9_plotting_with_pandas.ipynb

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          "sourceId": 2368,
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          "datasetId": 1312
        }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting With Pandas\n"
      ],
      "metadata": {
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        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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      "cell_type": "markdown",
      "source": [
        "Visualizations are one of the most powerful tools at your disposal for exploring data and communicating data insights. The pandas library includes basic plotting capabilities that let you create a variety of plots from DataFrames. Plots in pandas are built on top of a popular Python plotting library called matplotlib, which comes with the Anaconda Python distribution.\n",
        "\n",
        "Let's start by loading some packages, as well as a \"Diamonds\" data set which we'll use as a sample data set for plotting:"
      ],
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
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        "id": "MBog4Q4orcPi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds = pd.read_csv(\"../input/diamonds/diamonds.csv\")\n",
        "diamonds = diamonds.drop(\"Unnamed: 0\", axis=1)\n",
        "\n",
        "#diamonds = diamonds.rename(columns={\"Unnamed: 0\": \"idx_counter\"})\n",
        "\n",
        "print(diamonds.shape)        # Check data shape\n",
        "\n",
        "diamonds.head(5)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T13:41:19.153457Z",
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        "id": "TNkrgZjXrcPk",
        "outputId": "0b31ea2f-df1c-419c-8fc0-752634a9a8d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "(53940, 10)\n",
          "output_type": "stream"
        },
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   carat      cut color clarity  depth  table  price     x     y     z\n0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75",
            "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>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>depth</th>\n      <th>table</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.23</td>\n      <td>Ideal</td>\n      <td>E</td>\n      <td>SI2</td>\n      <td>61.5</td>\n      <td>55.0</td>\n      <td>326</td>\n      <td>3.95</td>\n      <td>3.98</td>\n      <td>2.43</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.21</td>\n      <td>Premium</td>\n      <td>E</td>\n      <td>SI1</td>\n      <td>59.8</td>\n      <td>61.0</td>\n      <td>326</td>\n      <td>3.89</td>\n      <td>3.84</td>\n      <td>2.31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.23</td>\n      <td>Good</td>\n      <td>E</td>\n      <td>VS1</td>\n      <td>56.9</td>\n      <td>65.0</td>\n      <td>327</td>\n      <td>4.05</td>\n      <td>4.07</td>\n      <td>2.31</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.29</td>\n      <td>Premium</td>\n      <td>I</td>\n      <td>VS2</td>\n      <td>62.4</td>\n      <td>58.0</td>\n      <td>334</td>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>2.63</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.31</td>\n      <td>Good</td>\n      <td>J</td>\n      <td>SI2</td>\n      <td>63.3</td>\n      <td>58.0</td>\n      <td>335</td>\n      <td>4.34</td>\n      <td>4.35</td>\n      <td>2.75</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The output shows that data set contains 10 features of 53940 different diamonds, including both numeric and categorical variables."
      ],
      "metadata": {
        "id": "uabYZMIhrcPm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histograms"
      ],
      "metadata": {
        "id": "77_9yNfRrcPm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A histogram is a univariate plot (a plot that displays one variable) that groups a numeric variable into bins and displays the number of observations that fall within each bin. A histogram is a useful tool for getting a sense of the distribution of a numeric variable. Let's create a histogram of diamond carat weight with the df.hist() function:"
      ],
      "metadata": {
        "id": "eJohfcN5rcPn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.hist(column=\"carat\",        # Column to plot\n",
        "              figsize=(8,8),         # Plot size\n",
        "              color=\"green\");         # Plot color"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:26:09.605562Z",
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        "outputId": "09ca7ee2-2113-4128-f9e6-d93035982505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see immediately that the carat weights are positively skewed: most diamonds are around 1 carat or below but there are extreme cases of larger diamonds.\n",
        "\n",
        "The plot above has fairly wide bins and there doesn't appear to be any data beyond a carat size of 3.5. We can try to get more out of our histogram by adding some additional arguments to control the size of the bins and limits of the x-axis:"
