SI_370_Day_02_Pandas.ipynb

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              "         a    b    c    d\n",
              "count  3.0  3.0  3.0  3.0\n",
              "mean   2.0  5.0  8.0  3.0\n",
              "std    1.0  1.0  1.0  1.0\n",
              "min    1.0  4.0  7.0  2.0\n",
              "25%    1.5  4.5  7.5  2.5\n",
              "50%    2.0  5.0  8.0  3.0\n",
              "75%    2.5  5.5  8.5  3.5\n",
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              "a    2.0\n",
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              "      <td>2</td>\n",
              "      <td>one</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>two</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>three</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d      e    f\n",
              "0  1  4  7  2    one  1.0\n",
              "1  2  5  8  3    two  5.0\n",
              "2  3  6  9  4  three  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlcmM52cTEwO",
        "colab_type": "code",
        "outputId": "6dc7aa85-a636-4c14-aedd-cc5c53b244ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a      int64\n",
              "b      int64\n",
              "c      int64\n",
              "d      int64\n",
              "e     object\n",
              "f    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEkwu6gTTHUt",
        "colab_type": "code",
        "outputId": "2b0fbde9-6a8c-4887-cd8d-ddc06377ac78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df.select_dtypes(include=['int','float'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>f</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d    f\n",
              "0  1  4  7  2  1.0\n",
              "1  2  5  8  3  5.0\n",
              "2  3  6  9  4  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njcoBXQBVUKl",
        "colab_type": "text"
      },
      "source": [
        "## Exercise X\n",
        "Use .describe() to gather descriptive statistics on all the columns in the df DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARrnmG3QTae_",
        "colab_type": "code",
        "outputId": "dc41f8a2-d838-48a3-f242-dde2ff4546d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# Solution\n",
        "df.describe(include='all')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "      <th>f</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>three</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.5</td>\n",
              "      <td>5.5</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a    b    c    d      e    f\n",
              "count   3.0  3.0  3.0  3.0      3  3.0\n",
              "unique  NaN  NaN  NaN  NaN      3  NaN\n",
              "top     NaN  NaN  NaN  NaN  three  NaN\n",
              "freq    NaN  NaN  NaN  NaN      1  NaN\n",
              "mean    2.0  5.0  8.0  3.0    NaN  5.0\n",
              "std     1.0  1.0  1.0  1.0    NaN  4.0\n",
              "min     1.0  4.0  7.0  2.0    NaN  1.0\n",
              "25%     1.5  4.5  7.5  2.5    NaN  3.0\n",
              "50%     2.0  5.0  8.0  3.0    NaN  5.0\n",
              "75%     2.5  5.5  8.5  3.5    NaN  7.0\n",
              "max     3.0  6.0  9.0  4.0    NaN  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4Ztk3T2Uorl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_missing = pd.DataFrame(\n",
        "    {'a':[1,2,3,np.NaN],\n",
        "     'b':[2,3,np.NaN,5]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFa1uOP4WMZ9",
        "colab_type": "code",
        "outputId": "9126d7d0-3b56-4cd9-b24b-a34c14a557a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "df_missing"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "     a    b\n",
              "0  1.0  2.0\n",
              "1  2.0  3.0\n",
              "2  3.0  NaN\n",
              "3  NaN  5.0"
            ]
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          "execution_count": 21
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      "cell_type": "code",
      "metadata": {
        "id": "KFelUHdSWPFU",
        "colab_type": "code",
        "outputId": "85508734-81d3-44fa-ba1f-bf20fa3ecdb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "df_missing.dropna()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <th>b</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>3.0</td>\n",
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            "text/plain": [
              "     a    b\n",
              "0  1.0  2.0\n",
              "1  2.0  3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQLVMeGVWWz9",
        "colab_type": "code",
        "outputId": "f4e8e186-b99c-4aaf-ddf9-dda87adfe206",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
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      },
      "source": [
        "df_missing.dropna(subset=['a'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "     a    b\n",
              "0  1.0  2.0\n",
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              "2  3.0  NaN"
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        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>3</th>\n",
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              "     a    b\n",
              "0  1.0  2.0\n",
              "1  2.0  3.0\n",
              "2  3.0  0.0\n",
              "3  0.0  5.0"
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          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oaWe-G5WqZk",
        "colab_type": "code",
