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diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "worldwide-blood",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-numbers",
+   "metadata": {},
+   "source": [
+    "*✏️ Write 2-3 sentences describing your research.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greater-circular",
+   "metadata": {},
+   "source": [
+    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-testimony",
+   "metadata": {},
+   "source": [
+    "*✏️ Write 2-3 sentences explaining why this question.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "permanent-pollution",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "technical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "overhead-sigma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_name = \"US_births_2000-2014_SSA.csv\"\n",
+    "dataset_path = \"data/\" + file_name\n",
+    "\n",
+    "df = pd.read_csv(dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "heated-blade",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>date_of_month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month  date_of_month  day_of_week  births\n",
+       "0  2000      1              1            6    9083\n",
+       "1  2000      1              2            7    8006\n",
+       "2  2000      1              3            1   11363\n",
+       "3  2000      1              4            2   13032\n",
+       "4  2000      1              5            3   12558"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continental-franklin",
+   "metadata": {},
+   "source": [
+    "**Data Overview**\n",
+    "\n",
+    "*This data set contains U.S. births data for the years 2000 to 2014, as provided by the Social Security Administration. The columns include the year, month, date of month, day of weeks, and the number of births on each one of these days.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infinite-instrument",
+   "metadata": {},
+   "source": [
+    "# Methods and Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-positive",
+   "metadata": {},
+   "source": [
+    "## First Research Question: On average, are less children born on the 13th of the month?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-palmer",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "endless-variation",
+   "metadata": {},
+   "source": [
+    "In order to research this question, I will use the aspects of date_of_month and births. \n",
+    "\n",
+    "In order to reorganize the data, I will have to create a new chart that will look at the average of each day individually. To do this I will have to regroup the data by the date_of_month and then find the mean for each date.\n",
+    "\n",
+    "I will then create a barplot using this new data to show a visual representation of the average number of births for each numbered day of the month. \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-japan",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "negative-highlight",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date_of_month\n",
+       "1     11131.261111\n",
+       "2     11280.261111\n",
+       "3     11346.894444\n",
+       "4     11137.694444\n",
+       "5     11312.138889\n",
+       "6     11320.716667\n",
+       "7     11463.422222\n",
+       "8     11453.622222\n",
+       "9     11358.888889\n",
+       "10    11478.633333\n",
+       "11    11411.655556\n",
+       "12    11513.794444\n",
+       "13    11111.466667\n",
+       "14    11534.950000\n",
+       "15    11483.327778\n",
+       "16    11436.950000\n",
+       "17    11508.733333\n",
+       "18    11542.627778\n",
+       "19    11474.044444\n",
+       "20    11573.594444\n",
+       "21    11551.100000\n",
+       "22    11394.511111\n",
+       "23    11241.972222\n",
+       "24    11073.350000\n",
+       "25    10958.522222\n",
+       "26    11118.394444\n",
+       "27    11308.238889\n",
+       "28    11397.377778\n",
+       "29    11354.822485\n",
+       "30    11393.484848\n",
+       "31    11074.933333\n",
+       "Name: births, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_births = df.groupby('date_of_month')['births'].mean()\n",
+    "average_births"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "victorian-burning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns \n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "affd0a87-f44b-47d9-829f-7015f815f3ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "average_births_df = average_births.reset_index()\n",
+    "average_births_df.columns = [\"date_of_month\", \"average_births\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7ea67a24-008f-45e6-9082-96a13804b45f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='date_of_month', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=average_births_df, x=\"date_of_month\", weights=\"average_births\", bins=31)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69a032ce-31ec-4613-8f21-d31d52d02fa2",
+   "metadata": {},
+   "source": [
+    "Looking at these results, the average number of births for each date of the month is within 1,000 births of each other, which is not a huge difference, but the 13th of each month is on the lower end. The only 2 dates of the month that has a lower average are the 25th and the 31st. Overall, there are less babies being born on the 13th than the majority of other dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collectible-puppy",
+   "metadata": {},
+   "source": [