      ],
      "metadata": {
        "id": "DIUijpBHrcPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.hist(column=\"carat\",        # Column to plot\n",
        "              figsize=(8,8),         # Plot size\n",
        "              color=\"blue\",          # Plot color\n",
        "              bins=50,               # Use 50 bins\n",
        "              range= (0,3.5));       # Limit x-axis range"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:28:38.453969Z",
          "iopub.execute_input": "2024-08-29T14:28:38.454391Z",
          "iopub.status.idle": "2024-08-29T14:28:39.050441Z",
          "shell.execute_reply.started": "2024-08-29T14:28:38.454332Z",
          "shell.execute_reply": "2024-08-29T14:28:39.049488Z"
        },
        "trusted": true,
        "id": "An_OBBZgrcPo",
        "outputId": "e4e509df-97bd-4783-d92e-475e929771fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This histogram gives us a better sense of some subtleties within the distribution, but we can't be sure that it contains all the data. Limiting the X-axis to 3.5 might have cut out some outliers with counts so small that they didn't show up as bars on our original chart. Let's check to see if any diamonds are larger than 3.5 carats:"
      ],
      "metadata": {
        "id": "nI14jUjYrcPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds[diamonds[\"carat\"] > 3.5]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:29:17.003076Z",
          "iopub.execute_input": "2024-08-29T14:29:17.003439Z",
          "iopub.status.idle": "2024-08-29T14:29:17.041606Z",
          "shell.execute_reply.started": "2024-08-29T14:29:17.003384Z",
          "shell.execute_reply": "2024-08-29T14:29:17.040704Z"
        },
        "trusted": true,
        "id": "rNDkr-BFrcPo",
        "outputId": "ce76bf2e-cf46-4206-c170-51ce382798e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "       carat        cut color clarity  ...   price      x      y     z\n23644   3.65       Fair     H      I1  ...   11668   9.53   9.48  6.38\n25998   4.01    Premium     I      I1  ...   15223  10.14  10.10  6.17\n25999   4.01    Premium     J      I1  ...   15223  10.02   9.94  6.24\n26444   4.00  Very Good     I      I1  ...   15984  10.01   9.94  6.31\n26534   3.67    Premium     I      I1  ...   16193   9.86   9.81  6.13\n27130   4.13       Fair     H      I1  ...   17329  10.00   9.85  6.43\n27415   5.01       Fair     J      I1  ...   18018  10.74  10.54  6.98\n27630   4.50       Fair     J      I1  ...   18531  10.23  10.16  6.72\n27679   3.51    Premium     J     VS2  ...   18701   9.66   9.63  6.03\n\n[9 rows x 10 columns]",
            "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>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>depth</th>\n      <th>table</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>23644</th>\n      <td>3.65</td>\n      <td>Fair</td>\n      <td>H</td>\n      <td>I1</td>\n      <td>67.1</td>\n      <td>53.0</td>\n      <td>11668</td>\n      <td>9.53</td>\n      <td>9.48</td>\n      <td>6.38</td>\n    </tr>\n    <tr>\n      <th>25998</th>\n      <td>4.01</td>\n      <td>Premium</td>\n      <td>I</td>\n      <td>I1</td>\n      <td>61.0</td>\n      <td>61.0</td>\n      <td>15223</td>\n      <td>10.14</td>\n      <td>10.10</td>\n      <td>6.17</td>\n    </tr>\n    <tr>\n      <th>25999</th>\n      <td>4.01</td>\n      <td>Premium</td>\n      <td>J</td>\n      <td>I1</td>\n      <td>62.5</td>\n      <td>62.0</td>\n      <td>15223</td>\n      <td>10.02</td>\n      <td>9.94</td>\n      <td>6.24</td>\n    </tr>\n    <tr>\n      <th>26444</th>\n      <td>4.00</td>\n      <td>Very Good</td>\n      <td>I</td>\n      <td>I1</td>\n      <td>63.3</td>\n      <td>58.0</td>\n      <td>15984</td>\n      <td>10.01</td>\n      <td>9.94</td>\n      <td>6.31</td>\n    </tr>\n    <tr>\n      <th>26534</th>\n      <td>3.67</td>\n      <td>Premium</td>\n      <td>I</td>\n      <td>I1</td>\n      <td>62.4</td>\n      <td>56.0</td>\n      <td>16193</td>\n      <td>9.86</td>\n      <td>9.81</td>\n      <td>6.13</td>\n    </tr>\n    <tr>\n      <th>27130</th>\n      <td>4.13</td>\n      <td>Fair</td>\n      <td>H</td>\n      <td>I1</td>\n      <td>64.8</td>\n      <td>61.0</td>\n      <td>17329</td>\n      <td>10.00</td>\n      <td>9.85</td>\n      <td>6.43</td>\n    </tr>\n    <tr>\n      <th>27415</th>\n      <td>5.01</td>\n      <td>Fair</td>\n      <td>J</td>\n      <td>I1</td>\n      <td>65.5</td>\n      <td>59.0</td>\n      <td>18018</td>\n      <td>10.74</td>\n      <td>10.54</td>\n      <td>6.98</td>\n    </tr>\n    <tr>\n      <th>27630</th>\n      <td>4.50</td>\n      <td>Fair</td>\n      <td>J</td>\n      <td>I1</td>\n      <td>65.8</td>\n      <td>58.0</td>\n      <td>18531</td>\n      <td>10.23</td>\n      <td>10.16</td>\n      <td>6.72</td>\n    </tr>\n    <tr>\n      <th>27679</th>\n      <td>3.51</td>\n      <td>Premium</td>\n      <td>J</td>\n      <td>VS2</td>\n      <td>62.5</td>\n      <td>59.0</td>\n      <td>18701</td>\n      <td>9.66</td>\n      <td>9.63</td>\n      <td>6.03</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It turns out that 9 diamonds are bigger than 3.5 carats. Should cutting these diamonds out concern us? On one hand, these outliers have very little bearing on the shape of the distribution. On the other hand, limiting the X-axis to 3.5 implies that no data lies beyond that point. For our own exploratory purposes this is not an issue but if we were to show this plot to someone else, it could be misleading. Including a note that 9 diamonds lie beyond the chart range could be helpful."
