{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to `geopandas`\n",
    "---\n",
    "Today we are going to learn how to use python and Jupyter notebooks to learn the basics of working with geospatial data in `geopandas`. `geopandas` is built on top of the `pandas` package you saw in the previous lab. Many of the methods you will see in this lab are shared between the two packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# RUN THIS CELL FIRST or the notebook won't work\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# These help the maps display nicely in the notebook\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [30, 20]\n",
    "\n",
    "# This line tells iPython to not display warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load our data and see what kind of data we are working with.  The `read_file` method requires that we pass the _filepath_ from our current directory (the location of this notebook) to the data. The `pwd()` function shows you the your current location in the file system. Another way you can say this is that it __p__rints your __w__orking __d__irectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/chrispyles/GitHub/modules-textbook/content/cp-101/lab09'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get to the data, we would first enter the `shapefiles` folder from the working directory, then the `alameda` folder. The way we communicate this to the function is by passing `'shapefiles/alameda/'` as an argument. This folder contains demographic information organized by census tract in Alameda County."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alameda = gpd.read_file('shapefiles/alameda/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what our data looks like in Jupyter. We can use the `.head()` method to show the first 5 rows of our data. Similarly, you can use the `.tail()` method to show the last 5 rows of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <td>47.9</td>\n",
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       "      <td>746.0</td>\n",
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       "               AFFGEOID     ALAND   ALAND_1  AWATER  AWATER_1 COUNTYFP  \\\n",
       "0  1400000US06001400600  297856.0  297856.0     0.0       0.0      001   \n",
       "1  1400000US06001400900  420877.0  420877.0     0.0       0.0      001   \n",
       "2  1400000US06001401400  758241.0  758241.0     0.0       0.0      001   \n",
       "3  1400000US06001403000  352394.0  352394.0     0.0       0.0      001   \n",
       "4  1400000US06001405902  487280.0  487280.0     0.0       0.0      001   \n",
       "\n",
       "  COUNTYFP_1  FID_1 FUNCSTAT        GEOID  ...  female    male medianage  \\\n",
       "0        001  275.0        S  06001400600  ...  1020.0   730.0      34.4   \n",
       "1        001  297.0        S  06001400900  ...  1106.0   925.0      38.9   \n",
       "2        001  239.0        S  06001401400  ...  2170.0  2127.0      31.3   \n",
       "3        001  258.0        S  06001403000  ...  1445.0  1026.0      58.3   \n",
       "4        001  746.0        S  06001405902  ...  1713.0  1808.0      31.5   \n",
       "\n",
       "       mergeid pct_20to64  pct_over64 pct_pov pct_und_20 total_pop  \\\n",
       "0  06001400600       78.2         9.8     8.3       12.0    1750.0   \n",
       "1  06001400900       71.0         7.7    12.0       21.0    2031.0   \n",
       "2  06001401400       64.7         8.2    41.7       27.1    4297.0   \n",
       "3  06001403000       47.9        43.8    30.2        8.3    2471.0   \n",
       "4  06001405902       58.6        10.0    31.0       31.5    3521.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-122.26807 37.844136, -122.26514 37....  \n",
       "1  POLYGON ((-122.285576 37.839778, -122.283186 3...  \n",
       "2  POLYGON ((-122.278611 37.826878, -122.268563 3...  \n",
       "3  POLYGON ((-122.274757 37.79883299999999, -122....  \n",
       "4  POLYGON ((-122.247175 37.789913, -122.243512 3...  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first few columns of the table contain information relating to the itentity of each census tract, but we can't interpret these without looking at the metadata for this dataset. If you scroll the table all the way to the right, you can see some columns with names that are more easily interpretable. However, because the table we are reading in has more columns than the page can accomodate, the display omits the middle columns to make the table narrower. To see a list of all of the columns in our `GeoDataFrame`, we access use the `.column` attribute like so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AFFGEOID', 'ALAND', 'ALAND_1', 'AWATER', 'AWATER_1', 'COUNTYFP',\n",
       "       'COUNTYFP_1', 'FID_1', 'FUNCSTAT', 'GEOID', 'GEOID_1', 'Geography',\n",
       "       'INTPTLAT', 'INTPTLON', 'Id', 'Id2', 'LSAD', 'MTFCC', 'NAME',\n",
       "       'NAMELSAD', 'NAME_1', 'STATEFP', 'STATEFP_1', 'TRACTCE', 'TRACTCE_1',\n",
       "       'female', 'male', 'medianage', 'mergeid', 'pct_20to64', 'pct_over64',\n",
