106 lines
52 KiB
Plaintext
106 lines
52 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "This method only works with the ScalarFormatter",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"File \u001b[0;32m~/opt/anaconda3/envs/visual-analytics/lib/python3.11/site-packages/matplotlib/axes/_base.py:3262\u001b[0m, in \u001b[0;36m_AxesBase.ticklabel_format\u001b[0;34m(self, axis, style, scilimits, useOffset, useLocale, useMathText)\u001b[0m\n\u001b[1;32m 3261\u001b[0m \u001b[39mif\u001b[39;00m is_sci_style \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 3262\u001b[0m axis\u001b[39m.\u001b[39;49mmajor\u001b[39m.\u001b[39;49mformatter\u001b[39m.\u001b[39;49mset_scientific(is_sci_style)\n\u001b[1;32m 3263\u001b[0m \u001b[39mif\u001b[39;00m scilimits \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
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"\u001b[0;31mAttributeError\u001b[0m: 'FuncFormatter' object has no attribute 'set_scientific'",
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"\nThe above exception was the direct cause of the following exception:\n",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[22], line 42\u001b[0m\n\u001b[1;32m 39\u001b[0m plt\u001b[39m.\u001b[39mshow()\n\u001b[1;32m 41\u001b[0m ticker_list \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mMETA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAAPL\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mKO\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m---> 42\u001b[0m compare_balance_sheets(ticker_list)\n",
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"Cell \u001b[0;32mIn[22], line 36\u001b[0m, in \u001b[0;36mcompare_balance_sheets\u001b[0;34m(ticker_list)\u001b[0m\n\u001b[1;32m 33\u001b[0m ax\u001b[39m.\u001b[39mgrid(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 35\u001b[0m \u001b[39m# Remove scientific notation\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m ax\u001b[39m.\u001b[39;49mticklabel_format(style\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mplain\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m 38\u001b[0m plt\u001b[39m.\u001b[39mtight_layout() \u001b[39m# Adjust subplot spacing\u001b[39;00m\n\u001b[1;32m 39\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
