18.1_Pandas_With_Real_Data.ipynb

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    "### Implementation of Pandas In Real DataSet\n",
    "**Student DataSet** - https://www.kaggle.com/datasets/abcsds/pokemon\n",
    "\n",
    "**Pandas all Operations** - https://www.youtube.com/watch?v=vmEHCJofslg\n",
    "<br></br>\n",
    "### Important Builtin Methods\n",
    "```import pandas as pd ```\n",
    "\n",
    "1. **DataFrame Creation**\n",
    "   1. pd.DataFrame(data): Create a DataFrame from various data structures like arrays, lists, dictionaries, or NumPy arrays.\n",
    "   2. pd.read_csv(file_path): Read a CSV file and create a DataFrame.\n",
    "   <br></br>\n",
    "2. **Data Exploration and Manipulation**\n",
    "   1. df.head(n): Get the first n rows of the DataFrame.\n",
    "   2. df.tail(n): Get the last n rows of the DataFrame.\n",
    "   3. df.info(): Display information about the DataFrame, including data types and column names.\n",
    "   4. df.describe(): Generate descriptive statistics of the DataFrame.\n",
    "   5. df.shape: Get the dimensions (rows, columns) of the DataFrame.\n",
    "   6. df.columns: Get the column labels of the DataFrame.\n",
    "   7. df.dtypes: Get the data types of each column.\n",
    "   8. df.isnull(): Check for missing values in the DataFrame.\n",
    "   9. df.dropna(): Remove rows or columns with missing values.\n",
    "   10. df.groupby(by): Group the DataFrame by one or more columns.\n",
    "   11. df.sort_values(by): Sort the DataFrame based on one or more columns.\n",
    "   12. df.rename(columns): Rename columns of the DataFrame.\n",
    "   13. df.merge(df2): Merge two DataFrames based on common columns.\n",
    "<br></br>\n",
    "3. **Data Selection and Filtering**\n",
    "   1. df.index = Used to get all the index of the data frame\n",
    "   2. df[column_name]: Select a specific column or multiple columns.\n",
    "   3. df.loc[row_indexer, column_indexer]: Access specific rows and columns using labels.\n",
    "   4. df.iloc[row_indexer, column_indexer]: Access specific rows and columns using integer-based indexing.\n",
    "   5. df.query(expression): Filter rows based on a Boolean expression.\n"
   ]
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    "import pandas as pd\n",
    "\n",
    "filePath = \"C:/Users/NANDANGN/Desktop/Python Programming/Python_Programming/Pokemon.csv\"\n",
    "\n",
    "df = pd.read_csv(filePath)\n",
    "\n",
    "# Understanding data head and tail\n",
    "head = df.head()\n",
    "tail = df.tail()\n",
    "# print(head)\n",
    "# print(tail)\n"
   ]
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    "### Reading Data In Pandas\n",
    "1. reading headers - df.columns\n",
    "2. reading each row - df.iloc['number of rows']\n",
    "3. reading each column - df['Col Name']\n",
    "4. reading specific location (R, C) - df.iloc[row, col]\n",
    "5. iterating each rows in for loop- df.iterrows() \n",
    "\n",
    "### Sorting and Describing Data\n",
    "1. Describing data - df.describe()\n",
    "2. Sortign Data based on order - df.sort_values('Column',ascending=False/True)\n",
    "\n",
    "### Making Changes in Data\n",
    "1. Creating a new Column\n",
    "2. Creating a new Column -> df['columnName'] = datao\n",
    "3. Dropping columns -> df.drop(columns= ColName)\n",
    "\n",
    "### Filetering Data & Saving the Data\n",
    "1. Example 1 -> df.loc[(df['Type 1']=='Grass') & (df['Total']>=500)]\n",
    "2. Example 2 -> \n",
    "   1. new_df = df.loc[(df['Type 1'] =='Grass') & (df['Type 2'] =='Poison') & (df['Total']>=500)]\n",
    "   2. new_df.reset_index(drop=True,inplace=True) #dropping the existing index and forming new index\n",
    "   3. new_df.to_csv('Strong_Polemon.csv')\n",
    "3. Example 3 ->\n",
    "   1. Checking for contains: Considering the data which contains the required string -> df.loc[(df['Name'].str.contains('Mega'))]\n",