+    "## Second Research Question: Of babies being born on the 13th of the month, which day of the week is the least common on average?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demographic-future",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incorporate-roller",
+   "metadata": {},
+   "source": [
+    "I will now use three different columns to organize and show my data: date_of_month, day_of_week, and births. \n",
+    "First, i will have to organize my data by extracting only the data with a date_of_month equal to 13. This will then allow me to look at the average day_of_week for this new data set, which I will then turn into a bar plot in order to easily see which day_of_week is the most and least common. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "juvenile-creation",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "pursuant-surrey",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1       False\n",
+       "2       False\n",
+       "3       False\n",
+       "4       False\n",
+       "        ...  \n",
+       "5474    False\n",
+       "5475    False\n",
+       "5476    False\n",
+       "5477    False\n",
+       "5478    False\n",
+       "Name: date_of_month, Length: 5479, dtype: bool"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.date_of_month==13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "located-night",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>date_of_month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5338</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5369</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>9</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5399</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5430</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>11</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5460</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>180 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      year  month  date_of_month  day_of_week  births\n",
+       "12    2000      1             13            4   11815\n",
+       "43    2000      2             13            7    7933\n",
+       "72    2000      3             13            1   11157\n",
+       "103   2000      4             13            4   11907\n",
+       "133   2000      5             13            6    8747\n",
+       "...    ...    ...            ...          ...     ...\n",
+       "5338  2014      8             13            3   12817\n",
+       "5369  2014      9             13            6    8903\n",
+       "5399  2014     10             13            1   11241\n",
+       "5430  2014     11             13            4   12103\n",
+       "5460  2014     12             13            6    8596\n",
+       "\n",
+       "[180 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "thirteen=df[df.date_of_month==13]\n",
+    "thirteen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4e2a89c4-5686-4865-94e6-ac30f5366e0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "day_of_week\n",
+       "1    11805.541667\n",
+       "2    12865.000000\n",
+       "3    12736.040000\n",
+       "4    12563.714286\n",
+       "5    11949.960000\n",
+       "6     8550.760000\n",
+       "7     7390.296296\n",
+       "Name: births, dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_day = thirteen.groupby('day_of_week')['births'].mean()\n",
+    "average_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "75b165fc-375b-4144-bf86-f85f04653cc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='day_of_week', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=thirteen, x=\"day_of_week\", weights=\"births\", bins=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97dbf4e7-d3d2-4320-be82-0b0df1e47369",
+   "metadata": {},
+   "source": [
+    "Based on the chart and graph I have created here, it is obvious that Friday is not the least common day for children to be born on the 13th. It sits right around the middle which proves that the myth of less children being born on Friday the 13th to be false."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-symbol",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-camping",
+   "metadata": {
+    "code_folding": []
+   },
+   "source": [
+    "## Considerations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-stadium",
+   "metadata": {},
+   "source": [
+    "I think that my results give a fairly accurate depiction of my research questions because it looks at the births in the U.S. over a series of 14 years. I believe this is a large enough sample size to make the assumptions we have made. If we wanted an even better example of this data and to expand upon the research we have a few different options:\n",
+    "1. We could look at the data over a longer period of time to see if that has any impact.\n",
+    "2. We could look at the data in terms of locations to see if the superstition exists more in different areas of the country.\n",
+    "3. We could expand the data to other countries to see if this superstition shows in their data.\n",
+    "\n",
+    "The fact that our data was only taken from the early 2000s in the United States could potentially show a bias. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beneficial-invasion",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "about-raise",
+   "metadata": {},
+   "source": [