       "       'pct_pov', 'pct_und_20', 'total_pop', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examining the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.loc[]` and `.iloc[]` methods allow us to view cells in a `DataFrame` or `GeoDataFrame` based on their name or location. The __i__ in `.iloc[]` stands for the __integer__ position of a cell, and accesses cells by location in index coordinates. The `.loc[]` method allows you to access cells by the index of the rows and the names of the columns. For both `.loc[]` and `.iloc[]`, the first argument refers to the row, and the second argument refers to the column.\n",
    "\n",
    "Typically the row index will be the same as its integer position, but that is not always the case. Let's set up a `DataFrame` to see this in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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      "text/plain": [
       "     a  b  c   d\n",
       "1.1  1  4  7  10\n",
       "1.2  2  5  8  11\n",
       "1.3  3  6  9  12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df = pd.DataFrame(data = {'a': [1, 2, 3],\n",
    "                         'b': [4, 5, 6],\n",
    "                         'c': [7, 8, 9],\n",
    "                         'd': [10, 11, 12]},\n",
    "                 index = [1.1, 1.2, 1.3])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to access the number 8 from `df`, we would need to tell `.iloc[]` to look in `df` at row `1`, column `2` (remember that python starts counting from zero!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `.loc[]`, we would need to tell the method that we want row index `1.2`, column name `'c'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1.2, 'c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also specify a range of indices to both of these methods if you want to access multiple adjacent cells. For `iloc[]`, this range will always refer to integer coordinates of the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "     b  c   d\n",
       "1.1  4  7  10\n",
       "1.3  6  9  12"
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     "metadata": {},
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   "source": [
    "# This code tells .iloc that we want rows 0 and 2, then all columns with index\n",
    "# greater than or equal to 1.\n",
    "df.iloc[[0, 2], 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also refer to a range named columns and rows using `.loc[]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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      "text/plain": [
       "     b  c   d\n",
       "1.2  5  8  11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This code tells .loc that we want row 1.2, columns 'b' through 'd' inclusive.\n",
    "df.loc[[1.2], 'b':'d']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `geometry` column contains a new data type called a polygon, which is how `geopandas` is able to store geographic information. Let's look at one of these polygons using `.loc[]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-122.2683718 37.8371802 0.007377599999998097 0.008148600000005501\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,75.68250900000001)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.00016297200000011002\" opacity=\"0.6\" d=\"M -122.26807,37.844136 L -122.26514,37.84453 L -122.261296,37.845027 L -122.261805,37.841789999999996 L -122.26186,37.841353 L -122.264164,37.839997 L -122.265631,37.837635999999996 L -122.26665299999999,37.837482 L -122.267257,37.840151999999996 L -122.267562,37.841671999999996 L -122.26807,37.844136 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x124d626a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda.loc[0, 'geometry']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view all of the polygons in the `GeoDataFrame`, we can just use the `.plot()` method. The documentation for this function can be found [here](http://geopandas.org/mapping.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124dea7f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alameda.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can improve the appearance of this graph by removing the axis labels surrounding the map and adding a title. You may also see a line that says something like `<matplotlib.axes._subplots.AxesSubplot at 0x1120b5710>` above the map. You can prevent Jupyter from displaying this line by adding a `;` to the last line of code creating the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alameda.plot()\n",
    "\n",
    "# Adding the fontsize argument allows you to manipulate the font size.\n",
    "plt.title('Alameda County Census Tracts', fontsize = 30)\n",
    "\n",
    "# This turns the plot axes off.\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choropleth maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to make this map more informative, we can incorporate some of the other columns from the data into the map. For example, the `'pct_pov'` column in the table contains the percentage of people living in poverty in each census tract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       8.3\n",
       "1      12.0\n",