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"File \u001b[0;32m~/opt/anaconda3/envs/visual-analytics/lib/python3.11/site-packages/matplotlib/axes/_base.py:3272\u001b[0m, in \u001b[0;36m_AxesBase.ticklabel_format\u001b[0;34m(self, axis, style, scilimits, useOffset, useLocale, useMathText)\u001b[0m\n\u001b[1;32m 3270\u001b[0m axis\u001b[39m.\u001b[39mmajor\u001b[39m.\u001b[39mformatter\u001b[39m.\u001b[39mset_useMathText(useMathText)\n\u001b[1;32m 3271\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3272\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[1;32m 3273\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mThis method only works with the ScalarFormatter\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
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"\u001b[0;31mAttributeError\u001b[0m: This method only works with the ScalarFormatter"
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]
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},
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{
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"data": {
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fv3zta19Lt27dSmOefvrpJCvfeXnIIYfUms+CBQvy5z//Ob17917jfNdl/9SfzfGcWRcrVqxYrzmdcsopad68eZLkxBNPzB//+Me8++67mTlzZunvAWhYq87Rqqqq0vfJyivGt2jRYo3jq6qqVtteWVlZeqMFrK+6Ho+r1NTU5IorrshVV12Vk046KUcfffSGniqbgboejxdddFF23HHHfOMb36i3ObL5qOvx2KxZs3zwwQcZPXp06SpTo0ePzpAhQzJp0qQcf/zx9TNxNkl1PR7vvffe3HrrrXn00UdLn+hz9dVX57Of/WwmTpyYb3/72/UzcYj1DBuW9TVFY41NkVhjUyTW2BSJNTYbs09qTSN2Zr2tugpso0aNaoVeLVq0yKc+9am8/fbbH/vOpKVLl+bCCy/M7373u1RWVmb77bcvhb//LhiaOHFiaczXv/711W6/4447Mnz48DRt2jR77713Lr744lxzzTV57rnn8txzzyVJWrdunbPOOiuHHnpott5661x//fW57LLLMm3atNx000256aab0qxZsxx++OE577zzMn/+/NL+V12x8MNWfVzAmqzL/lcFp2y6GvKc+TjvvvtukpTeab3qWK+url7jx/x8dNtH36HdoUOHJPFOsI1Qu3btSt+v7aPyZs2ale7du9f6mfXh42/ZsmVr3X/btm3X6bb333+/FN9/+GfvKmv6efvhq8qu+kX+350XCxcuzAMPPJAkefzxx9OlS5daty9dujR33313KdofPXp0Lrnkkjz88MOZNGlSJk2alGRl8H/ZZZelTZs2+c53vpNly5ZlwoQJeeSRR/LII48kSXbaaaf8/Oc/z5577ll6TkuWLCm9+/LDPu7jtdZl/9Sfze2c+Tgfftw2bdr8x3Na9eaDJPnggw/We17AJ2vVVeznzp2bT3/606Xtc+fOTdeuXVcb36FDhzz00EO1tlVVVeW9996rdZ7D+qjr8ZisXOOce+65ueeee3LWWWfluOOOq5e5sumr6/E4ceLElJWVpWfPnkn+583HX/rSl/KVr3wlo0aNqodZs6lan9frpk2b1vo43ebNm2f77bfPm2++ueEnzCatrsfjtGnTstNOO5X+J2yy8t/Wd9ppp7z++usbfL7wYdYzbEjW1xSNNTZFYo1NkVhjUyTW2GzMPqk1TeNPemJsPlYFKTU1NXnrrbdK25csWVKKOleFOGsKeX/5y19mwoQJ2WmnnfLYY4/loYceWusVbT9sxYoVpcBsbebNm1cKwJLkkEMOyaRJkzJ58uT89Kc/zQEHHJD33nsvP/rRj0pRWc+ePXPNNdfkj3/8Y8aMGZOjjjoqNTU1ufnmm3P//ffXioruueeezJgxIzNmzMjzzz+fGTNmlIK5tUXL/27/bPoa6pz5d5599tkkSY8ePWrNYffddy8d5zNmzMhzzz2XGTNmrHYF8w8/l+R/3gzQunXrtT4Ximm77bYr/VL88MMPr3b7b37zmxx++OE54IADUllZWfpv++F3X31c5P7hdxd+3G3bbrttmjRpkmTllfRXHYN/+tOfMn369Pz5z39e7f6rxq/Nmo7De++9d42x8YeNHz++9H379u1z4YUXpqKiIjfccENOPfXUfOpTn8qTTz6ZMWPGJFl59d3hw4fnsccey29/+9uce+656datW1577bWcc845Sf7nHPvc5z5Xem6vvPJKXnjhhcyYMSMHHXTQWue8Lvun/mxu58zHWfVakqz8nWd95vTh15MPv7HM6wkUR9euXdOyZcvSp4skKz+V4JVXXlnjpxL06dMns2fPzhtvvFHatuq+vXr12vATZpNW1+MxSc4666w88MADufzyy/1PWD5RdT0eJ0+enHvuuSd33nln7rzzzlx00UVJkmuvvTbf+9736m3ebJrqejz27t07y5Ytq/U7+tKlS33CCp+Iuh6PHTt2zBtvvFHr40uXLFmSN9980/FIvbOeYUOyvqZorLEpEmtsisQamyKxxmZj9kmtacTOrLdevXqVrir7s5/9LIsWLUp1dXUuvvji0iXzhwwZkiRp2nTlRcRXfTz6smXLMmPGjCQrrya47bbb5v3338/dd9+dpPbHwn/U448/Xroa4N133126WvOqr+7duyf5n0DtkksuSY8ePXLUUUelTZs2Oeyww0ofDbF8+fK8//77uf/++9O7d+8MGTIk8+fPz+c+97mceuqp2WabbZKsvPJtp06dSlfjvfrqq1NVVZUFCxbkkEMOSe/evTNu3LgkteOhxYsXZ9myZeu0fzZ9DXXOrE1lZWVGjx6d1157LUlyxBFHJFn5ApMkL7/8culNA4899lj22muv7LfffrVeeJLkmWeeyeTJk5MkTz31VCl4W/XL1IfPiUWLFn3sVUxpeKti9kcffTRXXHFFKQZ+6KGHcv311ydZeSyXl5eXjudp06YlWXlM3XPPPev1uB+OGJs2bVp69/W4ceOycOHCVFZW5rjjjkuvXr3ys5/9rM77X3Ucrvq5nCQTJkxIkhx00EGrvZaMHTs2SfLXv/41f/rTn/L2229n4MCB2WuvvfLggw9m7733zsknn5w99tgjycqf4zU1NTn88MPTo0ePXHLJJenWrVuOPvrofOELX0iS0pXbV51jf/jDH/Liiy8mSW677bb07NkzX/jCF0pXsV31c2Dx4sWpqanJ8uXL12n/1K/N6ZxZm5dffjlXXHFFkpXH9y677LJec7riiivywQcfpLKyMtdee22SlYvwzp07l57nus4J2DDKysoybNiwXHbZZXn44Yczffr0nHHGGenQoUMOPPDALF++PPPmzcvSpUuTJN27d0+vXr1yxhln5MUXX8zTTz+dH//4xznkkENceYr/WF2PxzvuuCP33XdfzjjjjPTt2zfz5s0rfa0aA+urrsfjDjvsUOtr1c/E7bbbbrVPT4K6quvx2Lt37+y99945++yz8+yzz+bvf/97zjrrrDRp0iQHH3xwAz8bNnZ1PR4POeSQJMnpp5+e6dOnl8aXlZVl6NChDfhM2BxYz1CfrK8pGmtsisQamyKxxqZIrLHZmGyoNY3YmfVWVlaW8847L40aNcojjzySfv36pXfv3hk/fnwaNWqUH/zgB6Ur2W6//fZJVsYwe+21Vx577LFSlf/888+nX79+2WeffUrxz4IFC9b6uBMnTkyS7LrrrunSpUu23HLLWl9f+tKXkiR//OMf8+abb+ZLX/pSGjdunJdeeikDBgxI//79S1Fnjx49sssuu2TQoEHZdttts3Dhwhx00EEZMGBA9tlnn8ybNy+tW7fO/vvvn8aNG+f0009PsvLKzn369MnAgQPzxhtvpEmTJvnf//t/J1n5S3PjxitPrcMPPzwjRoxYp/2z6Wuoc+bDRo0alcGDB2fgwIHp3bt3rr766iTJkUcemf79+ydJ6fisqanJKaeckr59++bkk09OTU1Ndt9999Xe4dWxY8ecdtpp6dWrV44++uisWLEi//Vf/1U6F1c9lyQZNGhQLr300vX9K6QefO1rX8s3vvGNJMnYsWOz1157pVevXvnud7+bqqqq7LTTTjn33HOTrDxWkpWR+7777pt99903s2bN+kTm8b3vfS9NmzZNRUVF+vfvn/79+2fatGlZsWJF6diqi1XH4SWXXJJ99903f/3rX0uh8Re/+MXVXkuGDBmSNm3aJFn55pntttuuFCmPHDkyffv2Te/evfPoo4+mUaNGGTp0aBo1apRDDjkky5cvz29+85v06dMnffr0yeWXX54kOeyww5IkX/nKV7LLLrtk6dKl+drXvpa+fftm1KhRWbFiRfbdd99stdVWSVIKPH//+99nr732yl//+td12j/1a3M5Zz5s5syZGTx4cAYPHpx+/fpl6NCheeONN7L11lvnwgsvXK85NW/ePK+++mr69++fPn365IknnkiSnHbaaaUxq86JG2+8MX369Fnn1z7gkzV8+PAcdthhOe+883LEEUekSZMmuf7661NWVpZZs2Zl4MCBue+++5KsfGPGlVdemc6dO+fb3/52Tj/99AwePDgXXHBBwz4JNhl1OR5XvcHo5z//eQYOHFjra9UY+E/U5XiEDa2ux+MvfvGL9O3bN6eeemoOO+ywLFy4MDfddFNpXQz/ibocj+3atcutt96ampqafPvb384xxxyTZs2a5Te/+U1atWrVwM+ETZ31DPXN+pqiscamSKyxKRJrbIrEGpuNxYZa0zSqqamp2QDzZRPz5ptvloLc8ePHp0ePHqXbKioqct111+WFF15IVVVVdt1115xyyimlK9Qmyfz58zNy5Mg8++yzadmyZS666KLsvffeufTSS3Pfffdl8eLF2WWXXfLtb387Z599dpYtW5bbb7893bt3z1FHHZWpU6fm8MMPLx3o1dXVOeOMM3LyySevNtd58+ZlyJAhWb58eU4++eScccYZefnll/PLX/4yL774YhYsWJD27dtn//33z8knn1z6aPR58+Zl7Nix+cMf/pB58