        "outputId": "ac2d34d9-faf6-448e-dbd6-42e4a8afb907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "df_missing.fillna(df_missing.mean())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "     a    b\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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            "text/plain": [
              "   a  b  c  d      e    f\n",
              "0  1  4  7  2    one  1.0\n",
              "1  2  5  8  3    two  5.0\n",
              "2  3  6  9  4  three  9.0"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "df.set_index('e')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>e</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>one</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
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              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>three</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
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              "      <td>9.0</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "       a  b  c  d    f\n",
              "e                     \n",
              "one    1  4  7  2  1.0\n",
              "two    2  5  8  3  5.0\n",
              "three  3  6  9  4  9.0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODT75D2PCbA2",
        "colab_type": "code",
        "outputId": "179d9019-d3ad-4841-f346-9162d69eea58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df.reset_index()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
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              "      <td>4</td>\n",
              "      <td>three</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  a  b  c  d      e    f\n",
              "0      0  1  4  7  2    one  1.0\n",
              "1      1  2  5  8  3    two  5.0\n",
              "2      2  3  6  9  4  three  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Em4zAtmtDJgl",
        "colab_type": "code",
        "outputId": "7f8fc50e-6ddc-4d28-9da4-76a550d529af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df # note that we have the original dataframe"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
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              "      <th>f</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>one</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>two</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>three</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d      e    f\n",
              "0  1  4  7  2    one  1.0\n",
              "1  2  5  8  3    two  5.0\n",
              "2  3  6  9  4  three  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6lXIfg5DWUE",
        "colab_type": "code",
        "outputId": "10503e95-a655-46c8-e699-331b53dfab43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "df.iloc[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a      2\n",
              "b      5\n",
              "c      8\n",
              "d      3\n",
              "e    two\n",
              "f      5\n",
              "Name: 1, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boO_p2WnDhBU",
        "colab_type": "code",
        "outputId": "b3e3da99-0f50-498c-b7ec-82298bbae151",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "df.iloc[1:3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "      <th>f</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>two</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>three</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d      e    f\n",
              "1  2  5  8  3    two  5.0\n",
              "2  3  6  9  4  three  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUO0wUt7DqAl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.set_index('e')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAMKSNYpEEb9",
        "colab_type": "code",
        "outputId": "3260ed0a-9267-4d81-e42f-daa9ded84330",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "df.loc['two']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    2.0\n",
              "b    5.0\n",
              "c    8.0\n",
              "d    3.0\n",
              "f    5.0\n",
              "Name: two, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
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              "       a  b  c  d    f\n",
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              "       a  b  c  d    f\n",
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            "text/plain": [
              "       a  b  c  d    f\n",
              "e                     \n",
              "one    1  4  7  2  1.0\n",
              "two    2  5  8  3  5.0\n",
              "three  3  6  9  4  9.0"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4MJtio2XTFc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from https://www.kaggle.com/austinreese/craigslist-carstrucks-data\n",
        "# 5000 row sample"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxQ78NN3cFw_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vehicles = pd.read_csv('https://raw.githubusercontent.com/umsi-data-science/si370/master/data/craigslist_vehicles_sample.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "dldu6e95QTta",
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          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "vehicles.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>region</th>\n",
