{
 "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 the following NumPy array `data`, create a pandas DataFrame named `first_data_frame` that contains the same elements. Print the DataFrame to make sure the operation executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.round(np.random.randn(5,5),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 4\n",
    "Assign the values of `row_labels` to the index of `first_data_frame`. Print the DataFrame to make sure the operation executed successfully.\n",
    "\n",
    "Hint: It will be easier to overwrite `first_data_frame` by using another `pd.DataFrame` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_labels = ['one','two','three','four','five']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 5\n",
    "Assign the values of `column_labels` to the columns of `first_data_frame`. Note that there are two main ways to do this - you are free to chose the method of your choice. Print the DataFrame to make sure the operation executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_labels = ['alpha','beta','charlie','delta','echo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 6\n",
    "Create a pandas Series named `my_series` that contains the values from row `alpha` of `first_data_frame`. Print `my_series` to make sure the operation executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 7\n",
    "Create a new DataFrame called `second_data_frame` that is equal to `first_data_frame` but without row `one`. Print `second_data_frame` to make sure the operation executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 8\n",
    "Create a new DataFrame called `third_data_frame` that is equal to `second_data_frame`, but without row `charlie`. Print `third_data_frame` to make sure the operation executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 9\n",
    "Create a variable called `row_two` that is equal to row `two` from `third_data_frame`. Print `row_two` to make sure the operation executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 10\n",
    "Print the shape of new_data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = np.round(np.random.randn(5,5),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 11\n",
    "Print a DataFrame that contains boolean values that indicate whether the elements of `new_data` are greater than 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 12\n",
    "Print a NumPy array that contains only the elements of `new_data` that are greater than 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Solution goes here"
   ]
  }
 ],
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