      ],
      "metadata": {
        "id": "4VN1xVBOrcPp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> <font color='green'>**The aforementioned visualizations are part of analysis where we decided to not exclud the outliers**</font>\n"
      ],
      "metadata": {
        "id": "tQw5XaNErcPp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplots"
      ],
      "metadata": {
        "id": "9WSSV7glrcPp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Boxplots are another type of univariate plot for summarizing distributions of numeric data graphically. Let's make a boxplot of carat using the pd.boxplot() function:"
      ],
      "metadata": {
        "id": "EsOHHN0ZrcPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.boxplot(column=\"carat\");"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:37:31.794798Z",
          "iopub.execute_input": "2024-08-29T14:37:31.795119Z",
          "iopub.status.idle": "2024-08-29T14:37:32.143094Z",
          "shell.execute_reply.started": "2024-08-29T14:37:31.795069Z",
          "shell.execute_reply": "2024-08-29T14:37:32.141538Z"
        },
        "trusted": true,
        "id": "pLSURVtvrcPp",
        "outputId": "657bd533-11e8-4cfd-e37a-9e321b10cf9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we learned in lesson 14, the central box of the boxplot represents the middle 50% of the observations, the central bar is the median and the bars at the end of the dotted lines (whiskers) encapsulate the great majority of the observations. Circles that lie beyond the end of the whiskers are data points that may be outliers.\n",
        "\n",
        "In this case, our data set has over 50,000 observations and we see many data points beyond the top whisker. We probably wouldn't want to classify all of those points as outliers, but the handful of diamonds at 4 carats and above are definitely far outside the norm.\n",
        "\n",
        "One of the most useful features of a boxplot is the ability to make side-by-side boxplots. A side-by-side boxplot takes a numeric variable and splits it on based on some categorical variable, drawing a different boxplot for each level of the categorical variable. Let's make a side-by-side boxplot of diamond price split by diamond clarity:"
      ],
      "metadata": {
        "id": "6JmIWcmFrcPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.boxplot(column=\"price\",        # Column to plot\n",
        "                 by= \"clarity\",         # Column to split upon\n",
        "                 figsize= (8,8));       # Figure size"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:48:02.863441Z",
          "iopub.execute_input": "2024-08-29T14:48:02.863758Z",
          "iopub.status.idle": "2024-08-29T14:48:03.532283Z",
          "shell.execute_reply.started": "2024-08-29T14:48:02.863709Z",
          "shell.execute_reply": "2024-08-29T14:48:03.531212Z"
        },
        "trusted": true,
        "id": "nts5OqBlrcPq",
        "outputId": "7f45b15d-9219-461d-e259-b6ec9e7c2a90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The boxplot above is curious: we'd expect diamonds with better clarity to fetch higher prices and yet diamonds on the highest end of the clarity spectrum (IF = internally flawless) actually have lower median prices than low clarity diamonds! What gives? Perhaps another boxplot can shed some light on this situation:"
      ],
      "metadata": {
        "id": "1N-FoBEUrcPq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.boxplot(column=\"carat\",        # Column to plot\n",
        "                 by= \"clarity\",         # Column to split upon\n",
        "                 figsize= (8,8));       # Figure size"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:42:06.782311Z",
          "iopub.execute_input": "2024-08-29T14:42:06.782685Z",
          "iopub.status.idle": "2024-08-29T14:42:07.392999Z",
          "shell.execute_reply.started": "2024-08-29T14:42:06.782621Z",
          "shell.execute_reply": "2024-08-29T14:42:07.392185Z"
        },
        "trusted": true,
        "id": "p8nWhAEjrcPq",
        "outputId": "f52083ed-2412-4a16-af4b-b16145c6f57a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot above shows that diamonds with low clarity ratings also tend to be larger. Since size is an important factor in determining a diamond's value, it isn't too surprising that low clarity diamonds have higher median prices."
