Data_Exploration Summary.ipynb

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    "\"\"\" What is Pandas?\n",
    "Pandas is a very flexible data science library that allows us to load in data, manipulate it, and analyze it. \n",
    "- Pandas uses dataframes which we can think of as tables. We can use functions to create or manipulate rows and columns. \n",
    "- Using pandas dataframes, we can begin to understand trends in data we can perform functions on the rows or columns to understand more about our set\n",
    "- Pandas also has some visualization tools that we can use to better understand our data\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd \n",
    "\n",
    "# read in data from workign directory (folder in top right)\n",
    "# can read in from anywhere if full path is the pd.read_csv()\n",
    "data = pd.read_csv('craigslistVehicles.csv')\n",
    "\n",
    "#rows and columns returns (rows, columns)\n",
    "data.shape\n",
    "\n",
    "#returns the first x number of rows when head(num). Without a number it returns 5\n",
    "data.head()\n",
    "\n",
    "#returns the last x number of rows when tail(num). Without a number it returns 5\n",
    "data.tail()\n",
    "\n",
    "#returns an object with all of the column headers \n",
    "data.columns\n",
    "\n",
    "#basic information on all columns \n",
    "data.info()\n",
    "\n",
    "#gives basic statistics on numeric columns\n",
    "data.describe()\n",
    "\n",
    "#shows what type the data was read in as (float, int, string, bool, etc.)\n",
    "data.dtypes\n",
    "\n",
    "#shows which values are null\n",
    "data.isnull()\n",
    "\n",
    "#shows which columns have null values\n",
    "data.isnull().any()\n",
    "\n",
    "#shows for each column the percentage of null values \n",
    "data.isnull().sum() / data.shape[0]\n",
    "\n",
    "# for categorical variables \n",
    "\n",
    "#shows unique values that appear in the column \n",
    "#data.type = data['type']\n",
    "data.type.unique()\n",
    "\n",
    "#shows the counts for those unique values \n",
    "data.type.value_counts()\n",
    "\n",
    "#shows the percentage of values from \n",
    "data.type.value_counts()/ data.type.notnull().sum()\n",
    "\n",
    "data.cylinders.head(10)\n",
    "\n",
    "############################################################# Graphing #######################################################\n",
    "#histogram of year \n",
    "# data.year.hist() == data.year.plot(kind='hist')\n",
    "data.year.hist()\n",
    "data.year.hist(bins=100)\n",
    "\n",
    "#bar chart of types \n",
    "data.type.value_counts().plot(kind='bar')"
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