video_25-How do I work with dates and times in pandas?.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do I work with dates and times in pandas?\n",
    "https://www.youtube.com/watch?v=yCgJGsg0Xa4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ufo = pd.read_csv(\"data/ufo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City               object\n",
       "Colors Reported    object\n",
       "Shape Reported     object\n",
       "State              object\n",
       "Time               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# time string olarak tutulmuş\n",
    "ufo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ufo['Time'] = pd.to_datetime(ufo.Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                       object\n",
       "Colors Reported            object\n",
       "Shape Reported             object\n",
       "State                      object\n",
       "Time               datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datatime a dönüşmüş\n",
    "ufo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Sunday\n",
       "1           Monday\n",
       "2           Sunday\n",
       "3           Monday\n",
       "4          Tuesday\n",
       "5         Saturday\n",
       "6         Saturday\n",
       "7        Wednesday\n",
       "8         Thursday\n",
       "9          Tuesday\n",
       "10          Sunday\n",
       "11        Thursday\n",
       "12          Friday\n",
       "13          Friday\n",
       "14          Sunday\n",
       "15       Wednesday\n",
       "16       Wednesday\n",
       "17          Monday\n",
       "18       Wednesday\n",
       "19          Friday\n",
       "20         Tuesday\n",
       "21          Sunday\n",
       "22          Sunday\n",
       "23          Friday\n",
       "24        Saturday\n",
       "25        Saturday\n",
       "26        Saturday\n",
       "27          Sunday\n",
       "28        Thursday\n",
       "29          Friday\n",
       "           ...    \n",
       "18211     Thursday\n",
       "18212     Thursday\n",
       "18213     Thursday\n",
       "18214       Friday\n",
       "18215       Friday\n",
       "18216       Friday\n",
       "18217       Friday\n",
       "18218       Friday\n",
       "18219       Friday\n",
       "18220       Friday\n",
       "18221       Friday\n",
       "18222       Friday\n",
       "18223       Friday\n",
       "18224       Friday\n",
       "18225     Saturday\n",
       "18226     Saturday\n",
       "18227     Saturday\n",
       "18228       Sunday\n",
       "18229       Sunday\n",
       "18230       Sunday\n",
       "18231       Sunday\n",
       "18232       Sunday\n",
       "18233       Sunday\n",
       "18234       Sunday\n",
       "18235       Sunday\n",
       "18236       Sunday\n",
       "18237       Sunday\n",
       "18238       Sunday\n",
       "18239       Sunday\n",
       "18240       Sunday\n",
       "Name: Time, Length: 18241, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bu sayede zamanın belli bölümlerini kulalnabiliriz.\n",
    "ufo.Time.dt.hour\n",
    "# yada \n",
    "ufo.Time.dt.weekday_name\n",
    "# ya da https://pandas.pydata.org/pandas-docs/stable/api.html#datetimelike-properties "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12832</th>\n",
       "      <td>Loma Rica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>1999-01-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12833</th>\n",
       "      <td>Bauxite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AR</td>\n",
       "      <td>1999-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12834</th>\n",
       "      <td>Florence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>SC</td>\n",
       "      <td>1999-01-01 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12835</th>\n",
       "      <td>Lake Henshaw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>CA</td>\n",
       "      <td>1999-01-01 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12836</th>\n",
       "      <td>Wilmington Island</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>GA</td>\n",
       "      <td>1999-01-01 17:15:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    City Colors Reported Shape Reported State  \\\n",
       "12832          Loma Rica             NaN          LIGHT    CA   \n",
       "12833            Bauxite             NaN            NaN    AR   \n",
       "12834           Florence             NaN       CYLINDER    SC   \n",
       "12835       Lake Henshaw             NaN          CIGAR    CA   \n",
       "12836  Wilmington Island             NaN          LIGHT    GA   \n",
       "\n",
       "                     Time  \n",
       "12832 1999-01-01 02:30:00  \n",
       "12833 1999-01-01 03:00:00  \n",
       "12834 1999-01-01 14:00:00  \n",
       "12835 1999-01-01 15:00:00  \n",
       "12836 1999-01-01 17:15:00  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1999 dan sonrakileri bulmak için \n",
    "ts = pd.to_datetime(\"1/1/1999\")\n",
    "ufo.loc[ufo.Time > ts, :].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25781"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 tarihi birbirinden çıkarırsak timedelta objesi oluşur.\n",
    "(ufo.Time.max()-ufo.Time.min()).days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00</td>\n",
       "      <td>1933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State  \\\n",
       "0                Ithaca             NaN       TRIANGLE    NY   \n",
       "1           Willingboro             NaN          OTHER    NJ   \n",
       "2               Holyoke             NaN           OVAL    CO   \n",
       "3               Abilene             NaN           DISK    KS   \n",
       "4  New York Worlds Fair             NaN          LIGHT    NY   \n",
       "\n",
       "                 Time  Year  \n",
       "0 1930-06-01 22:00:00  1930  \n",
       "1 1930-06-30 20:00:00  1930  \n",
       "2 1931-02-15 14:00:00  1931  \n",
       "3 1931-06-01 13:00:00  1931  \n",
       "4 1933-04-18 19:00:00  1933  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grafik yapacağız\n",
    "ufo['Year'] = ufo.Time.dt.year\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5418ca62d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD8CAYAAABpcuN4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLgWFiIjEpaAQEZG4FBQiIhLX/wcSocfejHKllgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5437fd9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yıllara göre kayıtları görebiliriz.\n",
    "ufo.Year.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}