+    "I believe that the results did make sense based on my predictions but doesn't follow the traditions of the superstition of Friday the 13th. It is often assumed that mothers avoid having their children on Friday the 13th as it comes with bad luck, but it is often impossible to plan ahead for such specifics when it comes to birth. Our results don't lead us to any conclusions where Friday the 13th is uncommon to the point where it is statistically significant. The date of the month is on the lower end, but only by a few hundred births. The date of the month data was very evenly distributed to where no one date was very different from another. In terms of day of the week, Friday the 13th fell right in the middle, with less births occuring on the weekends. As someone who was born on Friday the 13th, I always assumed I had a rare birth date but going forward I am able to see that it is just the same as any other birth date."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cfbdbeb-48a8-4f15-a1f7-e07e9e7846c4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_json": true,
+   "text_representation": {
+    "extension": ".Rmd",
+    "format_name": "rmarkdown",
+    "format_version": "1.2",
+    "jupytext_version": "1.9.1"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/argument.ipynb b/argument.ipynb
index 4ed27b4..f164070 100644
--- a/argument.ipynb
+++ b/argument.ipynb
@@ -42,26 +42,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "technical-evans",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Include any import statements you will need\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "overhead-sigma",
    "metadata": {},
    "outputs": [],
    "source": [
-    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
-    "\n",
-    "file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
+    "file_name = \"US_births_2000-2014_SSA.csv\"\n",
     "dataset_path = \"data/\" + file_name\n",
     "\n",
     "df = pd.read_csv(dataset_path)"
@@ -69,10 +66,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "heated-blade",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>date_of_month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month  date_of_month  day_of_week  births\n",
+       "0  2000      1              1            6    9083\n",
+       "1  2000      1              2            7    8006\n",
+       "2  2000      1              3            1   11363\n",
+       "3  2000      1              4            2   13032\n",
+       "4  2000      1              5            3   12558"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()"
    ]
@@ -84,7 +168,7 @@
    "source": [
     "**Data Overview**\n",
     "\n",
-    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
+    "*This data set contains U.S. births data for the years 2000 to 2014, as provided by the Social Security Administration. The columns include the year, month, date of month, day of weeks, and the number of births on each one of these days.*"
    ]
   },
   {
@@ -95,22 +179,12 @@
     "# Methods and Results"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "basic-canadian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Import any helper files you need here"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "recognized-positive",
    "metadata": {},
    "source": [
-    "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## First Research Question: On average, are less children born on the 13th of the month?\n"
    ]
   },
   {
@@ -126,12 +200,12 @@
    "id": "endless-variation",
    "metadata": {},
    "source": [
-    "*Explain how you will approach this research question below. Consider the following:* \n",
-    "  - *Which aspects of the dataset will you use?* \n",
-    "  - *How will you reorganize/store the data?* \n",
-    "  - *What data science tools/functions will you use and why?* \n",
-    "  \n",
-    "✏️ *Write your answer below:*\n",
+    "In order to research this question, I will use the aspects of date_of_month and births. \n",
+    "\n",
+    "In order to reorganize the data, I will have to create a new chart that will look at the average of each day individually. To do this I will have to regroup the data by the date_of_month and then find the mean for each date.\n",
+    "\n",
+    "I will then create a barplot using this new data to show a visual representation of the average number of births for each numbered day of the month. \n",
+    "\n",
     "\n"
    ]
   },
@@ -145,26 +219,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 44,
    "id": "negative-highlight",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date_of_month\n",
+       "1     11131.261111\n",
+       "2     11280.261111\n",
+       "3     11346.894444\n",
+       "4     11137.694444\n",
+       "5     11312.138889\n",
+       "6     11320.716667\n",
+       "7     11463.422222\n",
+       "8     11453.622222\n",
+       "9     11358.888889\n",
+       "10    11478.633333\n",
+       "11    11411.655556\n",
+       "12    11513.794444\n",
+       "13    11111.466667\n",
+       "14    11534.950000\n",
+       "15    11483.327778\n",
+       "16    11436.950000\n",
+       "17    11508.733333\n",
+       "18    11542.627778\n",
+       "19    11474.044444\n",
+       "20    11573.594444\n",
+       "21    11551.100000\n",
+       "22    11394.511111\n",
+       "23    11241.972222\n",
+       "24    11073.350000\n",
+       "25    10958.522222\n",
+       "26    11118.394444\n",
+       "27    11308.238889\n",
+       "28    11397.377778\n",
+       "29    11354.822485\n",
+       "30    11393.484848\n",
+       "31    11074.933333\n",
