cowdata_visualizations.ipynb

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  "metadata": {
    "colab": {
      "name": "cowdata_visualizations.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAphFaCuh7jY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ti_ClGsOiYwe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df['cattle-id'] = df['cattle-id'].str.replace(r\"Cattle ID : \", \"\")\n",
        "# df['age'] = df['age'].str.replace(r\"Age : \", \"\")\n",
        "# df['color'] = df['color'].str.replace(r\"Color : \", \"\")\n",
        "# df['breed'] = df['breed'].str.replace(r\"Breed : \", \"\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOtLVL43qShp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df['weight'] = df['weight'].str.replace(r\"Live weight \", \"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4wLQ_ICrBT9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df['weight'] = df['weight'].str.replace(r\"\\(.*\\)\",\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nO8a1oiJriWe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df['weight'] = df['weight'].str.replace(r\" : \",\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTAkCNstj-9p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqaATQJ_m4gD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EKnoqmTCKuk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ad2bebb0-8e29-4bfb-83f8-94d481505745"
      },
      "source": [
        "df = pd.read_csv(\"cleaned_data.csv\")\n",
        "df.head()"
      ],
      "execution_count": 87,
      "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>cattle-id</th>\n",
              "      <th>price</th>\n",
              "      <th>age</th>\n",
              "      <th>color</th>\n",
              "      <th>breed</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BLF1118</td>\n",
              "      <td>91000</td>\n",
              "      <td>2.5 years</td>\n",
              "      <td>Red</td>\n",
              "      <td>Red Chittagong</td>\n",
              "      <td>217 kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BLFS232</td>\n",
              "      <td>186000</td>\n",
              "      <td>2.5 years</td>\n",
              "      <td>Non Red</td>\n",
              "      <td>Holstein Friesian Cross</td>\n",
              "      <td>469 kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BLF960</td>\n",
              "      <td>118000</td>\n",
              "      <td>2.5 years</td>\n",
              "      <td>Red</td>\n",
              "      <td>Local</td>\n",
              "      <td>282 kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BLF1091</td>\n",
              "      <td>118000</td>\n",
              "      <td>2.5 years</td>\n",
              "      <td>Red</td>\n",
              "      <td>Local</td>\n",
              "      <td>283 kg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BLF1324</td>\n",
              "      <td>125000</td>\n",
              "      <td>2.5 years</td>\n",
              "      <td>Red</td>\n",
              "      <td>Red Chittagong</td>\n",
              "      <td>298 kg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  cattle-id   price        age    color                    breed  weight\n",
              "0   BLF1118   91000  2.5 years      Red           Red Chittagong  217 kg\n",
              "1   BLFS232  186000  2.5 years  Non Red  Holstein Friesian Cross  469 kg\n",
              "2    BLF960  118000  2.5 years      Red                    Local  282 kg\n",
              "3   BLF1091  118000  2.5 years      Red                    Local  283 kg\n",
              "4   BLF1324  125000  2.5 years      Red           Red Chittagong  298 kg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCj5py96KwDk",
        "colab_type": "text"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaXN6ZJfCBtR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to produce more beautiful pie charts with matplotlib\n",
        "def pie_chart(fractions, #values for the wedges\n",
        "              labels, #labels for the wedges\n",
        "              title = '', #title of the pie chart\n",
        "              cm_name = 'Pastel1', #name of the matplotlib colormap to use\n",
        "              autopct = lambda x: str(round(x, 1)) + '%', #format the value text on each pie wedge\n",
        "              labeldistance = 1.05, #where to place wedge labels in relation to pie wedges\n",
        "              shadow = True, #shadow around the pie\n",
        "              startangle = 0, #rotate 90 degrees to start the top of the data set on the top of the pie\n",
        "              edgecolor = 'w', #color of pie wedge edges\n",
        "              width = 8, #width of the figure in inches\n",
        "              height = 8, #height of the figure in inches\n",
        "              grouping_threshold = None, #group all wedges below this value into one 'all others' wedge\n",
        "              grouping_label = None): #what to label the grouped wedge\n",
        "    \n",
        "    # if the user passed a threshold value, group all fractions lower than it into one 'misc' pie wedge\n",
        "    if not grouping_threshold==None:\n",
        "        \n",
        "        # if user didn't pass a label, apply a default text\n",
        "        if grouping_label == None:\n",
        "            grouping_label = 'Others'\n",
        "\n",
        "        # select the rows greater than the cut-off value\n",
        "        row_mask = fractions > grouping_threshold\n",
        "        meets_threshold = fractions[row_mask]\n",
        "\n",
        "        # group all other rows below the cut-off value\n",
        "        all_others = pd.Series(fractions[~row_mask].sum())\n",
        "        all_others.index = [grouping_label]\n",
        "\n",
