Add parquet vs csv

book/pandas/Chapter.ipynb

@@ -189,6 +189,54 @@
     "data = {'Value': [1.2343129, 5.8956701, 6.224289]}\n",
     "df = pd.DataFrame(data)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Faster I/O with Parquet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever you work with bigger datasets, please avoid using CSV format (or similar).\n",
+    "\n",
+    "CSV files are text files, which are human-readable, and therefore a popular option to store data.\n",
+    "\n",
+    "For small datasets, this is not a big issue.\n",
+    "\n",
+    "But, what if your data has millions of rows?\n",
+    "\n",
+    "It can get really slow to do read/write operations on them.\n",
+    "\n",
+    "On the other side, binary files exist too.\n",
+    "\n",
+    "They consist of 0s and 1s and are not meant to be human-readable but to be used by programs that know how to interpret them.\n",
+    "\n",
+    "Because of that, binary files are more compact and consume less space.\n",
+    "\n",
+    "Parquet is one popular binary file format, which is more memory-efficient than CSVs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Shape: (100000000, 5)\n",
+    "df = pd.DataFrame(...)\n",
+    "\n",
+    "# Time: 1m 58s\n",
+    "df.to_csv(\"data.csv\")\n",
+    "\n",
+    "# Time: 8s\n",
+    "df.to_parquet(\"data.parquet\")"
+   ]
   }
  ],
  "metadata": {