+       "Name: births, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
-    "### \n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "average_births = df.groupby('date_of_month')['births'].mean()\n",
+    "average_births"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 45,
    "id": "victorian-burning",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "import seaborn as sns \n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "affd0a87-f44b-47d9-829f-7015f815f3ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "average_births_df = average_births.reset_index()\n",
+    "average_births_df.columns = [\"date_of_month\", \"average_births\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7ea67a24-008f-45e6-9082-96a13804b45f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='date_of_month', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=average_births_df, x=\"date_of_month\", weights=\"average_births\", bins=31)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69a032ce-31ec-4613-8f21-d31d52d02fa2",
+   "metadata": {},
+   "source": [
+    "Looking at these results, the average number of births for each date of the month is within 1,000 births of each other, which is not a huge difference, but the 13th of each month is on the lower end. The only 2 dates of the month that has a lower average are the 25th and the 31st. Overall, there are less babies being born on the 13th than the majority of other dates"
    ]
   },
   {
@@ -172,7 +337,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: Of babies being born on the 13th of the month, which day of the week is the least common on average?\n"
    ]
   },
   {
@@ -188,12 +353,8 @@
    "id": "incorporate-roller",
    "metadata": {},
    "source": [
-    "*Explain how you will approach this research question below. Consider the following:* \n",
-    "  - *Which aspects of the dataset will you use?* \n",
-    "  - *How will you reorganize/store the data?* \n",
-    "  - *What data science tools/functions will you use and why?* \n",
-    "\n",
-    "✏️ *Write your answer below:*\n"
+    "I will now use three different columns to organize and show my data: date_of_month, day_of_week, and births. \n",
+    "First, i will have to organize my data by extracting only the data with a date_of_month equal to 13. This will then allow me to look at the average day_of_week for this new data set, which I will then turn into a bar plot in order to easily see which day_of_week is the most and least common. "
    ]
   },
   {
@@ -206,26 +367,258 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 42,
    "id": "pursuant-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1       False\n",
+       "2       False\n",
+       "3       False\n",
+       "4       False\n",
+       "        ...  \n",
+       "5474    False\n",
+       "5475    False\n",
+       "5476    False\n",
+       "5477    False\n",
+       "5478    False\n",
+       "Name: date_of_month, Length: 5479, dtype: bool"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "df.date_of_month==13"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 48,
    "id": "located-night",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>date_of_month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5338</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5369</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>9</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5399</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5430</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>11</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5460</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>180 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      year  month  date_of_month  day_of_week  births\n",
+       "12    2000      1             13            4   11815\n",
+       "43    2000      2             13            7    7933\n",
+       "72    2000      3             13            1   11157\n",
+       "103   2000      4             13            4   11907\n",
+       "133   2000      5             13            6    8747\n",
+       "...    ...    ...            ...          ...     ...\n",
+       "5338  2014      8             13            3   12817\n",
+       "5369  2014      9             13            6    8903\n",
+       "5399  2014     10             13            1   11241\n",
+       "5430  2014     11             13            4   12103\n",
+       "5460  2014     12             13            6    8596\n",
+       "\n",
+       "[180 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "thirteen=df[df.date_of_month==13]\n",
+    "thirteen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4e2a89c4-5686-4865-94e6-ac30f5366e0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "day_of_week\n",
+       "1    11805.541667\n",
+       "2    12865.000000\n",
+       "3    12736.040000\n",
+       "4    12563.714286\n",
+       "5    11949.960000\n",
+       "6     8550.760000\n",
+       "7     7390.296296\n",
+       "Name: births, dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_day = thirteen.groupby('day_of_week')['births'].mean()\n",
+    "average_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "75b165fc-375b-4144-bf86-f85f04653cc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='day_of_week', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=thirteen, x=\"day_of_week\", weights=\"births\", bins=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97dbf4e7-d3d2-4320-be82-0b0df1e47369",
