python_processing_questions.ipynb

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    "# Data Engineering \r\n",
    "© Explore Data Science Academy\r\n",
    "## Moving Big Data Predict: Python Processing MCQ\r\n",
    "\r\n",
    "Use the CSV output file generated by your pipeline to answer the below questions"
   ],
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   "source": [
    "import numpy as np\r\n",
    "import pandas as pd"
   ],
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   "source": [
    "#### 1. What is the most common data type of the columns in your processed data?"
   ],
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   "source": [
    "#### 2. How many rows does the output dataframe have?"
   ],
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  },
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   "outputs": [],
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   "source": [
    "#### 3. What is the sum of the value_change column?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
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   "source": [
    "#### 4. What is the mean of the value_change column?"
   ],
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   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
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   "cell_type": "markdown",
   "source": [
    "#### 5. What is the mean open_value? "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 6. What is the mean close_value?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 7. What are the minimum and maximum values in the stock_date column?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 8. How many distinct values are in the company_name column?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 9.  What type of file format is more efficient in terms of storage space?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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