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   "source": [
    "## Exercise 2 - Pandas part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Throughout the exercise, the original data frame is to be processed!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 Read the content of the `data_export.csv` file saved at the end of the previous exercise into the dataframe so that the `Unnamed 0` column is not created when the file is read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 Drop the rows with `NaN` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
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   "source": [
    "3 Change all values ​​in the fifth and tenth row to `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4 Print the number of `NaN` values ​​in each column from the DataFrame object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 Fill in the `NaN` values ​​made above with linear interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 Change the last value of `column1` to 94 and the last value of `column3` to 45."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7 Add a new column called `column4` to the DataFrame object. This column should include the sum of the columns `column1` and `column3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8 Reset the index of the DataFrame object and print the number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9 Make time series data with the length of a DataFrame object in the time interval you want and make it an index column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 Convert all values ​​in `column1` to strings with each zero replaced by an uppercase `ND` character combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11 Make a copy of the DataFrame object, whose column names have been changed to the following: `column5`, `column6`, `column7`, `column8`. After that, make one big DataFrame object, which consists of the original DataFrame and a copy so that they are added next to each other.\n",
    "\n",
    "\n",
    "| column1 | column2 | column3 | column4 | column5 | column6 | column7 | column8 |\n",
    "| ------------- |:-------------:| -----:| ------------- |:-------------:| -----:|-----:|-----:|\n",
    "| 0.1 | 0.2 | 0.3 | ... | ... | ... | ... | ... |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12 Put the last value of `column5` into a variable and the last value of `column3` into a separate variable from the newly made DataFrame object in string format. Concatenate those variables as a string and remove the last two characters from the result. Finally, remove all dots `.`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  }
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