              "      <th>region_url</th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>model</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>vin</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "      <th>image_url</th>\n",
              "      <th>description</th>\n",
              "      <th>county</th>\n",
              "      <th>state</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27187</td>\n",
              "      <td>7089214269</td>\n",
              "      <td>https://philadelphia.craigslist.org/ctd/d/sher...</td>\n",
              "      <td>philadelphia</td>\n",
              "      <td>https://philadelphia.craigslist.org</td>\n",
              "      <td>25500</td>\n",
              "      <td>1972.0</td>\n",
              "      <td>gmc</td>\n",
              "      <td>k1500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>NaN</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00V0V_9bz8wfoFDy...</td>\n",
              "      <td>1972 GMC K1500 short wheelbase 4x4 pickup. VIN...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pa</td>\n",
              "      <td>33.6372</td>\n",
              "      <td>-96.6184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>421279</td>\n",
              "      <td>7077070594</td>\n",
              "      <td>https://gainesville.craigslist.org/ctd/d/atlan...</td>\n",
              "      <td>gainesville</td>\n",
              "      <td>https://gainesville.craigslist.org</td>\n",
              "      <td>16200</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>nissan</td>\n",
              "      <td>370z coupe 2d</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>78635.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>JN1AZ44E99M405371</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coupe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/01414_bX28qk3nTy...</td>\n",
              "      <td>Welcome to Carvana—the new way to buy a car (e...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fl</td>\n",
              "      <td>33.7792</td>\n",
              "      <td>-84.4118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>111869</td>\n",
              "      <td>7093288110</td>\n",
              "      <td>https://phoenix.craigslist.org/wvl/ctd/d/glend...</td>\n",
              "      <td>phoenix</td>\n",
              "      <td>https://phoenix.craigslist.org</td>\n",
              "      <td>4488</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>buick</td>\n",
              "      <td>lesabre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>51000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>1G4HP52K43U273977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00D0D_gPLpOTMHAv...</td>\n",
              "      <td>2003 Buick LeSabre Custom     Offered by: OVER...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>az</td>\n",
              "      <td>33.5375</td>\n",
              "      <td>-112.1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>342219</td>\n",
              "      <td>7089016788</td>\n",
              "      <td>https://annarbor.craigslist.org/cto/d/ann-arbo...</td>\n",
              "      <td>ann arbor</td>\n",
              "      <td>https://annarbor.craigslist.org</td>\n",
              "      <td>31899</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>q5</td>\n",
              "      <td>like new</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>21100.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>WA1ANAFY0J2119658</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>silver</td>\n",
              "      <td>https://images.craigslist.org/00r0r_9qnRWk3ICA...</td>\n",
              "      <td>excellent condition Audi Q5 Premium  All wheel...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mi</td>\n",
              "      <td>42.3042</td>\n",
              "      <td>-83.7068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>461726</td>\n",
              "      <td>7091959768</td>\n",
              "      <td>https://raleigh.craigslist.org/ctd/d/wilmingto...</td>\n",
              "      <td>raleigh / durham / CH</td>\n",
              "      <td>https://raleigh.craigslist.org</td>\n",
              "      <td>25595</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f350 xlt crew dually</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>181500.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00h0h_b6Kzt011qz...</td>\n",
              "      <td>SAVE THOUSANDS! HURRY! WONT BE HERE LONG AT TH...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>nc</td>\n",
              "      <td>34.2548</td>\n",
              "      <td>-77.8456</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0          id  ...      lat      long\n",
              "0       27187  7089214269  ...  33.6372  -96.6184\n",
              "1      421279  7077070594  ...  33.7792  -84.4118\n",
              "2      111869  7093288110  ...  33.5375 -112.1590\n",
              "3      342219  7089016788  ...  42.3042  -83.7068\n",
              "4      461726  7091959768  ...  34.2548  -77.8456\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcyUjOQ3VskV",
        "colab_type": "text"
      },
      "source": [
        "## Exercise:\n",
        "The \"year\" column doesn't look right: the values are floats but they should be integers.  Use the \"astype()\" function to cast the values to 32-bit integers.  You might be tempted to do something like the following, which will result in an error.  Fix the error."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LUlnj8IRLYm",