      ],
      "metadata": {
        "id": "Cmemie8srcPq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Density Plots"
      ],
      "metadata": {
        "id": "puPcqowArcPq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A density plot shows the distribution of a numeric variable with a continuous curve. It is similar to a histogram but without discrete bins, a density plot gives a better picture of the underlying shape of a distribution. Create a density plot with series.plot(kind=\"density\")"
      ],
      "metadata": {
        "id": "JB8XSVbbrcPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds[\"carat\"].plot(kind=\"density\",  # Create density plot\n",
        "                      figsize=(8,8),    # Set figure size\n",
        "                      xlim= (0,5));     # Limit x axis values"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:46:46.016707Z",
          "iopub.execute_input": "2024-08-29T14:46:46.017122Z",
          "iopub.status.idle": "2024-08-29T14:46:49.434615Z",
          "shell.execute_reply.started": "2024-08-29T14:46:46.017057Z",
          "shell.execute_reply": "2024-08-29T14:46:49.433660Z"
        },
        "trusted": true,
        "id": "lVyjPCiFrcPr",
        "outputId": "b30c725e-9144-4959-c882-3ff166ced2d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Barplots"
      ],
      "metadata": {
        "id": "vAZhXGQYrcPs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Barplots are graphs that visually display counts of categorical variables. We can create a barplot by creating a table of counts for a certain variable using the pd.crosstab() function and then passing the counts to df.plot(kind=\"bar\"):"
      ],
      "metadata": {
        "id": "UfBFvLLbrcPs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clarity_table = pd.crosstab(index=diamonds[\"clarity\"], columns=\"count\")\n",
        "clarity_table"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:52:11.552256Z",
          "iopub.execute_input": "2024-08-29T14:52:11.552631Z",
          "iopub.status.idle": "2024-08-29T14:52:11.595459Z",
          "shell.execute_reply.started": "2024-08-29T14:52:11.552577Z",
          "shell.execute_reply": "2024-08-29T14:52:11.863323Z"
        },
        "trusted": true,
        "id": "4cgYEzPyrcPt",
        "outputId": "584d8dac-2a89-4b6d-be99-5a8d3f292ce6"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 47,
          "output_type": "execute_result",
          "data": {
            "text/plain": "col_0    count\nclarity       \nI1         741\nIF        1790\nSI1      13065\nSI2       9194\nVS1       8171\nVS2      12258\nVVS1      3655\nVVS2      5066",
            "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>col_0</th>\n      <th>count</th>\n    </tr>\n    <tr>\n      <th>clarity</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>I1</th>\n      <td>741</td>\n    </tr>\n    <tr>\n      <th>IF</th>\n      <td>1790</td>\n    </tr>\n    <tr>\n      <th>SI1</th>\n      <td>13065</td>\n    </tr>\n    <tr>\n      <th>SI2</th>\n      <td>9194</td>\n    </tr>\n    <tr>\n      <th>VS1</th>\n      <td>8171</td>\n    </tr>\n    <tr>\n      <th>VS2</th>\n      <td>12258</td>\n    </tr>\n    <tr>\n      <th>VVS1</th>\n      <td>3655</td>\n    </tr>\n    <tr>\n      <th>VVS2</th>\n      <td>5066</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clarity_table.plot(kind=\"bar\",\n",
        "                 figsize=(8,8));"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:51:59.552867Z",
          "iopub.execute_input": "2024-08-29T14:51:59.553257Z",
          "iopub.status.idle": "2024-08-29T14:51:59.869398Z",
          "shell.execute_reply.started": "2024-08-29T14:51:59.553181Z",
          "shell.execute_reply": "2024-08-29T14:51:59.868456Z"
        },
        "trusted": true,
        "id": "Wms2NdOqrcPt",
        "outputId": "35ecd933-ab57-436e-9fdc-cbd93ad2210a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can use a two dimensional table to create a stacked barplot. Stacked barplots show the distribution of a second categorical variable within each bar:"
