{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Introduction To The Dataset"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"csv_list = open(\"US_births_1994-2003_CDC_NCHS.csv\").read().split(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['year,month,date_of_month,day_of_week,births',\n",
" '1994,1,1,6,8096',\n",
" '1994,1,2,7,7772',\n",
" '1994,1,3,1,10142',\n",
" '1994,1,4,2,11248',\n",
" '1994,1,5,3,11053',\n",
" '1994,1,6,4,11406',\n",
" '1994,1,7,5,11251',\n",
" '1994,1,8,6,8653',\n",
" '1994,1,9,7,7910']"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"csv_list[0:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Converting Data Into A List Of Lists"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read_csv(filename):\n",
" string_data = open(filename).read()\n",
" string_list = string_data.split(\"\\n\")[1:]\n",
" final_list = []\n",
" \n",
" for row in string_list:\n",
" string_fields = row.split(\",\")\n",
" int_fields = []\n",
" for value in string_fields:\n",
" int_fields.append(int(value))\n",
" final_list.append(int_fields)\n",
" return final_list\n",
" \n",
"cdc_list = read_csv(\"US_births_1994-2003_CDC_NCHS.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[1994, 1, 1, 6, 8096],\n",
" [1994, 1, 2, 7, 7772],\n",
" [1994, 1, 3, 1, 10142],\n",
" [1994, 1, 4, 2, 11248],\n",
" [1994, 1, 5, 3, 11053],\n",
" [1994, 1, 6, 4, 11406],\n",
" [1994, 1, 7, 5, 11251],\n",
" [1994, 1, 8, 6, 8653],\n",
" [1994, 1, 9, 7, 7910],\n",
" [1994, 1, 10, 1, 10498]]"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_list[0:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating Number Of Births Each Month"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read_csv(filename):\n",
" string_data = open(filename).read()\n",
" string_list = string_data.split(\"\\n\")[1:]\n",
" final_list = []\n",
" \n",
" for row in string_list:\n",
" string_fields = row.split(\",\")\n",
" int_fields = []\n",
" for value in string_fields:\n",
" int_fields.append(int(value))\n",
" final_list.append(int_fields)\n",
" return final_list\n",
" \n",
"cdc_list = read_csv(\"US_births_1994-2003_CDC_NCHS.csv\")\n",
"\n",
"\n",
"def month_births(data):\n",
" births_per_month = {}\n",
" \n",
" for row in data:\n",
" month = row[1]\n",
" births = row[4]\n",
" if month in births_per_month:\n",
" births_per_month[month] = births_per_month[month] + births\n",
" else:\n",
" births_per_month[month] = births\n",
" return births_per_month\n",
" \n",
"cdc_month_births = month_births(cdc_list)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 3232517,\n",
" 2: 3018140,\n",
" 3: 3322069,\n",
" 4: 3185314,\n",
" 5: 3350907,\n",
" 6: 3296530,\n",
" 7: 3498783,\n",
" 8: 3525858,\n",
" 9: 3439698,\n",
" 10: 3378814,\n",
" 11: 3171647,\n",
" 12: 3301860}"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_month_births"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating Number Of Births Each Day Of Week"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def dow_births(data):\n",
" births_per_dow = {}\n",
" \n",
" for row in data:\n",
" dow = row[3]\n",
" births = row[4]\n",
" if dow in births_per_dow:\n",
" births_per_dow[dow] = births_per_dow[dow] + births\n",
" else:\n",
" births_per_dow[dow] = births\n",
" return births_per_dow\n",
" \n",
"cdc_dow_births = dow_births(cdc_list)"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 5789166,\n",
" 2: 6446196,\n",
" 3: 6322855,\n",
" 4: 6288429,\n",
" 5: 6233657,\n",
" 6: 4562111,\n",
" 7: 4079723}"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_dow_births"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating A More General Function"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def calc_counts(data, column):\n",
" sums_dict = {}\n",
" \n",
" for row in data:\n",
" col_value = row[column]\n",
" births = row[4]\n",
" if col_value in sums_dict:\n",
" sums_dict[col_value] = sums_dict[col_value] + births\n",
" else:\n",
" sums_dict[col_value] = births\n",
" return sums_dict\n",
"\n",
"cdc_year_births = calc_counts(cdc_list, 0)\n",
"cdc_month_births = calc_counts(cdc_list, 1)\n",
"cdc_dom_births = calc_counts(cdc_list, 2)\n",
"cdc_dow_births = calc_counts(cdc_list, 3)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1994: 3952767,\n",
" 1995: 3899589,\n",
" 1996: 3891494,\n",
" 1997: 3880894,\n",
" 1998: 3941553,\n",
" 1999: 3959417,\n",
" 2000: 4058814,\n",
" 2001: 4025933,\n",
" 2002: 4021726,\n",
" 2003: 4089950}"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_year_births"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 3232517,\n",
" 2: 3018140,\n",
" 3: 3322069,\n",
" 4: 3185314,\n",
" 5: 3350907,\n",
" 6: 3296530,\n",
" 7: 3498783,\n",
" 8: 3525858,\n",
" 9: 3439698,\n",
" 10: 3378814,\n",
" 11: 3171647,\n",
" 12: 3301860}"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_month_births"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 1276557,\n",
" 2: 1288739,\n",
" 3: 1304499,\n",
" 4: 1288154,\n",
" 5: 1299953,\n",
" 6: 1304474,\n",
" 7: 1310459,\n",
" 8: 1312297,\n",
" 9: 1303292,\n",
" 10: 1320764,\n",
" 11: 1314361,\n",
" 12: 1318437,\n",
" 13: 1277684,\n",
" 14: 1320153,\n",
" 15: 1319171,\n",
" 16: 1315192,\n",
" 17: 1324953,\n",
" 18: 1326855,\n",
" 19: 1318727,\n",
" 20: 1324821,\n",
" 21: 1322897,\n",
" 22: 1317381,\n",
" 23: 1293290,\n",
" 24: 1288083,\n",
" 25: 1272116,\n",
" 26: 1284796,\n",
" 27: 1294395,\n",
" 28: 1307685,\n",
" 29: 1223161,\n",
" 30: 1202095,\n",
" 31: 746696}"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_dom_births"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 5789166,\n",
" 2: 6446196,\n",
" 3: 6322855,\n",
" 4: 6288429,\n",
" 5: 6233657,\n",
" 6: 4562111,\n",
" 7: 4079723}"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdc_dow_births"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:envdq]",
"language": "python",
"name": "conda-env-envdq-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}