       "2      41.7\n",
       "3      30.2\n",
       "4      31.0\n",
       "5      20.3\n",
       "6      34.2\n",
       "7       2.8\n",
       "8      43.9\n",
       "9      24.7\n",
       "10      4.3\n",
       "11      8.5\n",
       "12     11.2\n",
       "13     28.6\n",
       "14     10.1\n",
       "15     16.1\n",
       "16      2.4\n",
       "17      3.4\n",
       "18      6.5\n",
       "19      2.7\n",
       "20      2.8\n",
       "21      3.7\n",
       "22      6.6\n",
       "23      1.8\n",
       "24     34.9\n",
       "25     25.3\n",
       "26     26.5\n",
       "27     10.3\n",
       "28     10.8\n",
       "29     11.4\n",
       "       ... \n",
       "331     4.1\n",
       "332    42.6\n",
       "333    22.4\n",
       "334     3.7\n",
       "335     6.4\n",
       "336    11.3\n",
       "337    16.2\n",
       "338     1.6\n",
       "339     8.9\n",
       "340    14.5\n",
       "341     6.5\n",
       "342     1.6\n",
       "343     4.9\n",
       "344    34.4\n",
       "345    11.1\n",
       "346     4.5\n",
       "347     5.5\n",
       "348    17.7\n",
       "349     5.5\n",
       "350    31.8\n",
       "351    41.3\n",
       "352    27.0\n",
       "353     5.5\n",
       "354    37.2\n",
       "355    12.6\n",
       "356     4.2\n",
       "357    17.3\n",
       "358     8.1\n",
       "359     5.0\n",
       "360     2.6\n",
       "Name: pct_pov, Length: 361, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda['pct_pov']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass the column name as an optional argument to the `.plot()` method to create a map with a color gradient based on the values in this column. Setting `legend` equal to `True` in the function call will also tell the method that we want our final plot to have a legend so that we can interpret the colors on the map. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alameda.plot(column = 'pct_pov', legend = True)\n",
    "#Bounds supplies information on the maximum and minimum values of the x-axis and y-axis of our plot.\n",
    "bounds = alameda.bounds.iloc[0]\n",
    "''' \n",
    "        minx   -122.268070\n",
    "        miny     37.837482\n",
    "        maxx   -122.261296\n",
    "        maxy     37.845027\n",
    "        Name: 0, dtype: float64 \n",
    "'''\n",
    "minx = bounds[\"minx\"]\n",
    "miny = bounds[\"miny\"]\n",
    "# We use that information to place the text label for our legend\n",
    "plt.text(minx + 1, miny - 0.3, 'Percentage of People Living in Poverty', fontsize=20, rotation=90, rotation_mode='anchor')\n",
    "plt.title('Alameda County Poverty Rates by Census Tract', fontsize = 30)\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to change the color scheme of the map, you can choose the \"color map\" of the plot by adding in the optional `cmap` argument to `plot()`. You can choose from the variety of color maps available in the `matplotlib` package, which is what our plotting software is built off of. Above, we saw the default colormap, which is called viridis. If you want to view all the available colormaps, you can reference the documentation [here](https://matplotlib.org/users/colormaps.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alameda.plot(column = 'pct_pov', legend = True, cmap = 'magma')\n",
    "bounds = alameda.bounds.iloc[0]\n",
    "minx = bounds[\"minx\"]\n",
    "miny = bounds[\"miny\"]\n",
    "plt.text(minx + 1, miny - 0.3, 'Percentage of People Living in Poverty', fontsize=20, rotation=90, rotation_mode='anchor')\n",
    "plt.title('Alameda County Poverty Rates by Census Tract', fontsize = 30)\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's your turn! In the following cell, try creating your own choropleth map using a different column from `alameda`. If you want to see all of the columns in our data, you can scroll up to where we displayed all of the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are some conclusions that you can draw from the map you created?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new shapefile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's isolate the census tracts belonging to Berkeley. Below are the identification numbers for Berkeley's census tracts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "berkeley_census_tracts = [4211, 4212, 4213, 4214, 4215, 4216, 4217, 4218, 4219, 4220, 4221, 4222, 4223, 4224, 4225,\n",
    "                          4226, 4227, 4228, 4229, 4230, 4231, 4232, 4233, 4234, 4235, 4236, 4236.01, 4236.02,\n",
    "                          4237, 4238, 4239.01, 4239.02, 4240.01, 4240.02]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data in our `GeoDataFrame` are all strings, so we need to convert these numbers into strings before python can compare them to our data. We are going to use a technique called a list comprehension to do this. A list comprehension is basically a for loop condensed into a single line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "berkeley_census_tracts = [str(id) for id in berkeley_census_tracts]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we were going to write this list comprehension as a for loop, we would have to create a new list to save our converted data into. It would look a little something like this:\n",