      ],
      "metadata": {
        "id": "fn9eom5ZrcPt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "carat_table = pd.crosstab(index=diamonds[\"clarity\"],\n",
        "                          columns=diamonds[\"color\"])\n",
        "\n",
        "carat_table"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:52:30.936290Z",
          "iopub.execute_input": "2024-08-29T14:52:30.936671Z",
          "iopub.status.idle": "2024-08-29T14:52:30.985966Z",
          "shell.execute_reply.started": "2024-08-29T14:52:30.936613Z",
          "shell.execute_reply": "2024-08-29T14:52:30.984988Z"
        },
        "trusted": true,
        "id": "aeOuBtVprcPt",
        "outputId": "172a3635-5ae3-4d3b-ddc0-d3a695c72d94"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 48,
          "output_type": "execute_result",
          "data": {
            "text/plain": "color       D     E     F     G     H     I    J\nclarity                                         \nI1         42   102   143   150   162    92   50\nIF         73   158   385   681   299   143   51\nSI1      2083  2426  2131  1976  2275  1424  750\nSI2      1370  1713  1609  1548  1563   912  479\nVS1       705  1281  1364  2148  1169   962  542\nVS2      1697  2470  2201  2347  1643  1169  731\nVVS1      252   656   734   999   585   355   74\nVVS2      553   991   975  1443   608   365  131",
            "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>color</th>\n      <th>D</th>\n      <th>E</th>\n      <th>F</th>\n      <th>G</th>\n      <th>H</th>\n      <th>I</th>\n      <th>J</th>\n    </tr>\n    <tr>\n      <th>clarity</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>I1</th>\n      <td>42</td>\n      <td>102</td>\n      <td>143</td>\n      <td>150</td>\n      <td>162</td>\n      <td>92</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>IF</th>\n      <td>73</td>\n      <td>158</td>\n      <td>385</td>\n      <td>681</td>\n      <td>299</td>\n      <td>143</td>\n      <td>51</td>\n    </tr>\n    <tr>\n      <th>SI1</th>\n      <td>2083</td>\n      <td>2426</td>\n      <td>2131</td>\n      <td>1976</td>\n      <td>2275</td>\n      <td>1424</td>\n      <td>750</td>\n    </tr>\n    <tr>\n      <th>SI2</th>\n      <td>1370</td>\n      <td>1713</td>\n      <td>1609</td>\n      <td>1548</td>\n      <td>1563</td>\n      <td>912</td>\n      <td>479</td>\n    </tr>\n    <tr>\n      <th>VS1</th>\n      <td>705</td>\n      <td>1281</td>\n      <td>1364</td>\n      <td>2148</td>\n      <td>1169</td>\n      <td>962</td>\n      <td>542</td>\n    </tr>\n    <tr>\n      <th>VS2</th>\n      <td>1697</td>\n      <td>2470</td>\n      <td>2201</td>\n      <td>2347</td>\n      <td>1643</td>\n      <td>1169</td>\n      <td>731</td>\n    </tr>\n    <tr>\n      <th>VVS1</th>\n      <td>252</td>\n      <td>656</td>\n      <td>734</td>\n      <td>999</td>\n      <td>585</td>\n      <td>355</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>VVS2</th>\n      <td>553</td>\n      <td>991</td>\n      <td>975</td>\n      <td>1443</td>\n      <td>608</td>\n      <td>365</td>\n      <td>131</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "carat_table.plot(kind=\"bar\",\n",
        "                 figsize=(8,8),\n",
        "                 stacked=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:54:19.039624Z",
          "iopub.execute_input": "2024-08-29T14:54:19.040038Z",
          "iopub.status.idle": "2024-08-29T14:54:19.597040Z",
          "shell.execute_reply.started": "2024-08-29T14:54:19.039979Z",
          "shell.execute_reply": "2024-08-29T14:54:19.596041Z"
        },
        "trusted": true,
        "id": "QQHe7NHCrcPt",
        "outputId": "25e3da87-4ffd-41e2-c58b-200527dc8073"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 51,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f3521af1908>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can as well create subplot for each categorical variable and see if that helps."
