delo (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "mediterranean-protest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Linux; U; Android 4.1.2; en-us; HTC_PN071 Build\\\\/JZO54K) AppleWebKit\\\\/534.30 (KHTML, like Gecko) Version\\\\/4.0 Mobile Safari\\\\/534.30\", \"c\": \"US\", \"nk\": 0, \"tz\": \"America\\\\/Los_Angeles\", \"gr\": \"CA\", \"g\": \"15r91\", \"h\": \"10OBm3W\", \"l\": \"pontifier\", \"al\": \"en-US\", \"hh\": \"j.mp\", \"r\": \"direct\", \"u\": \"http:\\\\/\\\\/www.nsa.gov\\\\/\", \"t\": 1368832205, \"hc\": 1365701422, \"cy\": \"Anaheim\", \"ll\": [ 33.816101, -117.979401 ] }\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'usag.txt'\n",
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sweet-kelly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Linux; U; Android 4.1.2; en-us; HTC_PN071 Build/JZO54K) AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 Mobile Safari/534.30',\n",
       " 'c': 'US',\n",
       " 'nk': 0,\n",
       " 'tz': 'America/Los_Angeles',\n",
       " 'gr': 'CA',\n",
       " 'g': '15r91',\n",
       " 'h': '10OBm3W',\n",
       " 'l': 'pontifier',\n",
       " 'al': 'en-US',\n",
       " 'hh': 'j.mp',\n",
       " 'r': 'direct',\n",
       " 'u': 'http://www.nsa.gov/',\n",
       " 't': 1368832205,\n",
       " 'hc': 1365701422,\n",
       " 'cy': 'Anaheim',\n",
       " 'll': [33.816101, -117.979401]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "records = [json.loads(line) for line in open(path)]\n",
    "records[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "novel-dubai",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "America/Los_Angeles\n"
     ]
    }
   ],
   "source": [
    "print (records[0]['tz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "organizational-overall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/Los_Angeles',\n",
       " '',\n",
       " 'America/Phoenix',\n",
       " 'America/Chicago',\n",
       " '',\n",
       " 'America/Indianapolis',\n",
       " 'America/Chicago',\n",
       " '',\n",
       " 'Australia/NSW',\n",
       " '']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#time_zones = [rec['tz'] for rec in records] NOT ALL RECORDS HAVE TZ FIELD!\n",
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec] #ADD IF\n",
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "postal-numbers",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python function\n",
    "def get_counts(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] += 1\n",
    "        else:\n",
    "            counts[x] =1\n",
    "    return counts\n",
    "\n",
    "#pass time_zones list to function\n",
    "counts = get_counts(time_zones)\n",
    "counts['America/Los_Angeles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "published-barrel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3839"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sporting-filing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(40, 'America/Phoenix'),\n",
       " (50, 'America/Indianapolis'),\n",
       " (85, 'Europe/London'),\n",
       " (89, 'America/Denver'),\n",
       " (102, 'Asia/Tokyo'),\n",
       " (184, 'America/Puerto_Rico'),\n",
       " (421, 'America/Los_Angeles'),\n",
       " (636, ''),\n",
       " (686, 'America/Chicago'),\n",
       " (903, 'America/New_York')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dictionary for top counts\n",
    "def top_counts(count_dict, n=10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]\n",
    "\n",
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "worst-kitty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York        903\n",
       "America/Chicago         686\n",
       "Unknown                 636\n",
       "America/Los_Angeles     421\n",
       "America/Puerto_Rico     184\n",
       "Missing                 120\n",
       "Asia/Tokyo              102\n",
       "America/Denver           89\n",
       "Europe/London            85\n",
       "America/Indianapolis     50\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame, Series\n",
    "import pandas as pd\n",
    "frame = DataFrame(records)\n",
    "\n",
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]\n",
    "\n",
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "superb-triple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tz_counts[:10].plot(kind='barh', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hazardous-mistake",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}