10      11    10713     10054             659            461\n",
       "11      12    15433      3803           11629            814"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataframe\n",
    "fda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Months</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>ProfitAfterTax</th>\n",
       "      <th>MonthlyProfit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>14574</td>\n",
       "      <td>12051</td>\n",
       "      <td>2522</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>7606</td>\n",
       "      <td>5695</td>\n",
       "      <td>1911</td>\n",
       "      <td>1337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>8611</td>\n",
       "      <td>12319</td>\n",
       "      <td>-3707</td>\n",
       "      <td>-2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>9175</td>\n",
       "      <td>12089</td>\n",
       "      <td>-2914</td>\n",
       "      <td>-2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>8058</td>\n",
       "      <td>8658</td>\n",
       "      <td>-599</td>\n",
       "      <td>-419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>8105</td>\n",
       "      <td>840</td>\n",
       "      <td>7265</td>\n",
       "      <td>5085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>11496</td>\n",
       "      <td>3285</td>\n",
       "      <td>8210</td>\n",
       "      <td>5747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>9766</td>\n",
       "      <td>5821</td>\n",
       "      <td>3944</td>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>10305</td>\n",
       "      <td>6976</td>\n",
       "      <td>3328</td>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>14379</td>\n",
       "      <td>16618</td>\n",
       "      <td>-2238</td>\n",
       "      <td>-1567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>10713</td>\n",
       "      <td>10054</td>\n",
       "      <td>659</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>15433</td>\n",
       "      <td>3803</td>\n",
       "      <td>11629</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Months  Revenue  Expenses  ProfitAfterTax  MonthlyProfit\n",
       "0     Jan    14574     12051            2522           1765\n",
       "1     Feb     7606      5695            1911           1337\n",
       "2     Mar     8611     12319           -3707          -2595\n",
       "3     Apr     9175     12089           -2914          -2040\n",
       "4     May     8058      8658            -599           -419\n",
       "5     Jun     8105       840            7265           5085\n",
       "6     Jul    11496      3285            8210           5747\n",
       "7     Aug     9766      5821            3944           2761\n",
       "8     Sep    10305      6976            3328           2329\n",
       "9     Oct    14379     16618           -2238          -1567\n",
       "10    Nov    10713     10054             659            461\n",
       "11    Dec    15433      3803           11629            814"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import calendar\n",
    "# map the months from calendar to actual values in the months column\n",
    "fda.Months = fda['Months'].apply(lambda x: calendar.month_abbr[x])\n",
    "fda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the packages for visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize']=8,4\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualise Distributions for all the Variables\n",
    "\n",
    "MonthlyProfit is Gaussian Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 948.75x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpviz = sns.pairplot(fda, hue='Months')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monthly Profit\n",
    "\n",
    "Normal distribution for Monthly Profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpviz = sns.distplot(fda[\"MonthlyProfit\"], bins=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monthly Profit After Taxes\n",
    "Profit After Tax is skewed to the left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patviz = sns.distplot(fda[\"ProfitAfterTax\"], bins=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profit after tax over a year period\n",
    "\n",
    "Highest Profit After Tax achieved is on December at 11629 which is the Fiscal Year End.\n",
    "\n",
    "While the March has the highest deferred profit at -3707.\n",
    "\n",
    "lowest earnings were recorded for November and May respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patviz1 = sns.barplot(data=fda, x='ProfitAfterTax', y='Months', orient='h')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}