Practice Pandas.ipynb

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   "source": [
    "## Pandas \n",
    "It is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Data Creation </b>\n",
    "There are basically 2 type of objects in pandas<br>\n",
    "Dataframe and <br>\n",
    "Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A1  A2\n",
       "0   2   5\n",
       "1   3   1\n",
       "2   4   9"
      ]
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     "execution_count": 17,
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    "# DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.\n",
    "# You can think of it like a spreadsheet or SQL table, or a dict of Series objects.\n",
    "\n",
    "A=pd.DataFrame({\"A1\":[2,3,4],\"A2\":[5,1,9]})\n",
    "A"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    5\n",
       "3    7\n",
       "4    3\n",
       "5    4\n",
       "dtype: int64"
      ]
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     "execution_count": 6,
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   "source": [
    "#Series is a one-dimensional labeled array capable of holding data of any type \n",
    "#  that is ,integer, string, float, python objects, etc.. The axis labels are collectively called index.\n",
    "\n",
    "pd.Series([1,2,5,7,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N1    A\n",
       "N2    B\n",
       "N3    C\n",
       "N4    D\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "pd.Series(['A','B','C','D'], index=['N1','N2','N3','N4'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Reading csv Files </b> \n",
    "For e.g csv file\n",
    "pd.read_csv(\"path\\filename.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\ASUS\\Desktop\\Iris.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data details"
   ]
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   "execution_count": 14,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
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       "      <td>Iris-setosa</td>\n",
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       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df.shape"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Saving to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file_name.to_csv(\"path/saved file name.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <b> Indexing </b>\n",
    "Pandas has its own accessor operators, loc and iloc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>loc()</b> is label based data selecting method which means that we have to pass the name of the row or column which we want to select."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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       "      <td>Iris-setosa</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>9</th>\n",
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       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5.4</td>\n",
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       "      <td>1.5</td>\n",
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       "      <td>4.3</td>\n",
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       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>15</th>\n",
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       "      <td>5.7</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>16</th>\n",
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       "      <td>5.4</td>\n",
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       "      <td>5.1</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>18</th>\n",
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       "      <td>5.7</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <td>5.1</td>\n",
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       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>5.4</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>21</th>\n",
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       "      <td>5.1</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>24</th>\n",
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       "      <td>4.8</td>\n",
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       "      <td>5.0</td>\n",
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       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
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       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
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       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0    1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1    2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2    3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3    4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4    5            5.0           3.6            1.4           0.2  Iris-setosa\n",
       "5    6            5.4           3.9            1.7           0.4  Iris-setosa\n",
       "6    7            4.6           3.4            1.4           0.3  Iris-setosa\n",
       "7    8            5.0           3.4            1.5           0.2  Iris-setosa\n",
       "8    9            4.4           2.9            1.4           0.2  Iris-setosa\n",
       "9   10            4.9           3.1            1.5           0.1  Iris-setosa\n",
       "10  11            5.4           3.7            1.5           0.2  Iris-setosa\n",
       "11  12            4.8           3.4            1.6           0.2  Iris-setosa\n",
       "12  13            4.8           3.0            1.4           0.1  Iris-setosa\n",
       "13  14            4.3           3.0            1.1           0.1  Iris-setosa\n",
       "14  15            5.8           4.0            1.2           0.2  Iris-setosa\n",
       "15  16            5.7           4.4            1.5           0.4  Iris-setosa\n",
       "16  17            5.4           3.9            1.3           0.4  Iris-setosa\n",
       "17  18            5.1           3.5            1.4           0.3  Iris-setosa\n",
       "18  19            5.7           3.8            1.7           0.3  Iris-setosa\n",
