03usstorms.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US Tropical Cyclone data\n",
    "---\n",
    "Visualization of US storm data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import plotly.express as px\n",
    "import os\n",
    "\n",
    "df = pd.read_excel('data/cleanusstorms.xlsx')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year                          0\n",
      "Disaster Group                0\n",
      "Disaster Subgroup             0\n",
      "Disaster Type                 0\n",
      "Disaster Subtype              0\n",
      "Disaster Subsubtype         132\n",
      "Event Name                   27\n",
      "Country                       0\n",
      "ISO                           0\n",
      "Region                        0\n",
      "Continent                     0\n",
      "Location                      4\n",
      "Dis Mag Value                58\n",
      "Dis Mag Scale                 0\n",
      "Start Year                    0\n",
      "Start Month                  12\n",
      "Start Day                    26\n",
      "End Year                      0\n",
      "End Month                    12\n",
      "End Day                      26\n",
      "Total Deaths                 13\n",
      "Total Damages ('000 US$)     31\n",
      "CPI                           3\n",
      "State                        12\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year Disaster Group Disaster Subgroup Disaster Type  Disaster Subtype  \\\n",
      "0  1900        Natural    Meteorological         Storm  Tropical cyclone   \n",
      "1  1906        Natural    Meteorological         Storm  Tropical cyclone   \n",
      "2  1906        Natural    Meteorological         Storm  Tropical cyclone   \n",
      "3  1909        Natural    Meteorological         Storm  Tropical cyclone   \n",
      "4  1909        Natural    Meteorological         Storm  Tropical cyclone   \n",
      "\n",
      "   Disaster Subsubtype Event Name                         Country  ISO  \\\n",
      "0                  NaN        NaN  United States of America (the)  USA   \n",
      "1                  NaN        NaN  United States of America (the)  USA   \n",
      "2                  NaN        NaN  United States of America (the)  USA   \n",
      "3                  NaN        NaN  United States of America (the)  USA   \n",
      "4                  NaN        NaN  United States of America (the)  USA   \n",
      "\n",
      "       Region  ... Start Year Start Month  Start Day End Year  End Month  \\\n",
      "0  Gulf Coast  ...       1900         9.0        8.0     1900        9.0   \n",
      "1  Gulf Coast  ...       1906         NaN        NaN     1906        NaN   \n",
      "2  Gulf Coast  ...       1906         NaN        NaN     1906        NaN   \n",
      "3  Gulf Coast  ...       1909         NaN        NaN     1909        NaN   \n",
      "4  Gulf Coast  ...       1909         NaN        NaN     1909        NaN   \n",
      "\n",
      "   End Day  Total Deaths  Total Damages ('000 US$)       CPI       State  \n",
      "0      8.0        6000.0                   30000.0  3.221647          TX  \n",
      "1      NaN         164.0                       NaN  3.479379          FL  \n",
      "2      NaN         134.0                       NaN  3.479379  AL, FL, MS  \n",
      "3      NaN         350.0                       NaN  3.479379          LA  \n",
      "4      NaN          41.0                       NaN  3.479379          TX  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This df only looks at US Tropical cyclones identified in the last step.  Dropping columns that are not needed for this analysis.\n",
    "#df.drop(['Disaster Group', 'Disaster Subgroup', 'Disaster Subtype', 'Country', 'Continent', 'ISO'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year                          int64\n",
      "Disaster Group               object\n",
      "Disaster Subgroup            object\n",
      "Disaster Type                object\n",
      "Disaster Subtype             object\n",
      "Disaster Subsubtype         float64\n",
      "Event Name                   object\n",
      "Country                      object\n",
      "ISO                          object\n",
      "Region                       object\n",
      "Continent                    object\n",
      "Location                     object\n",
      "Dis Mag Value               float64\n",
      "Dis Mag Scale                object\n",
      "Start Year                    int64\n",
      "Start Month                 float64\n",
      "Start Day                   float64\n",
      "End Year                      int64\n",
      "End Month                   float64\n",
      "End Day                     float64\n",
      "Total Deaths                float64\n",
      "Total Damages ('000 US$)    float64\n",
      "CPI                         float64\n",
      "State                        object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "              Year  Disaster Subsubtype  Dis Mag Value   Start Year  \\\n",
      "count   132.000000                  0.0      74.000000   132.000000   \n",
      "mean   1986.431818                  NaN    5649.527027  1986.431818   \n",
      "std      31.595534                  NaN   31682.088392    31.595534   \n",
      "min    1900.000000                  NaN      65.000000  1900.000000   \n",
