diff --git a/Jupyter Notebook/Distribution_of_Head_Circumference.ipynb b/Jupyter Notebook/Distribution_of_Head_Circumference.ipynb
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@@ -0,0 +1,547 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df =pd.read_csv('training_set_pixel_size_and_HC.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "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>filename</th>\n",
+ " <th>pixel size(mm)</th>\n",
+ " <th>head circumference (mm)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>000_HC.png</td>\n",
+ " <td>0.069136</td>\n",
+ " <td>44.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>001_HC.png</td>\n",
+ " <td>0.089659</td>\n",
+ " <td>56.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>002_HC.png</td>\n",
+ " <td>0.062033</td>\n",
+ " <td>68.75</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>003_HC.png</td>\n",
+ " <td>0.091291</td>\n",
+ " <td>69.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>004_HC.png</td>\n",
+ " <td>0.061240</td>\n",
+ " <td>59.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>994</th>\n",
+ " <td>801_HC.png</td>\n",
+ " <td>0.296851</td>\n",
+ " <td>329.26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>995</th>\n",
+ " <td>802_HC.png</td>\n",
+ " <td>0.212267</td>\n",
+ " <td>330.90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>996</th>\n",
+ " <td>803_HC.png</td>\n",
+ " <td>0.202743</td>\n",
+ " <td>331.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>997</th>\n",
+ " <td>804_HC.png</td>\n",
+ " <td>0.281090</td>\n",
+ " <td>316.48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>998</th>\n",
+ " <td>805_HC.png</td>\n",
+ " <td>0.257475</td>\n",
+ " <td>330.70</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>999 rows × 3 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " filename pixel size(mm) head circumference (mm)\n",
+ "0 000_HC.png 0.069136 44.30\n",
+ "1 001_HC.png 0.089659 56.81\n",
+ "2 002_HC.png 0.062033 68.75\n",
+ "3 003_HC.png 0.091291 69.00\n",
+ "4 004_HC.png 0.061240 59.81\n",
+ ".. ... ... ...\n",
+ "994 801_HC.png 0.296851 329.26\n",
+ "995 802_HC.png 0.212267 330.90\n",
+ "996 803_HC.png 0.202743 331.70\n",
+ "997 804_HC.png 0.281090 316.48\n",
+ "998 805_HC.png 0.257475 330.70\n",
+ "\n",
+ "[999 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# df[(df['category_']<300) & (df['category']>=280)] = 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "10 - 50 - 1\n",
+ "50 - 60 - 4\n",
+ "60 - 65 - 9 0\n",
+ "65 - 70 - 28\n",
+ "70 - 75 - 30\n",
+ "----------------------------\n",
+ "75 - 80 - 47\n",
+ "80 - 85 - 26\n",
+ "85 - 90 - 9 1\n",
+ "90 - 95 - 9\n",
+ "95 - 100 - 20\n",
+ "---------------------------\n",
+ "100 - 150 - 61 2\n",
+ "---------------------------\n",
+ "150 - 200 - 538 \n",
+ "\n",
+ "150 - 160 3\n",
+ "160 - 170 4\n",
+ "170 - 180 5\n",
+ "180 - 190 6\n",
+ "190 - 200 7\n",
+ "---------------------------\n",
+ "200 - 250 8\n",
+ "---------------------------\n",
+ "250 - 280 9\n",
+ "---------------------------\n",
+ "280 - 300 10\n",
+ "---------------------------\n",
+ "300 - 350 11\n",
+ "---------------------------\n",
+ "###350 is last"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['category'] = df['head circumference (mm)']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['cat_int'] = df['category'].astype('int')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['categorical_con'] = df['cat_int'].astype('category')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 0\n",
+ "1 0\n",
+ "2 0\n",
+ "3 0\n",
+ "4 0\n",
+ " ..\n",
+ "994 11\n",
+ "995 11\n",
+ "996 11\n",
+ "997 11\n",
+ "998 11\n",
+ "Name: categorical_con, Length: 999, dtype: category\n",
+ "Categories (28, int64): [0, 1, 2, 3, ..., 294, 296, 298, 299]"
+ ]
+ },
+ "execution_count": 110,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['categorical_con']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv(r'updated.csv', index = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "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>filename</th>\n",
+ " <th>pixel size(mm)</th>\n",
+ " <th>head circumference (mm)</th>\n",
+ " <th>category</th>\n",
+ " <th>cat_int</th>\n",
+ " <th>categorical_con</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
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+ " <th>2</th>\n",
+ " <td>0</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>994</th>\n",
+ " <td>11</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>995</th>\n",
+ " <td>11</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>996</th>\n",
+ " <td>11</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>997</th>\n",
+ " <td>11</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>998</th>\n",
+ " <td>11</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>11</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>999 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " filename pixel size(mm) head circumference (mm) category cat_int \\\n",
+ "0 0 0.0 0.0 0.0 0 \n",
+ "1 0 0.0 0.0 0.0 0 \n",
+ "2 0 0.0 0.0 0.0 0 \n",
+ "3 0 0.0 0.0 0.0 0 \n",
+ "4 0 0.0 0.0 0.0 0 \n",
+ ".. ... ... ... ... ... \n",
+ "994 11 11.0 11.0 11.0 11 \n",
+ "995 11 11.0 11.0 11.0 11 \n",
+ "996 11 11.0 11.0 11.0 11 \n",
+ "997 11 11.0 11.0 11.0 11 \n",
+ "998 11 11.0 11.0 11.0 11 \n",
+ "\n",
+ " categorical_con \n",
+ "0 0 \n",
+ "1 0 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 \n",
+ ".. ... \n",
+ "994 11 \n",
+ "995 11 \n",
+ "996 11 \n",
+ "997 11 \n",
+ "998 11 \n",
+ "\n",
+ "[999 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 113,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
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+ "source": []
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+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
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+ "cell_type": "code",
+ "execution_count": null,
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+ "cell_type": "code",
+ "execution_count": null,
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+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}