      ],
      "metadata": {
        "id": "PbkcA52arcPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "axes = carat_table.plot.bar(rot=1, subplots=True)\n",
        "axes[1].legend(loc=2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:55:54.700291Z",
          "iopub.execute_input": "2024-08-29T14:55:54.700677Z",
          "iopub.status.idle": "2024-08-29T14:55:56.551724Z",
          "shell.execute_reply.started": "2024-08-29T14:55:54.700623Z",
          "shell.execute_reply": "2024-08-29T14:55:56.550407Z"
        },
        "trusted": true,
        "id": "XPveyBaCrcPu",
        "outputId": "1ea54cbe-8450-4e6f-e041-3effe6d54f9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 58,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x7f35087dfa58>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 7 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case, the sub plots were not of great help. But for now, we will keep them."
      ],
      "metadata": {
        "id": "TJw-Sl8krcPu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A grouped barplot is an alternative to a stacked barplot that gives each stacked section its own bar. To make a grouped barplot, do not include the stacked argument (or set stacked=False):"
      ],
      "metadata": {
        "id": "Vpg-r2SFrcPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "carat_table.plot(kind=\"bar\",\n",
        "                 figsize=(8,8),\n",
        "                 stacked=False);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:57:45.092933Z",
          "iopub.execute_input": "2024-08-29T14:57:45.093285Z",
          "iopub.status.idle": "2024-08-29T14:57:45.627736Z",
          "shell.execute_reply.started": "2024-08-29T14:57:45.093235Z",
          "shell.execute_reply": "2024-08-29T14:57:45.626667Z"
        },
        "trusted": true,
        "id": "nCiBHoyFrcPu",
        "outputId": "324db75c-ce45-42f1-898f-c0f5268b076c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scatterplots"
      ],
      "metadata": {
        "id": "Tlo5rbWWrcPu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplots are bivariate (two variable) plots that take two numeric variables and plot data points on the x/y plane. We saw an example of scatterplots in lesson 16 when we created a scatter plot matrix of the mtcars data set. To create a single scatterplot, use df.plot(kind=\"scatter\"):"
      ],
      "metadata": {
        "id": "ta3mGo9orcPv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diamonds.plot(kind=\"scatter\",     # Create a scatterplot\n",
        "              x=\"carat\",          # Put carat on the x axis\n",
        "              y=\"price\",          # Put price on the y axis\n",
        "              figsize=(10,10),\n",
        "              ylim=(0,20000));"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T14:59:26.789746Z",
          "iopub.execute_input": "2024-08-29T14:59:26.790112Z",
          "iopub.status.idle": "2024-08-29T14:59:28.207479Z",
          "shell.execute_reply.started": "2024-08-29T14:59:26.790050Z",
          "shell.execute_reply": "2024-08-29T14:59:28.206509Z"
        },
        "trusted": true,
        "id": "6Ubv1mFWrcPv",
        "outputId": "0c565fdf-591a-4266-8fac-104c1d8d817c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x720 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although the scatterplot above has many overlapping points, it still gives us some insight into the relationship between diamond carat weight and price: bigger diamonds are generally more expensive."