+alTZs26dGjR4YPH56dd965tP/7778/119/ff72t7+lvLw8vXr1yplnnplddtmlNOaaa67Jr371qyxZsiRf/OIX89Of/nSd98+moSjnzKhRo5IkXbp0qTW/Ro0aZcstt8xnPvOZfOMb3yi9o2uVRYsW5corr8wDDzyQefPmpV27dvnyl7+c7373uykrK0uS0uP88Ic/zOLFi3PzzTdn0aJFGTRoUH74wx/WeufNhRdemHvuuSfLli3L0UcfXSteo5h+//vf59Zbb83LL7+cZcuWpVOnTjnggANyzDHHlH5uLlmyJD/96U/zwAMPpHHjxvnc5z6Xgw8+OEceeWSSlK5GvqZjMvn48yRZGYSOHTs2L730Uho3bpzddtstp59+ein8r6ioyLe+9a0kyRNPPJG2bdsmSY477rg88cQT+epXv5pLLrkkSTJlypRcdNFFmTVrVnbccccMGDAgv/rVr7LlllvmqaeeSnl5+Wp/Bz/5yU9y6623pkWLFnn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|
|
"text/plain": [
|
|
"<Figure size 3600x1200 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import sys\n",
|
|
"sys.path.append('../group-1')\n",
|
|
"import pandas as pd\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import seaborn as sns\n",
|
|
"import matplotlib.font_manager as font_manager\n",
|
|
"\n",
|
|
"def compare_balance_sheets(ticker_list: list):\n",
|
|
" num_charts = len(ticker_list)\n",
|
|
"\n",
|
|
" # Create a single figure object and subplots\n",
|
|
" fig, axes = plt.subplots(1, num_charts, figsize=(12*num_charts, 12), sharey=True)\n",
|
|
"\n",
|
|
" # Set font properties\n",
|
|
" font = font_manager.FontProperties(weight='bold', size=12)\n",
|
|
"\n",
|
|
" for i, ticker in enumerate(ticker_list):\n",
|
|
" assets_debt = pd.read_csv(r'../Companies_Data/'+ticker+'_Data/'+ticker+'_balance_sheet_4Y+4Q.csv')\n",
|
|
" selected_data = assets_debt[['TotalAssets', 'TotalDebt', 'CurrentAssets', 'CurrentDebt']]\n",
|
|
"\n",
|
|
" sns.set(style=\"whitegrid\") # Set seaborn style\n",
|
|
"\n",
|
|
" # Create bar plot for Total Assets\n",
|
|
" ax = axes[i]\n",
|
|
" ax.bar(range(len(selected_data.columns)), selected_data.iloc[0], color='green')\n",
|
|
" ax.set_title(f'{ticker} - Total Assets', fontproperties=font, fontsize=14, weight='bold')\n",
|
|
" ax.set_xlabel(ticker, fontproperties=font)\n",
|
|
" ax.set_ylabel('Amount in $', fontproperties=font)\n",
|
|
" ax.set_xticks(range(len(selected_data.columns)))\n",
|
|
" ax.set_xticklabels(selected_data.columns, fontproperties=font)\n",
|
|
"\n",
|
|
" # Remove background grid\n",
|
|
" ax.grid(False)\n",
|
|
"\n",
|
|
" # Remove scientific notation\n",
|
|
" ax.ticklabel_format(style='plain')\n",
|
|
"\n",
|
|
" plt.tight_layout() # Adjust subplot spacing\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"ticker_list = ['META', 'AAPL', 'KO']\n",
|
|
"compare_balance_sheets(ticker_list)\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".env",
|
|
"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": "3.11.0"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|