diff --git a/indexer/balance_sheet.ipynb b/indexer/balance_sheet.ipynb index 3897e07..ee9bfb1 100644 --- a/indexer/balance_sheet.ipynb +++ b/indexer/balance_sheet.ipynb @@ -1,32 +1,26 @@ { "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Claudio, questo notebook genera la chart relativa alla salute finanziaria. Essenzialmente ci sono quattro colonne che sono stackate a coppie di due: Total Assets/Current Assets e Total Debt/Current Debt. Sostanzialmente asset e debiti a breve termine e lungo termine (quelli a lungo termine saranno sempre superiori in quanto incorporano quelli a breve termine in entrambi i casi). Come vedi il colore degli asset e debiti a breve è molto più marcato perché questi sono nettamente più rilevanti. Se hai qualche idea su come mostrarlo diversamente sono bene accette, io penso che questa soluzione faccia il suo, penso che le stacked bar siano molto efficaci in questo contesto. \n", + "

\n", + "\n", + "

*Nota: ho scelto proprio APPL e MSFT per mostrare che secondo me anche questo garfico ha bisogno di una versione \"relativa\". Quello che vedi sotto compara i valori assoluti, però anche averne uno in cui le altezze delle colonne sono determinate rispetto alle dimensioni dell'azienda non sarebbe male. Domani vediamo comunque.

" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 42, "metadata": {}, "outputs": [ - { - "ename": "AttributeError", - "evalue": "This method only works with the ScalarFormatter", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "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", - "\u001b[0;31mAttributeError\u001b[0m: 'FuncFormatter' object has no attribute 'set_scientific'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "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", - "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", - "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", - "\u001b[0;31mAttributeError\u001b[0m: This method only works with the ScalarFormatter" - ] - }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -44,38 +38,53 @@ "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", + " \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", + " sns.set(style=\"whitegrid\") \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", + " asset_labels = ['Total Assets & Current Assets', ' Total Debt & Current Debt']\n", + " asset_values = [selected_data.iloc[0]['TotalAssets'], selected_data.iloc[0]['CurrentAssets']]\n", + " current_asset_values = [0, selected_data.iloc[0]['CurrentAssets']]\n", + " ax.bar(range(len(asset_labels)), asset_values, color=['green', 'red'], label='Assets', alpha=0.5)\n", + " ax.bar(range(len(asset_labels)), current_asset_values, color=['white', 'red'], alpha=0.5)\n", + "\n", + " \n", + " debt_labels = ['Total Debt/ Current Debt', '']\n", + " debt_values = [selected_data.iloc[0]['TotalDebt'], selected_data.iloc[0]['CurrentDebt']]\n", + " current_debt_values = [0, selected_data.iloc[0]['CurrentDebt']]\n", + " ax.bar(range(len(debt_labels)), debt_values, color=['darkgreen', 'darkred'], label='Debts')\n", + " ax.bar(range(len(debt_labels)), current_debt_values, color=['white', 'darkred'])\n", + "\n", + " ax.set_title(f'{ticker} - Balance Sheet', 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", + " ax.set_xticks(range(len(asset_labels))) \n", + " ax.set_xticklabels(asset_labels, fontproperties=font)\n", "\n", - " # Remove background grid\n", - " ax.grid(False)\n", "\n", - " # Remove scientific notation\n", - " ax.ticklabel_format(style='plain')\n", + " ax.xaxis.grid(False)\n", + " ax.yaxis.grid(False)\n", "\n", - " plt.tight_layout() # Adjust subplot spacing\n", + " \n", + " green_patch = plt.Line2D([0], [0], color='green', alpha=0.5, linewidth=0, marker='s')\n", + " red_patch = plt.Line2D([0], [0], color='red', alpha=0.5, linewidth=0, marker='s')\n", + " ax.legend([green_patch, red_patch], ['Assets', 'Debts'], loc='best')\n", + "\n", + "\n", + " plt.tight_layout() \n", " plt.show()\n", "\n", - "ticker_list = ['META', 'AAPL', 'KO']\n", + "ticker_list = ['AAPL', 'MSFT']\n", "compare_balance_sheets(ticker_list)\n", + "\n", "\n" ] }