       "19  20            5.1           3.8            1.5           0.3  Iris-setosa\n",
       "20  21            5.4           3.4            1.7           0.2  Iris-setosa\n",
       "21  22            5.1           3.7            1.5           0.4  Iris-setosa\n",
       "22  23            4.6           3.6            1.0           0.2  Iris-setosa\n",
       "23  24            5.1           3.3            1.7           0.5  Iris-setosa\n",
       "24  25            4.8           3.4            1.9           0.2  Iris-setosa\n",
       "25  26            5.0           3.0            1.6           0.2  Iris-setosa\n",
       "26  27            5.0           3.4            1.6           0.4  Iris-setosa\n",
       "27  28            5.2           3.5            1.5           0.2  Iris-setosa\n",
       "28  29            5.2           3.4            1.4           0.2  Iris-setosa\n",
       "29  30            4.7           3.2            1.6           0.2  Iris-setosa\n",
       "30  31            4.8           3.1            1.6           0.2  Iris-setosa\n",
       "31  32            5.4           3.4            1.5           0.4  Iris-setosa\n",
       "32  33            5.2           4.1            1.5           0.1  Iris-setosa\n",
       "33  34            5.5           4.2            1.4           0.2  Iris-setosa\n",
       "34  35            4.9           3.1            1.5           0.1  Iris-setosa\n",
       "35  36            5.0           3.2            1.2           0.2  Iris-setosa\n",
       "36  37            5.5           3.5            1.3           0.2  Iris-setosa\n",
       "37  38            4.9           3.1            1.5           0.1  Iris-setosa\n",
       "38  39            4.4           3.0            1.3           0.2  Iris-setosa\n",
       "39  40            5.1           3.4            1.5           0.2  Iris-setosa\n",
       "40  41            5.0           3.5            1.3           0.3  Iris-setosa\n",
       "41  42            4.5           2.3            1.3           0.3  Iris-setosa\n",
       "42  43            4.4           3.2            1.3           0.2  Iris-setosa\n",
       "43  44            5.0           3.5            1.6           0.6  Iris-setosa\n",
       "44  45            5.1           3.8            1.9           0.4  Iris-setosa\n",
       "45  46            4.8           3.0            1.4           0.3  Iris-setosa\n",
       "46  47            5.1           3.8            1.6           0.2  Iris-setosa\n",
       "47  48            4.6           3.2            1.4           0.2  Iris-setosa\n",
       "48  49            5.3           3.7            1.5           0.2  Iris-setosa\n",
       "49  50            5.0           3.3            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.Species=='Iris-setosa']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <b> iloc() </b>is a indexed based selecting method which means that we have to pass integer index in the method to select specific row/column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm\n",
       "5   6            5.4\n",
       "6   7            4.6\n",
       "7   8            5.0\n",
       "8   9            4.4\n",
       "9  10            4.9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[5:10,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>SepalLengthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm      Species\n",
       "1            4.9  Iris-setosa\n",
       "2            4.7  Iris-setosa\n",
       "3            4.6  Iris-setosa"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,3],[1,5]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting Categorical data value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.843333333333334"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.SepalLengthCm.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>map() </b>is used to map values from two series having one column same. For mapping two series, the last column of the first series should be same as index column of the second series, also the values should be unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -2.0\n",
       "1   -1.0\n",
       "2    0.0\n",
       "3    1.0\n",
       "4   -3.0\n",
       "5    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=pd.Series([2,3,4,5,1,9])\n",
    "m=A.mean()\n",
    "A.map(lambda x:x-m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Type is Iris-setosa\n",
       "1         Type is Iris-setosa\n",
       "2         Type is Iris-setosa\n",
       "3         Type is Iris-setosa\n",
       "4         Type is Iris-setosa\n",
       "                ...          \n",
       "145    Type is Iris-virginica\n",
       "146    Type is Iris-virginica\n",
       "147    Type is Iris-virginica\n",
       "148    Type is Iris-virginica\n",
       "149    Type is Iris-virginica\n",
       "Name: Species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].map('Type is {}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "SepalLengthCm    float64\n",
       "SepalWidthCm     float64\n",
       "PetalLengthCm    float64\n",
       "PetalWidthCm     float64\n",
       "Species           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Searching for <b>Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              setosa\n",
       "1              setosa\n",
       "2              setosa\n",
       "3              setosa\n",
       "4              setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: Species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Species.replace('Iris-setosa','setosa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining Datsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
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      "text/plain": [
       "   A1  A2  B1  B2\n",
       "0   2   5  12  51\n",
       "1   3   1  13  11\n",
       "2   4   9  14  19"
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     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=pd.DataFrame({\"A1\":[2,3,4],\"A2\":[5,1,9]})\n",
    "B=pd.DataFrame({\"B1\":[12,13,14],\"B2\":[51,11,19]})\n",
    "pd.concat([A,B],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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