    "```\n",
    "berkeley_census_tracts_str = []\n",
    "for id in berkley_census_tracts:\n",
    "    berkeley_census_tracts_str.append(str(id))\n",
    "```\n",
    "List comprehensions are so much more efficient!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are going to use a for loop to look at all of the rows in `alameda` and add the rows belonging to Berkeley census tracts to a new `GeoDataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>ALAND_1</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>AWATER_1</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYFP_1</th>\n",
       "      <th>FID_1</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>...</th>\n",
       "      <th>female</th>\n",
       "      <th>geometry</th>\n",
       "      <th>male</th>\n",
       "      <th>medianage</th>\n",
       "      <th>mergeid</th>\n",
       "      <th>pct_20to64</th>\n",
       "      <th>pct_over64</th>\n",
       "      <th>pct_pov</th>\n",
       "      <th>pct_und_20</th>\n",
       "      <th>total_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1400000US06001421400</td>\n",
       "      <td>474780.0</td>\n",
       "      <td>474780.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>686.0</td>\n",
       "      <td>S</td>\n",
       "      <td>06001421400</td>\n",
       "      <td>...</td>\n",
       "      <td>974.0</td>\n",
       "      <td>POLYGON ((-122.276027 37.88887, -122.272507 37...</td>\n",
       "      <td>747.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>06001421400</td>\n",
       "      <td>53.7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1400000US06001422900</td>\n",
       "      <td>544282.0</td>\n",
       "      <td>544282.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>157.0</td>\n",
       "      <td>S</td>\n",
       "      <td>06001422900</td>\n",
       "      <td>...</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>POLYGON ((-122.273067 37.871512, -122.268403 3...</td>\n",
       "      <td>2177.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>06001422900</td>\n",
       "      <td>72.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>43.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>4437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1400000US06001424001</td>\n",
       "      <td>507529.0</td>\n",
       "      <td>507529.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>131.0</td>\n",
       "      <td>S</td>\n",
       "      <td>06001424001</td>\n",
       "      <td>...</td>\n",
       "      <td>2045.0</td>\n",
       "      <td>POLYGON ((-122.279121 37.853287, -122.271161 3...</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>06001424001</td>\n",
       "      <td>72.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>24.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>3999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1400000US06001421500</td>\n",
       "      <td>1590680.0</td>\n",
       "      <td>1590680.0</td>\n",
       "      <td>3635.0</td>\n",
       "      <td>3635.0</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>314.0</td>\n",
       "      <td>S</td>\n",
       "      <td>06001421500</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>POLYGON ((-122.267737 37.893131, -122.262683 3...</td>\n",
       "      <td>1809.0</td>\n",
       "      <td>49.9</td>\n",
       "      <td>06001421500</td>\n",
       "      <td>56.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>19.2</td>\n",
       "      <td>3793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1400000US06001422100</td>\n",
       "      <td>551943.0</td>\n",
       "      <td>551943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>488.0</td>\n",
       "      <td>S</td>\n",
       "      <td>06001422100</td>\n",
       "      <td>...</td>\n",
       "      <td>1183.0</td>\n",
       "      <td>POLYGON ((-122.300853 37.877571, -122.295353 3...</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>39.7</td>\n",
       "      <td>06001422100</td>\n",
       "      <td>71.7</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>2284.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AFFGEOID      ALAND    ALAND_1  AWATER  AWATER_1 COUNTYFP  \\\n",
       "7   1400000US06001421400   474780.0   474780.0     0.0       0.0      001   \n",
       "8   1400000US06001422900   544282.0   544282.0     0.0       0.0      001   \n",
       "9   1400000US06001424001   507529.0   507529.0     0.0       0.0      001   \n",
       "36  1400000US06001421500  1590680.0  1590680.0  3635.0    3635.0      001   \n",
       "37  1400000US06001422100   551943.0   551943.0     0.0       0.0      001   \n",
       "\n",
       "   COUNTYFP_1  FID_1 FUNCSTAT        GEOID  ...  female  \\\n",
       "7         001  686.0        S  06001421400  ...   974.0   \n",
       "8         001  157.0        S  06001422900  ...  2260.0   \n",
       "9         001  131.0        S  06001424001  ...  2045.0   \n",