    "   2. Checking For Not Contains: df.loc[(df['Type 1'].str.contains('Grass')) & (df['Type 2'].str.contains('Poison')==False)]\n",
    "   3. Using Regex for more filter(import re)\n",
    "      1. df.loc[df['Name'].str.contains('pi[a-z]*',flags=re.I,regex=True)]\n",
    "      2. df.loc[(df['Type 1'].str.contains('Grass')) & (df['Type 2'].str.contains('Poison|Fighting', flags=re.I,regex=True))]\n",
    "\n",
    "### Conditional Changes\n",
    "1. Changing value inside the table -> df.loc[df['Type 1']=='Fairy', 'Type 1']='Fire'\n",
    "\n",
    "### Groupby(Sum, Mean, Counting)\n",
    "1. df.groupby(['Type 1']).mean().sort_values('Defense',ascending=False)\n",
    "2. df.groupby(['Name']).sum().sort_values('Total')\n"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fire</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grass</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fire</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bug</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairy</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fire</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychic</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>433 rows × 14 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         # Name Type 1 Type 2 HP Attack Defense Sp. Atk Sp. Def Speed  \\\n",
       "Fire     1    1    NaN      0  1      1       1       1       1     1   \n",
       "Grass    4    4    NaN      4  4      4       4       4       4     4   \n",
       "Fire     4    4    NaN      3  4      4       4       4       4     4   \n",
       "Water    1    1    NaN      0  1      1       1       1       1     1   \n",
       "Bug      2    2    NaN      0  2      2       2       2       2     2   \n",
       "...     ..  ...    ...    ... ..    ...     ...     ...     ...   ...   \n",
       "Fairy    1    1    NaN      0  1      1       1       1       1     1   \n",
       "Flying   2    2    NaN      2  2      2       2       2       2     2   \n",
       "Fire     1    1    NaN      1  1      1       1       1       1     1   \n",
       "Psychic  2    2    NaN      2  2      2       2       2       2     2   \n",
       "Rock     2    2    NaN      2  2      2       2       2       2     2   \n",
       "\n",
       "        Generation Legendary Total count  \n",
       "Fire             1         1   NaN   NaN  \n",
       "Grass            4         4   NaN   NaN  \n",
       "Fire             4         4   NaN   NaN  \n",
       "Water            1         1   NaN   NaN  \n",
       "Bug              2         2   NaN   NaN  \n",
       "...            ...       ...   ...   ...  \n",
       "Fairy            1         1   NaN   NaN  \n",
       "Flying           2         2   NaN   NaN  \n",
       "Fire             1         1   NaN   NaN  \n",
       "Psychic          2         2   NaN   NaN  \n",
       "Rock             2         2   NaN   NaN  \n",
       "\n",
       "[433 rows x 14 columns]"
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    "## reading headers\n",
    "columns = df.columns\n",
    "# print(columns)\n",
    "\n",
    "## Read each column\n",
    "name = df['Name'] #also we can have multiple columns df[['Name','Speed']]\n",
    "# print(name)\n",
    "# print(name[0:5])\n",
    "\n",
    "## Reading each row\n",
    "# print(df.iloc[1])\n",
    "\n",
    "## reading specific location (R, C)\n",
    "# print(df.iloc[2,2])\n",
    "\n",
    "## iteration rows\n",
    "# for index, row in df.iterrows():\n",
    "#   print(index, row)\n",
    "\n",
    "grass = df.loc[df['Type 1']=='Grass']\n",
    "grass\n",
    "\n",
    "\n",
    "## Describing data\n",
    "describeData = df.describe()\n",
    "describeData\n",
    "\n",
    "## Sortign Data based on order\n",
    "sortedData = df.sort_values('Name',ascending=False) #ascending=False gives descending data\n",
    "sortedData\n",
    "\n",
    "## Creating a new Column -> df['columnName'] = datao\n",
