{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1\n",
    "Import NumPy under the alias `np`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 2\n",
    "Import pandas under the alias `pd`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 3\n",
    "Given `df1` and `df2` below, concatenate the two DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "               'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "               'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "               'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "              index=[0, 1, 2, 3])\n",
    "\n",
    "\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                  'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                  'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                  'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                   index=[4, 5, 6, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 4\n",
    "Given `df1` and `df2` below, concatenate the two DataFrames along their columns (instead of along their rows)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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