Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import path\n",
    "fname = path.expanduser('./b2cUSD.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\to\\path\n",
      "owhere.csv\n"
     ]
    }
   ],
   "source": [
    "print('c:\\to\\path\\nowhere.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\to\\path\\nowhere.csv\n"
     ]
    }
   ],
   "source": [
    "print(r'c:\\to\\path\\nowhere.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--@ 1 kgoh  staff    15K  9  8 00:44 ./b2cUSD.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh $fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15237"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.getsize(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.8798828125"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.getsize(fname) / (1 << 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1301677200,2.850000000000,4.000000000000\r\n",
      "1301677200,2.600000000000,0.500000000000\r\n",
      "1301677200,3.000000000000,5.000000000000\r\n",
      "1301677200,2.500000000000,1.000000000000\r\n",
      "1301677200,2.500000000000,1.500000000000\r\n",
      "1301677200,2.500000000000,27.300000000000\r\n",
      "1301677200,2.550000000000,8.600000000000\r\n",
      "1301677200,2.600000000000,5.850000000000\r\n",
      "1301677200,3.000000000000,1.660000000000\r\n",
      "1301677200,3.000000000000,3.330000000000\r\n"
     ]
    }
   ],
   "source": [
    "!head $fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1301677200,2.850000000000,4.000000000000\n",
      "1301677200,2.600000000000,0.500000000000\n",
      "1301677200,3.000000000000,5.000000000000\n",
      "1301677200,2.500000000000,1.000000000000\n",
      "1301677200,2.500000000000,1.500000000000\n",
      "1301677200,2.500000000000,27.300000000000\n",
      "1301677200,2.550000000000,8.600000000000\n",
      "1301677200,2.600000000000,5.850000000000\n",
      "1301677200,3.000000000000,1.660000000000\n",
      "1301677200,3.000000000000,3.330000000000\n",
      "1301677200,3.000000000000,7.390000000000\n"
     ]
    }
   ],
   "source": [
    "with open(fname) as fp:\n",
    "    for lnum, line in enumerate(fp):\n",
    "        if lnum > 10:\n",
    "            break\n",
    "        print(line[:-1])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     366 ./b2cUSD.csv\r\n"
     ]
    }
   ],
   "source": [
    "!wc -l $fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "366\n"
     ]
    }
   ],
   "source": [
    "with open (fname) as fp:\n",
    "    print(sum(1 for line in fp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(fname)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1301677200', '2.850000000000', '4.000000000000'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      1301677200  2.850000000000  4.000000000000\n",
       "0    1301677200            2.60            0.50\n",
       "1    1301677200            3.00            5.00\n",
       "2    1301677200            2.50            1.00\n",
       "3    1301677200            2.50            1.50\n",
       "4    1301677200            2.50           27.30\n",
       "5    1301677200            2.55            8.60\n",
       "6    1301677200            2.60            5.85\n",
       "7    1301677200            3.00            1.66\n",
       "8    1301677200            3.00            3.33\n",
       "9    1301677200            3.00            7.39\n",
       "10   1301677200            2.75            1.80\n",
       "11   1301677200            0.50           99.88\n",
       "12   1301677200            0.50          169.00\n",
       "13   1301677200            0.51           60.00\n",
       "14   1301677200            0.52          600.00\n",
       "15   1301677200            0.87          105.62\n",
       "16   1301677200            0.88           12.50\n",
       "17   1301677200            2.75           18.20\n",
       "18   1301677200            2.00           24.93\n",
       "19   1301677200            1.73            2.89\n",
       "20   1301677200            1.73           44.18\n",
       "21   1301677200            1.73            7.84\n",
       "22   1301677200            1.00          200.00\n",
       "23   1301677200            1.68            5.00\n",
       "24   1301677200            1.67           25.00\n",
       "25   1301677200            1.68            5.00\n",
       "26   1301677200            1.00           10.00\n",
       "27   1301677200            1.68            5.00\n",
       "28   1301677200            1.73          168.00\n",
       "29   1301677200            1.73            5.00\n",
       "..          ...             ...             ...\n",
       "335  1320108903            3.40            1.00\n",
       "336  1320130503            3.25            4.00\n",
       "337  1320192005            3.40            5.00\n",
       "338  1320426006            3.20            2.00\n",
       "339  1320500403            3.10            3.00\n",
       "340  1320503404            3.10           10.00\n",
       "341  1320509704            3.10           10.00\n",
       "342  1320512703            3.00            1.00\n",
       "343  1320537903            3.50            5.71\n",