       "36        001  314.0        S  06001421500  ...  1984.0   \n",
       "37        001  488.0        S  06001422100  ...  1183.0   \n",
       "\n",
       "                                             geometry    male medianage  \\\n",
       "7   POLYGON ((-122.276027 37.88887, -122.272507 37...   747.0      51.3   \n",
       "8   POLYGON ((-122.273067 37.871512, -122.268403 3...  2177.0      23.1   \n",
       "9   POLYGON ((-122.279121 37.853287, -122.271161 3...  1954.0      30.4   \n",
       "36  POLYGON ((-122.267737 37.893131, -122.262683 3...  1809.0      49.9   \n",
       "37  POLYGON ((-122.300853 37.877571, -122.295353 3...  1101.0      39.7   \n",
       "\n",
       "        mergeid  pct_20to64 pct_over64 pct_pov pct_und_20 total_pop  \n",
       "7   06001421400        53.7       26.3     2.8       20.2    1721.0  \n",
       "8   06001422900        72.5        2.5    43.9       25.1    4437.0  \n",
       "9   06001424001        72.2        6.8    24.7       21.1    3999.0  \n",
       "36  06001421500        56.0       24.9     4.5       19.2    3793.0  \n",
       "37  06001422100        71.7       11.9     7.7       16.7    2284.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new GeoDataFrame\n",
    "berkeley = gpd.GeoDataFrame()\n",
    "for row in range(len(alameda)):\n",
    "    # If the NAME of a census tract is in our list of Berkeley census tracts, we want to\n",
    "    # add it to our berkeley GeoDataFrame.\n",
    "    if alameda.loc[row, 'NAME'] in berkeley_census_tracts:\n",
    "        berkeley = berkeley.append(alameda.loc[row, :])\n",
    "berkeley.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can save our new `GeoDataFrame`. The following code saves `berkeley` as a file type called a \"shapefile,\" which happens to be the same file type as the data we read in earlier. `geopandas` can also read and write many other geospatial file types, but we are just going to be using the same file type as before for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "berkeley.to_file(driver='ESRI Shapefile', filename = 'shapefiles/berkeley')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make a map with our new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "berkeley.plot(column = 'medianage', legend = True)\n",
    "bounds = berkeley.bounds.iloc[0]\n",
    "minx = bounds[\"minx\"]\n",
    "miny = bounds[\"miny\"]\n",
    "plt.text(minx + 0.0625, miny - 0.015, 'Median Age in Years', fontsize=20, rotation=90, rotation_mode='anchor')\n",
    "plt.title('Berkeley Median Age by Census Tract', fontsize = 30)\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you tell which census tracts are around the UC Berkeley campus from this map? Explain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining multiple `GeoDataFrame`s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with multiple sets of geospatial data, we may need to plot multiple `GeoDataFrames` together on the same map. There are a few different ways we can do this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If one of our `GeoDataFrames` covers a smaller area than the other, we can use the `GeoDataFrame` with the larger area as a base on top of which we plot the smaller `GeoDataFrame`. To do this, we use the optional `ax` argument when plotting the smaller `GeoDataFrame` to tell the plotting sofware that we want to continue using a previously plotted map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also define explicitly the colors of the lines and open spaces on our map.\n",
    "base = alameda.plot(color = 'white', edgecolor = 'black')\n",
    "berkeley.plot(ax = base, color = 'blue', edgecolor = 'red')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may also have separate `GeoDataFrames` that do not overlap, and we may want to combine them into a single table. Let's load in some more data so we can see this in action."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to read in our data. Let's use the `read_file` method from before to load in shapefiles in the `contra_costa` and `san_francisco` folders inside the `shapefiles` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "contra_costa = gpd.read_file('shapefiles/contra_costa')\n",
    "san_francisco = gpd.read_file('shapefiles/san_francisco')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have loaded the data in, let's take a look at the columns it contains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AFFGEOID', 'ALAND', 'ALAND_1', 'AWATER', 'AWATER_1', 'COUNTYFP',\n",
       "       'COUNTYFP_1', 'FID_1', 'FUNCSTAT', 'GEOID', 'GEOID_1', 'Geography',\n",
       "       'INTPTLAT', 'INTPTLON', 'Id', 'Id2', 'LSAD', 'MTFCC', 'NAME',\n",
       "       'NAMELSAD', 'NAME_1', 'STATEFP', 'STATEFP_1', 'TRACTCE', 'TRACTCE_1',\n",
       "       'female', 'male', 'medianage', 'mergeid', 'pct_20to64', 'pct_over64',\n",
       "       'pct_pov', 'pct_und_20', 'total_pop', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contra_costa.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AFFGEOID', 'ALAND', 'ALAND_1', 'AWATER', 'AWATER_1', 'COUNTYFP',\n",
