PANDAS1 DONE

big_countries.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3df2908",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Assume 'world' DataFrame is already defined\n",
+    "big_countries = world[(world['area'] >= 300000) | (world['population'] >= 25000000)][['name', 'population', 'area']]\n",
+    "big_countries\n"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

customersneverorder.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f81924e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Assume 'Customers' and 'Orders' DataFrames are already defined\n",
+    "result = Customers[~Customers['id'].isin(Orders['customerId'])][['name']]\n",
+    "result\n"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

dataframe.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baede9fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas as pd \n",
+    "import pandas as pd  \n",
+    "\n",
+    "# List1  \n",
+    "lst = [['Geek', 25], ['is', 30], \n",
+    "       ['for', 26], ['Geeksforgeeks', 22]] \n",
+    "\n",
+    "# creating df object with columns specified    \n",
+    "df = pd.DataFrame(lst, columns =['Tag', 'number']) \n",
+    "print(df )"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

recylable lowfat.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a96a26c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Assume 'products' DataFrame is already defined\n",
+    "result = products[(products['low_fats'] == 'Y') & (products['recyclable'] == 'Y')]\n",
+    "result\n"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}