        "colab_type": "code",
        "outputId": "2b881ea9-ff41-4da8-af3a-540add2b0163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "vehicles['year'] = vehicles['year'].astype('int32')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-61229f84d5e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvehicles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvehicles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int32'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5696\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5697\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5698\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5699\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, filter, **kwargs)\u001b[0m\n\u001b[1;32m    440\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    443\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    866\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    867\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 868\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot convert non-finite values (NA or inf) to integer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    869\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCKK5ha2WOeh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Solution\n",
        "vehicles['year'] = vehicles.year.fillna(0).astype('int32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOxduoAqU07f",
        "colab_type": "text"
      },
      "source": [
        "## Exercise:\n",
        "\n",
        "What proportion of listings were from the Ann Arbor region?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mU64NmxXQfkG",
        "colab_type": "code",
        "outputId": "0327225a-0097-4634-da80-917ef3c09ab9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Solution\n",
        "len(vehicles[vehicles['region'] == 'ann arbor'])/len(vehicles)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0028"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjcFfzcFWv0g",
        "colab_type": "text"
      },
      "source": [
        "## Exercise:\n",
        "List the top 3 manufacturers (note: when we say \"top N\", we typically mean the top N ranked from most common to least common)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6AtQ39CRDsQ",
        "colab_type": "code",
        "outputId": "a668d212-19ce-42bd-f49d-dfa0782c1d08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        }
      },
      "source": [
        "# Solution\n",
        "vehicles.manufacturer.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ford               941\n",
              "chevrolet          702\n",
              "toyota             377\n",
              "nissan             291\n",
              "honda              248\n",
              "jeep               242\n",
              "ram                241\n",
              "gmc                218\n",
              "dodge              189\n",
              "bmw                160\n",
              "subaru             110\n",
              "hyundai            107\n",
              "volkswagen          94\n",
              "mercedes-benz       94\n",
              "kia                 92\n",
              "chrysler            72\n",
              "cadillac            68\n",
              "lexus               63\n",
              "buick               60\n",
              "mazda               55\n",
              "audi                52\n",
              "infiniti            47\n",
              "acura               36\n",
              "pontiac             33\n",
              "volvo               33\n",
              "lincoln             31\n",
              "mitsubishi          31\n",
              "mercury             23\n",
              "rover               16\n",
              "mini                16\n",
              "saturn              12\n",
              "fiat                11\n",
              "jaguar               6\n",
              "harley-davidson      2\n",
              "tesla                2\n",
              "datsun               1\n",
              "ferrari              1\n",
              "Name: manufacturer, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UQ3i9fZZhQJ",
        "colab_type": "code",
        "outputId": "eb6db7a0-5c6e-4cbe-93f4-bd7380fd9654",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        }
      },
      "source": [
        "vehicles.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>region</th>\n",
              "      <th>region_url</th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>model</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>vin</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "      <th>image_url</th>\n",
              "      <th>description</th>\n",
              "      <th>county</th>\n",
              "      <th>state</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27187</td>\n",
              "      <td>7089214269</td>\n",
              "      <td>https://philadelphia.craigslist.org/ctd/d/sher...</td>\n",
              "      <td>philadelphia</td>\n",
              "      <td>https://philadelphia.craigslist.org</td>\n",
              "      <td>25500</td>\n",
              "      <td>1972</td>\n",
              "      <td>gmc</td>\n",
              "      <td>k1500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>NaN</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00V0V_9bz8wfoFDy...</td>\n",
              "      <td>1972 GMC K1500 short wheelbase 4x4 pickup. VIN...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pa</td>\n",