        "        # append the grouped row to the bottom of the rows to display\n",
        "        fractions = meets_threshold.append(all_others)\n",
        "        labels = fractions.index\n",
        "    \n",
        "    # get the color map then pull 1 color from it for each pie wedge we'll draw\n",
        "    color_map = cm.get_cmap(cm_name)\n",
        "    num_of_colors = len(fractions)\n",
        "    colors = color_map([x/float(num_of_colors) for x in range(num_of_colors)])\n",
        "    \n",
        "    # create the figure and an axis to plot on\n",
        "    fig, ax = plt.subplots(figsize=[width, height])\n",
        "    \n",
        "    # plot the pie\n",
        "    wedges = ax.pie(fractions, \n",
        "                    labels = labels, \n",
        "                    labeldistance = labeldistance,\n",
        "                    autopct = autopct,\n",
        "                    colors = colors,\n",
        "                    shadow = shadow, \n",
        "                    startangle = startangle)\n",
        "    \n",
        "    # change the edgecolor for each wedge\n",
        "    for wedge in wedges[0]:\n",
        "        wedge.set_edgecolor(edgecolor)\n",
        "    \n",
        "    # set the title and show the plot\n",
        "    ax.set_title(title)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbAytlbiuG5u",
        "colab_type": "code",
        "outputId": "5d3a522b-6ac2-438f-ebb4-6f0ef410aeec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        }
      },
      "source": [
        "\n",
        "price_counts = df['price'].value_counts()\n",
        "price_counts.plot(kind='bar', title='Price', figsize=(22,8), width=0.6, alpha=0.5, \n",
        "                    color='g', edgecolor='k')\n"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f41597ea9b0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
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AAABgAII+AAAAABiAoA8AAAAABiDoAwAAAIABCPoAAAAAYACCPgAAAAAYgKAP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\nf00/UZc6xlpHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1584x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hgGQF_SWA_B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "c6eaa69b-3420-45a0-b63e-1af3bec61be4"
      },
      "source": [
        "weight_counts = df['weight'].value_counts()\n",
        "weight_counts.plot(kind='bar', title='Weight', figsize=(22,8), width=0.6, alpha=0.4, \n",
        "                    color='r', edgecolor='k')\n"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4155990358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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trC0OJLlVoz5f0yhTGa67qH9+KMkT1myLsfP+eKNMVyY5Zwv1abXjWL8fzfJx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\n",
            "text/plain": [
              "<Figure size 1584x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8PqszRIO49j",
        "colab_type": "code",
        "outputId": "0b9dd196-b605-4687-de05-4ea8bb97492b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "price_median = df['price'].median()\n",
        "below_median = df['price'].where(df['price'] < price_median).dropna()\n",
        "above_median = df['price'].where(df['price'] >= price_median).dropna()\n",
        "len(below_median), len(above_median)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(230, 236)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcS01yXeZGLh",
        "colab_type": "code",
        "outputId": "a3f58608-76c4-4feb-b8c1-0621d16c753e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "price_median"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "105000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBhH4znXyIIA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "breed_counts = df['breed'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gu-idC1EJDCr",
        "colab_type": "code",
        "outputId": "71dc753e-3a18-4636-bedc-d9ea8961f6c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        }
      },
      "source": [
        "pie_chart(fractions = breed_counts,\n",
        "          labels = breed_counts.index,\n",
        "          title = 'Percentage of breeds'\n",
        "          )"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Aw996ww2LWfOMS2DdesaY0S1cubyCMLwVKIu7FjM6WFAoID8d8oIlp5xSOnHy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qhfQU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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tMDfUDYCWWQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "outputId": "d7d27f8f-9f20-4e25-ff87-b4857db5c466"
      },
      "source": [
        "color_counts = df['color'].value_counts()\n",
        "color_counts.plot(kind='bar', title='Color', figsize=(10,8), width=0.6, alpha=0.4, \n",
        "                    color='b', edgecolor='k')"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41598f4438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AkQUAMIDIAgAYQGQBAAwgsgAABhBZAAADiCwAgAFEFgDAACILAGAAkQUAMIDIAgAYQGQB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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Wzt6VRqTZOY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "outputId": "96af0240-1ce1-47a0-cd3e-3fbb0c8b2c66"
      },
      "source": [
        "age_counts = df['age'].value_counts()\n",
        "age_counts.plot(kind='bar', title='Age', figsize=(10,8), width=0.6, alpha=0.4, \n",
        "                    color='g', edgecolor='k')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4159901550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b92IMAIDlc2sj6yVJ7pXkhCRXJ3nBnv4G3X1Wd2/t7q1btmy5lWMAACynWxVZ3X1td9/Y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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9_2jxOf_GZk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2tjkHjc_K2x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}