DATACAMP PANDAS, DATAFRAMES-2.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first import numpy and pandas\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# many types of data structures could be taken as an input in pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2\n",
      "0  1  2  3\n",
      "1  4  5  6\n",
      "   1  2  3\n",
      "0  1  1  2\n",
      "1  3  2  4\n",
      "   A\n",
      "0  4\n",
      "1  5\n",
      "2  6\n",
      "3  7\n",
      "                         0\n",
      "United Kingdom      London\n",
      "India            New Delhi\n",
      "United States   Washington\n",
      "Belgium           Brussels\n"
     ]
    }
   ],
   "source": [
    "# Take a 2D array as input to your DataFrame \n",
    "my_2darray = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(pd.DataFrame(my_2darray))\n",
    "\n",
    "# Take a dictionary as input to your DataFrame \n",
    "my_dict = {1: ['1', '3'], 2: ['1', '2'], 3: ['2', '4']}\n",
    "print(pd.DataFrame(my_dict))\n",
    "\n",
    "# Take a DataFrame as input to your DataFrame \n",
    "my_df = pd.DataFrame(data=[4,5,6,7], index=range(0,4), columns=['A'])\n",
    "print(pd.DataFrame(my_df))\n",
    "\n",
    "# Take a Series as input to your DataFrame\n",
    "my_series = pd.Series({\"United Kingdom\":\"London\", \"India\":\"New Delhi\", \"United States\":\"Washington\", \"Belgium\":\"Brussels\"})\n",
    "print(pd.DataFrame(my_series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORTING CSV FILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-5-2cb9008d0ace>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-2cb9008d0ace>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    cars=pd.read_csv('/administrator/cars.csv)\u001b[0m\n\u001b[0m                                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "cars=pd.read_csv('/administrator/cars.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Index and Selcet Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    5\n",
       "2    6\n",
       "3    7\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column access from a data frame\n",
    "my_df ['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A\n",
       "0  4\n",
       "1  5\n",
       "2  6\n",
       "3  7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for not seeing the info below the column info, use 2 brackets\n",
    "#BECAUSE this is a pd type\n",
    "\n",
    "print(type(my_df))\n",
    "\n",
    "my_df[['A']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(my_2darray))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b  c\n",
      "0  q  w  e\n",
      "1  4  5  6\n",
      "2  7  8  9\n"
     ]
    }
   ],
   "source": [
    "#     * in pandas loc for label based, iloc for positions\n",
    "# you can select one partion of a data frame by .loc, with using 2 brackets\n",
    "\n",
    "df2 = pd.DataFrame(np.array([['q', 'w', 'e'], [4, 5, 6], [7, 8, 9]]),columns=['a', 'b', 'c'])\n",
    "\n",
    "print (df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#square brackets:\n",
    "# column access: df2[[\"column name1\"],[\"colum name2\"]]\n",
    "# row access : slicing df2 [1:3]\n",
    "# with loc it is easier\n",
    "#with iloc, we use row and column numbers, not the names\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    " dict1 = {\"country\":[\"Brazil\", \"Russia\", \"India\", \"China\", \"South Africa\"],\n",
    " \"capital\":[\"Brasilia\", \"Moscow\", \"New Delhi\", \"Beijing\", \"Pretoria\"],\n",
    " \"area\":[8.516, 17.10, 3.286, 9.597, 1.221],\"population\":[200.4, 143.5, 1252, 1357, 52.98] }\n",
    "\n",
    "import pandas as pd\n",
    "brics = pd.DataFrame(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        country    capital    area  population\n",
      "0        Brazil   Brasilia   8.516      200.40\n",
      "1        Russia     Moscow  17.100      143.50\n",
      "2         India  New Delhi   3.286     1252.00\n",
      "3         China    Beijing   9.597     1357.00\n",
      "4  South Africa   Pretoria   1.221       52.98\n"
     ]
    }
   ],
   "source": [
    "print(brics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'country': ['Brazil', 'Russia', 'India', 'China', 'South Africa'],\n",
       " 'capital': ['Brasilia', 'Moscow', 'New Delhi', 'Beijing', 'Pretoria'],\n",
       " 'area': [8.516, 17.1, 3.286, 9.597, 1.221],\n",
       " 'population': [200.4, 143.5, 1252, 1357, 52.98]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics.loc[[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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