Joins_and_Bar_Plot_with_Plotly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "471bfd37",
   "metadata": {
    "id": "471bfd37"
   },
   "source": [
    "# Joins and Bar Plot in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8b897d",
   "metadata": {
    "id": "3b8b897d"
   },
   "source": [
    "\n",
    "This notebook demonstrates how to perform various types of joins and create a bar plot using Python.\n",
    "It is a Python adaptation of an R script using libraries such as Pandas and Plotly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "r9HU6TDQpf1I",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "r9HU6TDQpf1I",
    "outputId": "8e0aa0ce-6aba-4580-87f4-42cfd7cd80b7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Country</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>21433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>14343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>5082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>2875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country    GDP\n",
       "0      USA  21433\n",
       "1    China  14343\n",
       "2    Japan   5082\n",
       "3  Germany   3861\n",
       "4    India   2875"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Sample GDP data (in billion USD)\n",
    "gdp_data = pd.DataFrame({\n",
    "    \"Country\": [\"USA\", \"China\", \"Japan\", \"Germany\", \"India\"],\n",
    "    \"GDP\": [21433, 14343, 5082, 3861, 2875]\n",
    "})\n",
    "\n",
    "# Sample Inflation data (in %)\n",
    "inflation_data = pd.DataFrame({\n",
    "    \"Country\": [\"USA\", \"China\", \"Japan\", \"UK\", \"India\"],\n",
    "    \"Inflation\": [1.2, 2.3, 0.5, 1.8, 4.5]\n",
    "})\n",
    "\n",
    "gdp_data\n",
    "#inflation_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb02354",
   "metadata": {
    "id": "aeb02354"
   },
   "source": [
    "## Joins in Detail"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2905b605",
   "metadata": {
    "id": "2905b605"
   },
   "source": [
    "### 1. Inner Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ed9c390",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5ed9c390",
    "outputId": "18cf9d86-842a-4fd5-c1de-73add8e01087"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner Join Result:\n",
      "  Country    GDP  Inflation\n",
      "0     USA  21433        1.2\n",
      "1   China  14343        2.3\n",
      "2   Japan   5082        0.5\n",
      "3   India   2875        4.5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Inner join: Keep only common countries\n",
    "inner_join_result = pd.merge(gdp_data, inflation_data, on='Country', how='inner')\n",
    "print(\"Inner Join Result:\")\n",
    "print(inner_join_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c07033",
   "metadata": {
    "id": "00c07033"
   },
   "source": [
    "### 2. Left Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb6df6df",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bb6df6df",
    "outputId": "079ce5ee-230f-4329-f9aa-b1db8a1c6119"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left Join Result:\n",
      "   Country    GDP  Inflation\n",
      "0      USA  21433        1.2\n",
      "1    China  14343        2.3\n",
      "2    Japan   5082        0.5\n",
      "3  Germany   3861        NaN\n",
      "4    India   2875        4.5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Left join: Keep all rows from gdp_data\n",
    "left_join_result = pd.merge(gdp_data, inflation_data, on='Country', how='left')\n",
    "print(\"Left Join Result:\")\n",
    "print(left_join_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e64ca72",
   "metadata": {
    "id": "4e64ca72"
   },
   "source": [
    "### 3. Right Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "225fa1d4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "225fa1d4",
    "outputId": "1793f9b6-60b8-4ec0-af3b-14ad287f975c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right Join Result:\n",
      "  Country      GDP  Inflation\n",
      "0     USA  21433.0        1.2\n",
      "1   China  14343.0        2.3\n",
      "2   Japan   5082.0        0.5\n",
      "3      UK      NaN        1.8\n",
      "4   India   2875.0        4.5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Right join: Keep all rows from inflation_data\n",
    "right_join_result = pd.merge(gdp_data, inflation_data, on='Country', how='right')\n",
    "print(\"Right Join Result:\")\n",
    "print(right_join_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2498774",
   "metadata": {
    "id": "b2498774"
   },
   "source": [
    "### 4. Full Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8cc44325",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8cc44325",
    "outputId": "23b9f70e-6b1a-4c4b-8b15-ac1f3d3b33a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full Join Result:\n",
      "   Country      GDP  Inflation\n",
      "0    China  14343.0        2.3\n",
      "1  Germany   3861.0        NaN\n",
      "2    India   2875.0        4.5\n",
      "3    Japan   5082.0        0.5\n",
      "4       UK      NaN        1.8\n",
      "5      USA  21433.0        1.2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Full join: Keep all rows from both datasets\n",
    "full_join_result = pd.merge(gdp_data, inflation_data, on='Country', how='outer')\n",
    "print(\"Full Join Result:\")\n",
    "print(full_join_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a695f116",
   "metadata": {
    "id": "a695f116"
   },
   "source": [
    "## Bar Plot: GDP and Inflation by Country\n",
    "\n",
    "### Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "676872b8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "676872b8",
    "outputId": "909282bf-ae31-45d9-d5a9-87b07bdb815b"
   },
   "outputs": [
    {
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       "    }\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>Country</th>\n",
       "      <th>Variable</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>GDP</td>\n",
       "      <td>14343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>GDP</td>\n",