              "      <td>33.6372</td>\n",
              "      <td>-96.6184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>421279</td>\n",
              "      <td>7077070594</td>\n",
              "      <td>https://gainesville.craigslist.org/ctd/d/atlan...</td>\n",
              "      <td>gainesville</td>\n",
              "      <td>https://gainesville.craigslist.org</td>\n",
              "      <td>16200</td>\n",
              "      <td>2009</td>\n",
              "      <td>nissan</td>\n",
              "      <td>370z coupe 2d</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>78635.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>JN1AZ44E99M405371</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coupe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/01414_bX28qk3nTy...</td>\n",
              "      <td>Welcome to Carvana—the new way to buy a car (e...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fl</td>\n",
              "      <td>33.7792</td>\n",
              "      <td>-84.4118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>111869</td>\n",
              "      <td>7093288110</td>\n",
              "      <td>https://phoenix.craigslist.org/wvl/ctd/d/glend...</td>\n",
              "      <td>phoenix</td>\n",
              "      <td>https://phoenix.craigslist.org</td>\n",
              "      <td>4488</td>\n",
              "      <td>2003</td>\n",
              "      <td>buick</td>\n",
              "      <td>lesabre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>51000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>1G4HP52K43U273977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00D0D_gPLpOTMHAv...</td>\n",
              "      <td>2003 Buick LeSabre Custom     Offered by: OVER...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>az</td>\n",
              "      <td>33.5375</td>\n",
              "      <td>-112.1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>342219</td>\n",
              "      <td>7089016788</td>\n",
              "      <td>https://annarbor.craigslist.org/cto/d/ann-arbo...</td>\n",
              "      <td>ann arbor</td>\n",
              "      <td>https://annarbor.craigslist.org</td>\n",
              "      <td>31899</td>\n",
              "      <td>2018</td>\n",
              "      <td>audi</td>\n",
              "      <td>q5</td>\n",
              "      <td>like new</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>21100.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>WA1ANAFY0J2119658</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>silver</td>\n",
              "      <td>https://images.craigslist.org/00r0r_9qnRWk3ICA...</td>\n",
              "      <td>excellent condition Audi Q5 Premium  All wheel...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mi</td>\n",
              "      <td>42.3042</td>\n",
              "      <td>-83.7068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>461726</td>\n",
              "      <td>7091959768</td>\n",
              "      <td>https://raleigh.craigslist.org/ctd/d/wilmingto...</td>\n",
              "      <td>raleigh / durham / CH</td>\n",
              "      <td>https://raleigh.craigslist.org</td>\n",
              "      <td>25595</td>\n",
              "      <td>2015</td>\n",
              "      <td>ford</td>\n",
              "      <td>f350 xlt crew dually</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>181500.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00h0h_b6Kzt011qz...</td>\n",
              "      <td>SAVE THOUSANDS! HURRY! WONT BE HERE LONG AT TH...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>nc</td>\n",
              "      <td>34.2548</td>\n",
              "      <td>-77.8456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>188718</td>\n",
              "      <td>7091352525</td>\n",
              "      <td>https://newyork.craigslist.org/lgi/cto/d/hemps...</td>\n",
              "      <td>new york city</td>\n",
              "      <td>https://newyork.craigslist.org</td>\n",
              "      <td>1500</td>\n",
              "      <td>2002</td>\n",
              "      <td>honda</td>\n",
              "      <td>accord sedan</td>\n",
              "      <td>good</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>127000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>red</td>\n",
              "      <td>https://images.craigslist.org/00V0V_dGAFyHgUOD...</td>\n",
              "      <td>This car is the perfect car to get you from po...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ny</td>\n",
              "      <td>40.7060</td>\n",
              "      <td>-73.6190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>229922</td>\n",
              "      <td>7090189512</td>\n",
              "      <td>https://cleveland.craigslist.org/ctd/d/cuyahog...</td>\n",
              "      <td>cleveland</td>\n",
              "      <td>https://cleveland.craigslist.org</td>\n",
              "      <td>42882</td>\n",
              "      <td>2017</td>\n",
              "      <td>rover</td>\n",
              "      <td>sport</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>78012.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>SALWR2FE3HA129380</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SUV</td>\n",
              "      <td>brown</td>\n",
              "      <td>https://images.craigslist.org/00t0t_6P5lE0Cd60...</td>\n",
