STD_Part1.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt  "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "ds = pd.read_csv('gas_prices.csv') #read in data set from a csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1 Display a descriptive table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "#get descriptive about data set\n",
    "ds.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3.1 Display a descriptive table only with information for counts, mean, and standard deviation for the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "ds.describe().loc[['','','']] #insert names between quotation marks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 4.1 Add the correct quartiles (Q1,Median,Q3) labels to 25%, 50%, 75% listed in the table\n"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "print(\"\",ds.describe().loc[['25%']] ) #insert the quartile that represents 25% between the red quotation \"\" marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "print(\"\",ds.describe().loc[['50%']] ) #insert the quartile that represets 50% between the red quotation \"\" marks\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "print(\"\",ds.describe().loc[['75%']] ) #insert the quartile that represents 75% between the red quotation \"\" marks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 5.1 Plot a box-plot of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\") \n",
    "ax = sns.boxplot(x=ds[\"\"]) # insert column name between quotation marks, run code to plot box-plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 6.1 Plot a histogram of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds.hist(column=\"\",        # insert the column name to plot between red quotation \"\" marks\n",
    "              figsize=(8,8),         # Plot size\n",
    "              color=\"\",          # insert color name between red quotation \"\" marks\n",
    "              bins=4 )           # number of bins\n",
    "plt.show() #run code to plot histogram"
   ]
  }
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