       "       'COUNTYFP_1', 'FID_1', 'FUNCSTAT', 'GEOID', 'GEOID_1', 'Geography',\n",
       "       'INTPTLAT', 'INTPTLON', 'Id', 'Id2', 'LSAD', 'MTFCC', 'NAME',\n",
       "       'NAMELSAD', 'NAME_1', 'STATEFP', 'STATEFP_1', 'TRACTCE', 'TRACTCE_1',\n",
       "       'female', 'male', 'medianage', 'mergeid', 'pct_20to64', 'pct_over64',\n",
       "       'pct_pov', 'pct_und_20', 'total_pop', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "san_francisco.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `contra_costa` and `san_francisco` both have the same columns as `alameda`, we can use the `.append()` method to combine them without introducing any missing values, or spaces in our data table that do not have any information. Different packages can often have different ways of dealing with missing values, such completely ignoring any rows containing missing values to interpreting missing values as some default value. Since we do not have any missing values here, we don't have to worry about this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These three lines create a new column called 'county' in each of the GeoDataFrames,\n",
    "# then assigns the name of the county to all rows within the GeoDataFrame.\n",
    "alameda['county'] = 'alameda'\n",
    "contra_costa['county'] = 'contra costa'\n",
    "san_francisco['county'] = 'san francisco'\n",
    "\n",
    "# This creates a new, empty GeoDataFrame, then appends of our GeoDataFrames to it.\n",
    "data = gpd.GeoDataFrame()\n",
    "data = data.append(alameda)\n",
    "data = data.append(contra_costa)\n",
    "data = data.append(san_francisco)\n",
    "\n",
    "# This plots our new aggregated GeoDataFrame using the 'county' column we created\n",
    "# above.\n",
    "data.plot(column = 'county', legend = True, cmap = 'Set3')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the code for this map has essentially the same form as the choropleth maps we created earlier. Since the column we are using to distinguish the census transects from each other is text rather than continuous numerical values, the plotting software uses distinct colors rather than a smooth spectrum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More geospatial data types in `geopandas`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have only been creating maps using polygons, but `geopandas` has a few more data types we can work with. First, we need some new data to work with. In the `shapefiles` folder, we have folders named `bart_stations` and `bart_routes` containing geospatial data about the BART system. Load in the data as we did with `alameda` file from the beginning of the lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bart_stations = ...\n",
    "bart_routes = ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examining the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea of what the data we are working with looks like, let's call the `.head()` method on the data we have just read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bart_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bart_routes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the `geometry` columns of our two GeoDataFrames, we can see that we have some new types of data. Where we had polygons before, our new data have points and linestrings. Points are are exactly what they sound like, 1-dimensional coordinates in space. Linestrings are line segments that do not necessarily have to be a straight line.\n",
    "\n",
    "Use the following cell to draw a linestring the same way we did the first time we looked at a polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next few cells, you will create a map that overlays the bart stations and routes over a map of all the counties. (Alameda, Contra Costa, and San Francisco)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need the base map of all the counties. We can use the aggregated `GeoDataFrame` we created earlier in this notebook. Try setting the `color` and `edgecolor` arguments so the map looks more uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base = ...\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, overlay the routes and stations and plot the graph by using this as the base for the map. The Jupyter notebook will not remember the map you drew in the previous cell even if you assigned it a name, so make sure to plot the base map again in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base = ...\n",
    "bart_routes.plot(...)\n",
    "bart_stations.plot(...)\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You're done with this lab! If you are interested in learning more about what you can do with `geopandas`, you can find the documentation for the package [here](http://geopandas.org/reference.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authors: Monica Wilkinson and Vera Wang\n",
    "### References:\n",
    "- http://geopandas.org/mapping.html\n",
    "- https://matplotlib.org/users/colormaps.html\n",
    "- http://www.bayareacensus.ca.gov/small/small.htm\n",
    "- https://www.bart.gov/schedules/developers/geo"
   ]
  }
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