              "      <td>2017 Land Rover Range Rover Sport -- $42,882 C...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>oh</td>\n",
              "      <td>41.1259</td>\n",
              "      <td>-81.5080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>429837</td>\n",
              "      <td>7093276189</td>\n",
              "      <td>https://philadelphia.craigslist.org/cto/d/warr...</td>\n",
              "      <td>philadelphia</td>\n",
              "      <td>https://philadelphia.craigslist.org</td>\n",
              "      <td>27250</td>\n",
              "      <td>2011</td>\n",
              "      <td>dodge</td>\n",
              "      <td>challenger</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>5421.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>2B3CJ7DJ4BH505142</td>\n",
              "      <td>rwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>coupe</td>\n",
              "      <td>blue</td>\n",
              "      <td>https://images.craigslist.org/00a0a_a1IciKcM6M...</td>\n",
              "      <td>Inaugural Edition #793/1100 Deep Water Blue Pe...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pa</td>\n",
              "      <td>40.2502</td>\n",
              "      <td>-75.1776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>146446</td>\n",
              "      <td>7084560454</td>\n",
              "      <td>https://siouxfalls.craigslist.org/ctd/d/sauk-c...</td>\n",
              "      <td>sioux falls / SE SD</td>\n",
              "      <td>https://siouxfalls.craigslist.org</td>\n",
              "      <td>38995</td>\n",
              "      <td>2016</td>\n",
              "      <td>gmc</td>\n",
              "      <td>sierra slt 3500 hd diese</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>diesel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>clean</td>\n",
              "      <td>other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>white</td>\n",
              "      <td>https://images.craigslist.org/01414_9Roq39WWer...</td>\n",
              "      <td>2016 GMC Sierra SLT 3500 HD Diesel Truck, Whit...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sd</td>\n",
              "      <td>45.7281</td>\n",
              "      <td>-94.9682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>46754</td>\n",
              "      <td>7091354973</td>\n",
              "      <td>https://siouxcity.craigslist.org/ctd/d/ponca-1...</td>\n",
              "      <td>sioux city</td>\n",
              "      <td>https://siouxcity.craigslist.org</td>\n",
              "      <td>9999</td>\n",
              "      <td>1998</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>gmt-400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>172000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>2GCEK19R9W1179420</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pickup</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/01010_DF7n32P8dS...</td>\n",
              "      <td>1998 Chevrolet Chevy C/K 1500 Series K1500 Sil...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ia</td>\n",
              "      <td>42.5374</td>\n",
              "      <td>-96.7441</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0          id  ...      lat      long\n",
              "0       27187  7089214269  ...  33.6372  -96.6184\n",
              "1      421279  7077070594  ...  33.7792  -84.4118\n",
              "2      111869  7093288110  ...  33.5375 -112.1590\n",
              "3      342219  7089016788  ...  42.3042  -83.7068\n",
              "4      461726  7091959768  ...  34.2548  -77.8456\n",
              "5      188718  7091352525  ...  40.7060  -73.6190\n",
              "6      229922  7090189512  ...  41.1259  -81.5080\n",
              "7      429837  7093276189  ...  40.2502  -75.1776\n",
              "8      146446  7084560454  ...  45.7281  -94.9682\n",
              "9       46754  7091354973  ...  42.5374  -96.7441\n",
              "\n",
              "[10 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73IL0knjZjcn",
        "colab_type": "code",
        "outputId": "db80bd39-47db-4260-d9d4-bb4552f19e3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "vehicles.vin.dropna().map(lambda x:x[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       J\n",
              "2       1\n",
              "3       W\n",
              "6       S\n",
              "7       2\n",
              "       ..\n",
              "4992    1\n",
              "4995    5\n",
              "4996    1\n",
              "4997    W\n",
              "4999    1\n",
              "Name: vin, Length: 2911, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvBj_e99aS1-",
        "colab_type": "code",
        "outputId": "de8e74cb-e76d-4a5d-cac9-74d12761e85a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "vehicles.title_status.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clean         4772\n",
              "rebuilt        100\n",
              "salvage         58\n",
              "lien            27\n",
              "missing          8\n",
              "parts only       2\n",
              "Name: title_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVa5YcKac0SY",
        "colab_type": "code",
        "outputId": "6ac53f10-a29f-4f5f-9c9a-e9d9197c974f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "vehicles.cylinders.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6 cylinders     1124\n",
              "4 cylinders      881\n",
              "8 cylinders      866\n",