+   "metadata": {},
+   "source": [
+    "Based on the chart and graph I have created here, it is obvious that Friday is not the least common day for children to be born on the 13th. It sits right around the middle which proves that the myth of less children being born on Friday the 13th to be false."
    ]
   },
   {
@@ -251,14 +644,12 @@
    "id": "bearing-stadium",
    "metadata": {},
    "source": [
-    "*It's important to recognize the limitations of our research.\n",
-    "Consider the following:*\n",
+    "I think that my results give a fairly accurate depiction of my research questions because it looks at the births in the U.S. over a series of 14 years. I believe this is a large enough sample size to make the assumptions we have made. If we wanted an even better example of this data and to expand upon the research we have a few different options:\n",
+    "1. We could look at the data over a longer period of time to see if that has any impact.\n",
+    "2. We could look at the data in terms of locations to see if the superstition exists more in different areas of the country.\n",
+    "3. We could expand the data to other countries to see if this superstition shows in their data.\n",
     "\n",
-    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
-    "- *What were limitations of your datset?*\n",
-    "- *Are there any known biases in the data?*\n",
-    "\n",
-    "✏️ *Write your answer below:*"
+    "The fact that our data was only taken from the early 2000s in the United States could potentially show a bias. "
    ]
   },
   {
@@ -274,15 +665,16 @@
    "id": "about-raise",
    "metadata": {},
    "source": [
-    "*Summarize what you discovered through the research. Consider the following:*\n",
-    "\n",
-    "- *What did you learn about your media consumption/digital habits?*\n",
-    "- *Did the results make sense?*\n",
-    "- *What was most surprising?*\n",
-    "- *How will this project impact you going forward?*\n",
-    "\n",
-    "✏️ *Write your answer below:*"
+    "I believe that the results did make sense based on my predictions but doesn't follow the traditions of the superstition of Friday the 13th. It is often assumed that mothers avoid having their children on Friday the 13th as it comes with bad luck, but it is often impossible to plan ahead for such specifics when it comes to birth. Our results don't lead us to any conclusions where Friday the 13th is uncommon to the point where it is statistically significant. The date of the month is on the lower end, but only by a few hundred births. The date of the month data was very evenly distributed to where no one date was very different from another. In terms of day of the week, Friday the 13th fell right in the middle, with less births occuring on the weekends. As someone who was born on Friday the 13th, I always assumed I had a rare birth date but going forward I am able to see that it is just the same as any other birth date."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cfbdbeb-48a8-4f15-a1f7-e07e9e7846c4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -310,7 +702,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.5"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/data/US_births_2000-2014_SSA.csv b/data/US_births_2000-2014_SSA.csv
new file mode 100644
index 0000000..161566a
--- /dev/null
+++ b/data/US_births_2000-2014_SSA.csv
@@ -0,0 +1,5480 @@
+year,month,date_of_month,day_of_week,births
+2000,1,1,6,9083
+2000,1,2,7,8006
+2000,1,3,1,11363
+2000,1,4,2,13032
+2000,1,5,3,12558
+2000,1,6,4,12466
+2000,1,7,5,12516
+2000,1,8,6,8934
+2000,1,9,7,7949
+2000,1,10,1,11668
+2000,1,11,2,12611
+2000,1,12,3,12398
+2000,1,13,4,11815
+2000,1,14,5,12180
+2000,1,15,6,8525
+2000,1,16,7,7657
+2000,1,17,1,10824
+2000,1,18,2,12350
+2000,1,19,3,12405
+2000,1,20,4,12506
+2000,1,21,5,11953
+2000,1,22,6,8855
+2000,1,23,7,7856
+2000,1,24,1,11449
+2000,1,25,2,12593
+2000,1,26,3,12143
+2000,1,27,4,12408
+2000,1,28,5,11934
+2000,1,29,6,8805
+2000,1,30,7,7764
+2000,1,31,1,11133
+2000,2,1,2,12614
+2000,2,2,3,12465
+2000,2,3,4,12062
+2000,2,4,5,12035
+2000,2,5,6,8624
+2000,2,6,7,7862
+2000,2,7,1,11630
+2000,2,8,2,12879
+2000,2,9,3,12315
+2000,2,10,4,12612
+2000,2,11,5,12411
+2000,2,12,6,8836
+2000,2,13,7,7933
+2000,2,14,1,12152
+2000,2,15,2,13017
+2000,2,16,3,12504
+2000,2,17,4,12520
+2000,2,18,5,12406
+2000,2,19,6,8861
+2000,2,20,7,7943
+2000,2,21,1,10610
+2000,2,22,2,12664
+2000,2,23,3,12252
+2000,2,24,4,12423
+2000,2,25,5,12204
+2000,2,26,6,9026
+2000,2,27,7,7836
+2000,2,28,1,11455
+2000,2,29,2,11895
+2000,3,1,3,12672
+2000,3,2,4,12524
+2000,3,3,5,12390
+2000,3,4,6,9054
+2000,3,5,7,7734
+2000,3,6,1,11375
+2000,3,7,2,12420
+2000,3,8,3,12338
+2000,3,9,4,12615
+2000,3,10,5,12152
+2000,3,11,6,8822
+2000,3,12,7,7937
+2000,3,13,1,11157
+2000,3,14,2,12773
+2000,3,15,3,12428
+2000,3,16,4,12431
+2000,3,17,5,12385
+2000,3,18,6,8677
+2000,3,19,7,7782
+2000,3,20,1,11595
+2000,3,21,2,12689
+2000,3,22,3,12272
+2000,3,23,4,12396
+2000,3,24,5,12327
+2000,3,25,6,9155
+2000,3,26,7,7815
+2000,3,27,1,11291
+2000,3,28,2,12650
+2000,3,29,3,12077
+2000,3,30,4,12071
+2000,3,31,5,11820
+2000,4,1,6,8388
+2000,4,2,7,7540
+2000,4,3,1,11397
+2000,4,4,2,12797
+2000,4,5,3,12209
+2000,4,6,4,12192
+2000,4,7,5,12213
+2000,4,8,6,8743
+2000,4,9,7,7759
+2000,4,10,1,11333
+2000,4,11,2,12850
+2000,4,12,3,12545
+2000,4,13,4,11907
+2000,4,14,5,12053
+2000,4,15,6,8887
+2000,4,16,7,7859
+2000,4,17,1,11322
+2000,4,18,2,12783
+2000,4,19,3,12562
+2000,4,20,4,12301
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