       "344  1320775503            3.10            3.00\n",
       "345  1320845103            3.00            2.00\n",
       "346  1320847803            3.00            1.40\n",
       "347  1321236904            2.80           17.00\n",
       "348  1321238404            2.70           30.00\n",
       "349  1321291503            2.66            7.50\n",
       "350  1321295703            2.66            0.02\n",
       "351  1324301108            4.00           10.00\n",
       "352  1324403708            5.00            9.70\n",
       "353  1324659614            5.00            6.00\n",
       "354  1325264415            4.40            8.00\n",
       "355  1325450710            5.00           10.00\n",
       "356  1325457013            5.00           40.00\n",
       "357  1325716213            8.00            2.00\n",
       "358  1325815508            6.00           10.00\n",
       "359  1325866810            8.00            1.00\n",
       "360  1327104309            5.00            2.00\n",
       "361  1327119309            7.00            0.25\n",
       "362  1327142410            6.00            2.75\n",
       "363  1327174212            6.00            1.50\n",
       "364  1327176610            6.50            0.10\n",
       "\n",
       "[365 rows x 3 columns]>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      1301677200  2.850000000000  4.000000000000\n",
       "0    1301677200            2.60            0.50\n",
       "1    1301677200            3.00            5.00\n",
       "2    1301677200            2.50            1.00\n",
       "3    1301677200            2.50            1.50\n",
       "4    1301677200            2.50           27.30\n",
       "5    1301677200            2.55            8.60\n",
       "6    1301677200            2.60            5.85\n",
       "7    1301677200            3.00            1.66\n",
       "8    1301677200            3.00            3.33\n",
       "9    1301677200            3.00            7.39\n",
       "10   1301677200            2.75            1.80\n",
       "11   1301677200            0.50           99.88\n",
       "12   1301677200            0.50          169.00\n",
       "13   1301677200            0.51           60.00\n",
       "14   1301677200            0.52          600.00\n",
       "15   1301677200            0.87          105.62\n",
       "16   1301677200            0.88           12.50\n",
       "17   1301677200            2.75           18.20\n",
       "18   1301677200            2.00           24.93\n",
       "19   1301677200            1.73            2.89\n",
       "20   1301677200            1.73           44.18\n",
       "21   1301677200            1.73            7.84\n",
       "22   1301677200            1.00          200.00\n",
       "23   1301677200            1.68            5.00\n",
       "24   1301677200            1.67           25.00\n",
       "25   1301677200            1.68            5.00\n",
       "26   1301677200            1.00           10.00\n",
       "27   1301677200            1.68            5.00\n",
       "28   1301677200            1.73          168.00\n",
       "29   1301677200            1.73            5.00\n",
       "..          ...             ...             ...\n",
       "335  1320108903            3.40            1.00\n",
       "336  1320130503            3.25            4.00\n",
       "337  1320192005            3.40            5.00\n",
       "338  1320426006            3.20            2.00\n",
       "339  1320500403            3.10            3.00\n",
       "340  1320503404            3.10           10.00\n",
       "341  1320509704            3.10           10.00\n",
       "342  1320512703            3.00            1.00\n",
       "343  1320537903            3.50            5.71\n",
       "344  1320775503            3.10            3.00\n",
       "345  1320845103            3.00            2.00\n",
       "346  1320847803            3.00            1.40\n",
       "347  1321236904            2.80           17.00\n",
       "348  1321238404            2.70           30.00\n",
       "349  1321291503            2.66            7.50\n",
       "350  1321295703            2.66            0.02\n",
       "351  1324301108            4.00           10.00\n",
       "352  1324403708            5.00            9.70\n",
       "353  1324659614            5.00            6.00\n",
       "354  1325264415            4.40            8.00\n",
       "355  1325450710            5.00           10.00\n",
       "356  1325457013            5.00           40.00\n",
       "357  1325716213            8.00            2.00\n",
       "358  1325815508            6.00           10.00\n",
       "359  1325866810            8.00            1.00\n",
       "360  1327104309            5.00            2.00\n",
       "361  1327119309            7.00            0.25\n",
       "362  1327142410            6.00            2.75\n",
       "363  1327174212            6.00            1.50\n",
       "364  1327176610            6.50            0.10\n",
       "\n",
       "[365 rows x 3 columns]>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "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",
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