              "5 cylinders       29\n",
              "10 cylinders      25\n",
              "other             15\n",
              "3 cylinders        6\n",
              "12 cylinders       1\n",
              "Name: cylinders, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOV8XDBQfTMg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import ee"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiBSwGZufin3",
        "colab_type": "code",
        "outputId": "bff15f46-5e62-4fc6-8f37-e15dc6be0eb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "ee.Authenticate()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions. If the web browser does not start automatically, please manually browse the URL below.\n",
            "\n",
            "    https://accounts.google.com/o/oauth2/auth?client_id=517222506229-vsmmajv00ul0bs7p89v5m89qs8eb9359.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fearthengine+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdevstorage.full_control&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&code_challenge=uJaQq4wDvDzGSPs1nCMyrTe5i5QaWBnb63o914GETVo&code_challenge_method=S256\n",
            "\n",
            "The authorization workflow will generate a code, which you should paste in the box below. \n",
            "Enter verification code: 4/ygH_UZvBcfYlXXX6Mps443KoTAn2To4V9sH1blCJxkbDgLULh5hBG30\n",
            "\n",
            "Successfully saved authorization token.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "do1_yecVfkfQ",
        "colab_type": "code",
        "outputId": "09c3b000-0781-46c5-8c16-143946599fab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "ee.Initialize()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "EEException",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHttpError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ee/data.py\u001b[0m in \u001b[0;36m_execute_cloud_call\u001b[0;34m(call, num_retries)\u001b[0m\n\u001b[1;32m    337\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mapiclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHttpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/googleapiclient/_helpers.py\u001b[0m in \u001b[0;36mpositional_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    133\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/googleapiclient/http.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, http, num_retries)\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHttpError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muri\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    899\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostproc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHttpError\u001b[0m: <HttpError 403 when requesting https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/algorithms?prettyPrint=false&alt=json returned \"Permission denied.\">",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mEEException\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-99e03d56abd3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mee\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ee/__init__.py\u001b[0m in \u001b[0;36mInitialize\u001b[0;34m(credentials, opt_url, use_cloud_api, cloud_api_key, http_transport, project)\u001b[0m\n\u001b[1;32m    123\u001b[0m       http_transport=http_transport)\n\u001b[1;32m    124\u001b[0m   \u001b[0;31m# Initialize the dynamically loaded functions on the objects that want them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m   \u001b[0mApiFunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m   \u001b[0mElement\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m   \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ee/apifunction.py\u001b[0m in \u001b[0;36minitialize\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;34m\"\"\"Initializes the list of signatures from the Earth Engine front-end.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_api\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m       \u001b[0msignatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetAlgorithms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    155\u001b[0m       \u001b[0mapi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msignatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ee/data.py\u001b[0m in \u001b[0;36mgetAlgorithms\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1017\u001b[0m       call = _cloud_api_resource.projects().algorithms().list(\n\u001b[1;32m   1018\u001b[0m           project=_get_projects_path(), prettyPrint=False)\n\u001b[0;32m-> 1019\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_cloud_api_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_algorithms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_execute_cloud_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1020\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0msend_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/algorithms'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'GET'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ee/data.py\u001b[0m in \u001b[0;36m_execute_cloud_call\u001b[0;34m(call, num_retries)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mapiclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHttpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0m_translate_cloud_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mEEException\u001b[0m: Permission denied."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiE12mu7fqsh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}