      "25%    1971.500000                  NaN     150.000000  1971.500000   \n",
      "50%    1995.500000                  NaN     200.000000  1995.500000   \n",
      "75%    2009.000000                  NaN     249.000000  2009.000000   \n",
      "max    2021.000000                  NaN  252912.000000  2021.000000   \n",
      "\n",
      "       Start Month   Start Day     End Year   End Month     End Day  \\\n",
      "count   120.000000  106.000000   132.000000  120.000000  106.000000   \n",
      "mean      8.358333   16.396226  1986.431818    8.433333   16.849057   \n",
      "std       1.543737    9.149217    31.595534    1.543397    8.925931   \n",
      "min       1.000000    1.000000  1900.000000    1.000000    1.000000   \n",
      "25%       8.000000    8.000000  1971.500000    8.000000    9.000000   \n",
      "50%       9.000000   16.500000  1995.500000    9.000000   17.000000   \n",
      "75%       9.000000   25.000000  2009.000000    9.000000   25.000000   \n",
      "max      11.000000   31.000000  2021.000000   11.000000   30.000000   \n",
      "\n",
      "       Total Deaths  Total Damages ('000 US$)         CPI  \n",
      "count    119.000000              1.010000e+02  129.000000  \n",
      "mean     140.100840              6.415899e+06   53.800583  \n",
      "std      599.045914              1.742068e+07   33.028822  \n",
      "min        1.000000              6.000000e+03    3.221647  \n",
      "25%        5.000000              2.300000e+05   15.001282  \n",
      "50%       16.000000              9.000000e+05   58.878183  \n",
      "75%       51.000000              3.500000e+06   83.189023  \n",
      "max     6000.000000              1.250000e+08  100.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
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           "line": {
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           "aaxis": {
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            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
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          },
          "yaxis": {
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           "ticks": "",
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            "standoff": 15
           },
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        },
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        },
        "xaxis": {
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          0,
          1
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         }
        },
        "yaxis": {
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "storms_per_year = df.groupby('Year')['Event Name'].count()\n",
    "\n",
    "fig = px.line(storms_per_year, labels={'value': 'Number of Cyclones', 'Year': 'Year'}, title='US Tropical Cyclones Frequency Over Time')\n",
    "fig.update_xaxes(range=[1947, 2025])\n",
    "fig.update_traces(mode='lines+markers')\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Region\n",
      "Gulf Coast    90\n",
      "East Coast    26\n",
      "Other         13\n",
      "Island         2\n",
      "West Coast     1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Region'].value_counts())"
   ]
  },
  {
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           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Frequency of Tropical Cyclones by Region"
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        "xaxis": {
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         "domain": [
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          "text": "Region"
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          "text": "Number of Events"
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   ],
   "source": [
    "# Frequency of Events by Region\n",
    "events_by_region = df.groupby('Region')['Event Name'].nunique().reset_index(name='Number of Events')\n",
    "\n",
    "# Visualization\n",
    "fig = px.bar(events_by_region, x='Region', y='Number of Events', title='Frequency of Tropical Cyclones by Region')\n",
    "fig.show()\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_year_cpi = df['CPI'].max()\n",
    "\n",
    "# Adjust damages to the base year's CPI value\n",
    "df['Adjusted Damages'] = df.apply(lambda row: (row['Total Damages (\\'000 US$)'] * base_year_cpi) / row['CPI'] if pd.notnull(row['Total Damages (\\'000 US$)']) else None, axis=1)\n",
    "\n",
    "# Total Adjusted Damages by Region\n",
    "annual_damages_by_region = df.groupby(['Start Year', 'Region'])['Adjusted Damages'].sum().reset_index()\n"
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  },
  {
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Annual Damages by Region (Adjusted for Inflation)"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "range": [
          1947,
          2025
         ],
         "title": {
          "text": "Year"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Adjusted Damages ('000 US$)"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(annual_damages_by_region, x='Start Year', y='Adjusted Damages', color='Region',\n",
    "              title='Annual Damages by Region (Adjusted for Inflation)',\n",
    "              labels={'Adjusted Damages': 'Adjusted Damages (\\'000 US$)', 'Start Year': 'Year'})\n",
    "fig.update_xaxes(range=[1947, 2025])\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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