      ],
      "metadata": {
        "id": "EQmMpKFFrcPw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Line Plots"
      ],
      "metadata": {
        "id": "V2dkkVrhrcPw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> <font color='orange'>**So, the line plots are best suited for time series data. Not for unvariate or bivariate plotting**</font>"
      ],
      "metadata": {
        "id": "MFBpNRForcPw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Line plots are charts used to show the change in a numeric variable based on some other ordered variable. Line plots are often used to plot time series data to show the evolution of a variable over time. Line plots are the default plot type when using df.plot() so you don't have to specify the kind argument when making a line plot in pandas. Let's create some fake time series data and plot it with a line plot:"
      ],
      "metadata": {
        "id": "F6qCVzsurcPw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create some data\n",
        "years = [y for y in range(1950,2016)]\n",
        "\n",
        "readings = [(y np.random.uniform(0,20)) for y in years]\n",
        "## np.random.uniform generates uniformally distributed samples within the range mentione\n",
        "\n",
        "time_df = pd.DataFrame({\"year\":years,\n",
        "                        \"readings\":readings})\n",
        "\n",
        "# Plot the data\n",
        "time_df.plot(x=\"year\",\n",
        "             y=\"readings\",\n",
        "             figsize=(9,9));"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T15:05:36.441945Z",
          "iopub.execute_input": "2024-08-29T15:05:36.442343Z",
          "iopub.status.idle": "2024-08-29T15:05:36.952322Z",
          "shell.execute_reply.started": "2024-08-29T15:05:36.442280Z",
          "shell.execute_reply": "2024-08-29T15:05:36.951351Z"
        },
        "trusted": true,
        "id": "FSiFRObYrcPw",
        "outputId": "fda90890-cd10-40df-cfa7-a200811d7592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 648x648 with 1 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving Plots"
      ],
      "metadata": {
        "id": "Gd12WhuqrcPw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you want to save plots for later use, you can export the plot figure (plot information) to a file. First get the plot figure with plot.get_figure() and then save it to a file with figure.savefig(\"filename\"). You can save plots to a variety of common image file formats, such as png, jpeg and pdf."
      ],
      "metadata": {
        "id": "7g6zIqlSrcPx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_plot = time_df.plot(x=\"year\",     # Create the plot and save to a variable\n",
        "             y=\"readings\",\n",
        "             figsize=(9,9))\n",
        "\n",
        "my_fig = my_plot.get_figure()            # Get the figure\n",
        "\n",
        "my_fig.savefig(\"line_plot_example.png\")  # Save to file"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-29T15:06:21.493717Z",
          "iopub.execute_input": "2024-08-29T15:06:21.494035Z",
          "iopub.status.idle": "2024-08-29T15:06:22.139389Z",
          "shell.execute_reply.started": "2024-08-29T15:06:21.493987Z",
          "shell.execute_reply": "2024-08-29T15:06:22.138469Z"
        },
        "trusted": true,
        "id": "eQttvar7rcPx",
        "outputId": "ccb95bbb-87cb-4df1-eaba-6cf6f33d577e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 648x648 with 1 Axes>",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAIaCAYAAAAKv7YjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl0ZHd1L/rvr ZBVapSaWxJ3Wr13HTb7nZ7wlwTxmsIYJKsEBwwXkwmgTzgJjcJuSsPuJC8wErCu9eBkBgCcZgCARIz5RFjTLie3d0e2z2oB6nVak01qSbV/Ht/nHNKJamGU6Om72etXi2dOufUkYCuzf7tvX9CSgkiIiKi9cSw1g9AREREtBIDFCIiIlp3GKAQERHRusMAhYiIiNYdBihERES07jBAISIionWHAQoRERGtOwxQiIiIaN1hgEJERETrDgMUIiIiWndMa/0A1XR3d8uRkZG1fgwiIiJqghMnTvillD3Vzlv3AcrIyAiOHz  1o9BRERETSCEmNBzHpd4iIiIaN1hgEJERETrDgMUIiIiWnfWfQ1KKZlMBleuXEEymVzrR9lQbDYbhoaGYDab1/pRiIiIKtqQAcqVK1fgcrkwMjICIcRaP86GIKVEIBDAlStXsHPnzrV HCIiooo25BJPMpmEz djcFIDIQR8Ph zTkREtCFsyAAFAIOTOvB3RkREG8WGDVA2i/HxcRw6dAgAcPz4cXz4wx9e4yciIiJaexuyBmU9kVJCSgmDofFY79ixYzh27FgTnoqIiGhjYwalDuPj49i3bx/e9a534dChQ/ja176GW265BUePHsVv/uZvIhaLAQA 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Wrap Up"
      ],
      "metadata": {
        "id": "9JufhQoNrcPx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Python plotting ecosystem has many different libraries, which can make it confusing to know what to use and where to start. Pandas plotting functions let you visualize and explore data quickly. Pandas plotting functions don't offer all the features of dedicated plotting package like matplotlib for Python or ggplot2 for R, but they are often enough to get the job done.\n",
        "\n"
      ],
      "metadata": {
        "id": "Nmm94R90rcPx"
      }
    }
  ]
}