lab01/TSA-Ex1-task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8bb7568",
   "metadata": {},
   "source": [
    "# Ex 1\n",
    "\n",
    "13.01.24\n",
    "\n",
    "1.) **Load a time-series data and peek at your time series data and query it by date-time. Also review summary statistics on time series data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "54af92d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "series = read_csv('daily-total-female-births.csv', header=0, parse_dates=[0], index_col=0, squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53b42890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(series))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5926199a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "1959-01-01    35\n",
      "1959-01-02    32\n",
      "1959-01-03    30\n",
      "1959-01-04    31\n",
      "1959-01-05    44\n",
      "Name: Births, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(series.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32985f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "seriesp = read_csv('daily-total-female-births.csv', header=0, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb19b153",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Births\n",
      "Date              \n",
      "1959-01-01      35\n",
      "1959-01-02      32\n",
      "1959-01-03      30\n",
      "1959-01-04      31\n",
      "1959-01-05      44\n",
      "1959-01-06      29\n",
      "1959-01-07      45\n",
      "1959-01-08      43\n",
      "1959-01-09      38\n",
      "1959-01-10      27\n"
     ]
    }
   ],
   "source": [
    "print(seriesp.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97e88af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Births\n",
      "Date              \n",
      "1959-12-27      37\n",
      "1959-12-28      52\n",
      "1959-12-29      48\n",
      "1959-12-30      55\n",
      "1959-12-31      50\n"
     ]
    }
   ],
   "source": [
    "print(seriesp.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "83b38042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "365\n"
     ]
    }
   ],
   "source": [
    "print(seriesp.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "280404fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "1959-01-01    35\n",
      "1959-01-02    32\n",
      "1959-01-03    30\n",
      "1959-01-04    31\n",
      "1959-01-05    44\n",
      "1959-01-06    29\n",
      "1959-01-07    45\n",
      "1959-01-08    43\n",
      "1959-01-09    38\n",
      "1959-01-10    27\n",
      "1959-01-11    38\n",
      "1959-01-12    33\n",
      "1959-01-13    55\n",
      "1959-01-14    47\n",
      "1959-01-15    45\n",
      "1959-01-16    37\n",
      "1959-01-17    50\n",
      "1959-01-18    43\n",
      "1959-01-19    41\n",
      "1959-01-20    52\n",
      "1959-01-21    34\n",
      "1959-01-22    53\n",
      "1959-01-23    39\n",
      "1959-01-24    32\n",
      "1959-01-25    37\n",
      "1959-01-26    43\n",
      "1959-01-27    39\n",
      "1959-01-28    35\n",
      "1959-01-29    44\n",
      "1959-01-30    38\n",
      "1959-01-31    24\n",
      "Name: Births, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(series['1959-01'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "71a11c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.describe of Date\n",
      "1959-01-01    35\n",
      "1959-01-02    32\n",
      "1959-01-03    30\n",
      "1959-01-04    31\n",
      "1959-01-05    44\n",
      "              ..\n",
      "1959-12-27    37\n",
      "1959-12-28    52\n",
      "1959-12-29    48\n",
      "1959-12-30    55\n",
      "1959-12-31    50\n",
      "Name: Births, Length: 365, dtype: int64>\n"
     ]
    }
   ],
   "source": [
    "print(series.describe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6daf65",
   "metadata": {},
   "source": [
    " Plotting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e028d23c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(series)\n",
    "pyplot.show()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}