Pandas, Dataframe , Reading Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\a_ade\\\\Documents\\\\Coding Languages\\\\Python\\\\PandasPython'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd #to see path location/directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #“pandas read csv in other folder”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#“pandas read csv in other folder”\n",
    "\n",
    "#df = pd.read_csv (r'C:\\Users\\a_ade\\Downloads\\weather.csv')\n",
    "#df\n",
    "#print(df.head(3)) # this is to only see first 3 list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #“pandas read csv in directory path , run pwd to check”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         0       1   \n",
      "2            3         0       3   \n",
      "3            4         0       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch            Ticket     Fare Cabin Embarked  \n",
      "0      0         A/5 21171   7.2500   NaN        S  \n",
      "1      0          PC 17599  71.2833   C85        C  \n",
      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3      0            113803  53.1000  C123        S  \n",
      "4      0            373450   8.0500   NaN        S  \n"
     ]
    }
   ],
   "source": [
    "#df = pd.read_csv (r'C:\\Users\\a_ade\\Documents\\Spring2021\\CSC 115\\Python Project\\titanic3.txt' , delimiter='\\t')  \n",
    "#remember when reading a txt file make sure to put the delimiter='\\t'  so it wont look weird just like what is done above\n",
    "\n",
    "df = pd.read_csv (r'C:\\Users\\a_ade\\Documents\\Spring2021\\CSC 115\\Python Project\\titanic3.csv')  \n",
    "\n",
    "#for now let's print only 5 rows to make it short\n",
    "print(df.head(5))\n",
    "#print(df.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Data in Pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## read headers\n",
    "#print(df.columns)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "##read each column \n",
    "#df['Name'][0:5]\n",
    "# df['Sex'][0:5]\n",
    "# df['Age'][0:5]\n",
    "\n",
    "#multiple column\n",
    "#print(df[['Name', 'Survived', 'Pclass']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "female\n"
     ]
    }
   ],
   "source": [
    "# read each row\n",
    "print(df.iloc[1,4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId                                                    2\n",
      "Survived                                                       0\n",
      "Pclass                                                         1\n",
      "Name           Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
      "Sex                                                       female\n",
      "Age                                                           38\n",
      "SibSp                                                          1\n",
      "Parch                                                          0\n",
      "Ticket                                                  PC 17599\n",
      "Fare                                                     71.2833\n",
      "Cabin                                                        C85\n",
      "Embarked                                                       C\n",
      "Name: 1, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[1]) #row integer location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for index, row in df.iterrows():\n",
    " #   print(index, row['Name'] \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
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       "      <td>2677</td>\n",
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      "text/plain": [
       "    PassengerId  Survived  Pclass              Name   Sex   Age  SibSp  Parch  \\\n",
       "36           37         0       3  Mamee, Mr. Hanna  male  34.0      0      0   \n",
       "\n",
       "   Ticket    Fare Cabin Embarked  \n",
       "36   2677  7.2292   NaN        C  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Name']==\"Mamee, Mr. Hanna\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.297297</td>\n",
       "      <td>29.758621</td>\n",
       "      <td>0.648649</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>32.401911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.824355</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.877650</td>\n",
       "      <td>16.500336</td>\n",
       "      <td>1.033232</td>\n",
       "      <td>1.216133</td>\n",
       "      <td>47.745553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.029200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       PassengerId  Survived     Pclass        Age      SibSp      Parch  \\\n",
       "count    37.000000      37.0  37.000000  29.000000  37.000000  37.000000   \n",
       "mean     19.000000       0.0   2.297297  29.758621   0.648649   0.486486   \n",
       "std      10.824355       0.0   0.877650  16.500336   1.033232   1.216133   \n",
       "min       1.000000       0.0   1.000000   2.000000   0.000000   0.000000   \n",
       "25%      10.000000       0.0   1.000000  19.000000   0.000000   0.000000   \n",
       "50%      19.000000       0.0   3.000000  31.000000   0.000000   0.000000   \n",
       "75%      28.000000       0.0   3.000000  38.000000   1.000000   0.000000   \n",
       "max      37.000000       0.0   3.000000  66.000000   4.000000   5.000000   \n",
       "\n",
       "             Fare  \n",
       "count   37.000000  \n",
       "mean    32.401911  \n",
       "std     47.745553  \n",
       "min      7.225000  \n",
       "25%      8.029200  \n",
       "50%     16.700000  \n",
       "75%     31.275000  \n",
       "max    263.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe method , high level mean, standard deviations , count ,min max\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorting values\n",
    "\n",
    "#df.sort_values('Name')\n",
    "#df.sort_values('Name', ascending= False) #to descend it \n",
    "#df.sort_values(['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making changes to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         0       1   \n",
       "2            3         0       3   \n",
       "3            4         0       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.drop(columns=['Age']) #dropping a column\n",
    "\n",
    "# df['Total'] = df.iloc[:, 4:7].sum(axis=1)\n",
    "# cols = list(df.columns)\n",
    "# df = df[cols[0:4] + [cols [-1]] + cols [4:13]]\n",
    "\n",
    "#switching columns \n",
    "\n",
    "df = pd.read_csv (r'C:\\Users\\a_ade\\Documents\\Spring2021\\CSC 115\\Python Project\\titanic3.csv')  \n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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