    "df['Total'] = df['HP'] + df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def']+df['Speed']\n",
    "df\n",
    "\n",
    "### Way 2 of sum\n",
    "df['Total'] = df.iloc[:,4:10].sum(axis=1)\n",
    "\n",
    "## Dropping columns -> df.drop(columns= ColName)\n",
    "# df = df.drop(columns='Total')\n",
    "df\n",
    "\n",
    "print(df['Total'].max())\n",
    "\n",
    "\n",
    "## Filetering Data & Saving the Data\n",
    "df.loc[(df['Type 1']=='Grass') & (df['Total']>=500)]\n",
    "new_df = df.loc[(df['Type 1'] =='Grass') & (df['Type 2'] =='Poison') & (df['Total']>=500)]\n",
    "new_df.reset_index(drop=True,inplace=True) #dropping the existing index and forming new index\n",
    "new_df.to_csv('Strong_Polemon.csv')\n",
    "\n",
    "### Considering the data which contains the required string\n",
    "df.loc[(df['Name'].str.contains('Mega'))]\n",
    "df.loc[(df['Type 1'].str.contains('Grass'))]\n",
    "df.loc[(df['Type 1'].str.contains('Grass')) & (df['Type 2'].str.contains('Poison')==False)]\n",
    "\n",
    "### Using Regex for more filter\n",
    "import re\n",
    "df.loc[df['Name'].str.contains('pi[a-z]*',flags=re.I,regex=True)] ##displays data that contains pi in the Name column\n",
    "df.loc[(df['Type 1'].str.contains('Grass')) & (df['Type 2'].str.contains('Poison|Fighting', flags=re.I,regex=True))]\n",
    "\n",
    "## Conditional Changes\n",
    "\n",
    "df.loc[df['Type 1']=='Fairy', 'Type 1']='Fire'\n",
    "df['Type 2'].unique()\n",
    "\n",
    "\n",
    "## Groupby\n",
    "df.groupby(['Type 1']).mean().sort_values('Defense',ascending=False)\n",
    "\n",
    "df.groupby(['Name']).sum().sort_values('Attack',ascending=False)\n",
    "\n",
    "\n",
    "df['count'] = 1\n",
    "df.groupby(['Name','Type 1','Type 2']).count()['count']"
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>719</td>\n",
       "      <td>DiancieMega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>170</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                   Name   Type 1  Type 2  HP  Attack  Defense  \\\n",
       "0      1              Bulbasaur    Grass  Poison  45      49       49   \n",
       "1      2                Ivysaur    Grass  Poison  60      62       63   \n",
       "2      3               Venusaur    Grass  Poison  80      82       83   \n",
       "3      3  VenusaurMega Venusaur    Grass  Poison  80     100      123   \n",
       "4      4             Charmander     Fire     NaN  39      52       43   \n",
       "..   ...                    ...      ...     ...  ..     ...      ...   \n",
       "795  719                Diancie     Rock   Fairy  50     100      150   \n",
       "796  719    DiancieMega Diancie     Rock   Fairy  50     160      110   \n",
       "797  720    HoopaHoopa Confined  Psychic   Ghost  80     110       60   \n",
       "798  720     HoopaHoopa Unbound  Psychic    Dark  80     160       60   \n",
       "799  721              Volcanion     Fire   Water  80     110      120   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  Total  \n",
       "0         65       65     45           1      False    318  \n",
       "1         80       80     60           1      False    405  \n",
       "2        100      100     80           1      False    525  \n",
       "3        122      120     80           1      False    625  \n",
       "4         60       50     65           1      False    309  \n",
       "..       ...      ...    ...         ...        ...    ...  \n",
       "795      100      150     50           6       True    600  \n",
       "796      160      110    110           6       True    700  \n",
       "797      150      130     70           6       True    600  \n",
       "798      170      130     80           6       True    680  \n",
       "799      130       90     70           6       True    600  \n",
       "\n",
       "[800 rows x 13 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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