       "      <td>3861.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>GDP</td>\n",
       "      <td>2875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Japan</td>\n",
       "      <td>GDP</td>\n",
       "      <td>5082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UK</td>\n",
       "      <td>GDP</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USA</td>\n",
       "      <td>GDP</td>\n",
       "      <td>21433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>China</td>\n",
       "      <td>Inflation</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Inflation</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>Inflation</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Inflation</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UK</td>\n",
       "      <td>Inflation</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>USA</td>\n",
       "      <td>Inflation</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country   Variable    Value\n",
       "0     China        GDP  14343.0\n",
       "1   Germany        GDP   3861.0\n",
       "2     India        GDP   2875.0\n",
       "3     Japan        GDP   5082.0\n",
       "4        UK        GDP      NaN\n",
       "5       USA        GDP  21433.0\n",
       "6     China  Inflation      2.3\n",
       "7   Germany  Inflation      NaN\n",
       "8     India  Inflation      4.5\n",
       "9     Japan  Inflation      0.5\n",
       "10       UK  Inflation      1.8\n",
       "11      USA  Inflation      1.2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Full join to merge datasets\n",
    "merged_data = pd.merge(gdp_data, inflation_data, on='Country', how='outer')\n",
    "\n",
    "# Reshape data to long format\n",
    "long_data = merged_data.melt(id_vars=['Country'], var_name='Variable', value_name='Value')\n",
    "\n",
    "long_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59682fdc",
   "metadata": {},
   "source": [
    "### Option 1: seaborn (sns) and matplotlib (plt) packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f895519",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6c/wrrn1gpx26q5r1jr0h59sb5h0000gn/T/ipykernel_3452/3639896152.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=merged_data, x='Country', y='GDP', palette=\"Blues_d\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6c/wrrn1gpx26q5r1jr0h59sb5h0000gn/T/ipykernel_3452/3639896152.py:13: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=merged_data, x='Country', y='Inflation', palette=\"Greens_d\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for GDP\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(data=merged_data, x='Country', y='GDP', palette=\"Blues_d\")\n",
    "plt.title('GDP by Country')\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('GDP (billion USD)')\n",
    "plt.xticks(rotation=0)   # Rotate x-labels by 0 degrees. Change for long x-labels\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot for Inflation\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(data=merged_data, x='Country', y='Inflation', palette=\"Greens_d\")\n",
    "plt.title('Inflation by Country')\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Inflation (%)')\n",
    "plt.xticks(rotation=0) # Rotate x-labels by 0 degrees. Change for long x-labels\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c01e13",
   "metadata": {},
   "source": [
    "### Option 2: Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "741a84e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Variable=GDP<br>Country=%{x}<br>Value=%{y}<extra></extra>",
         "legendgroup": "GDP",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "GDP",
         "offsetgroup": "GDP",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "China",
          "Germany",
          "India",
          "Japan",
          "UK",
          "USA"
         ],
         "xaxis": "x",
         "y": [
          14343,
          3861,
          2875,
          5082,
          null,
          21433
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Variable=Inflation<br>Country=%{x}<br>Value=%{y}<extra></extra>",
         "legendgroup": "Inflation",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Inflation",
         "offsetgroup": "Inflation",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "China",
          "Germany",
          "India",
          "Japan",
          "UK",
          "USA"
         ],
         "xaxis": "x2",
         "y": [
          2.3,
          null,
          4.5,
          0.5,
          1.8,
          1.2
         ],
         "yaxis": "y2"
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {},
          "showarrow": false,
          "text": "GDP in billion USD, Inflation in %",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": -0.25,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {},
          "showarrow": false,
          "text": "GDP in billion USD, Inflation in %",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": -0.25,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "barmode": "relative",
        "height": 400,
        "legend": {
         "title": {
          "text": "Variable"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
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   "source": [
    "# Create a faceted bar plot\n",
    "fig = px.bar(long_data, x='Country', y='Value',\n",
    "    color='Variable',\n",
    "    facet_col='Variable',\n",
    "    title='GDP and Inflation by Country',\n",
    "    labels={'Value': 'Value', 'Country': 'Country'},\n",
    "    height=400\n",
    ")\n",
    "\n",
    "# Ensure separate scales per facet\n",
    "fig.update_yaxes(matches=None)\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(\n",
    "    annotations=[\n",
    "        dict(\n",
    "            text='GDP in billion USD, Inflation in %',\n",
    "            xref='paper', yref='paper',\n",
    "            x=0.5, y=-0.25,\n",
    "            showarrow=False\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "fig.show()\n"
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