18.0_Pandas.ipynb

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    "## Getting Started with Pandas\n",
    "\n",
    "\n",
    "**Important Message**\n",
    "\n",
    "- 🔰 Notes Link - [Python Learning From Zero to Hero](https://gnnandan.notion.site/Python-Learning-b3aa7ed958fd44659aa1be95f0f225a6)\n",
    "- 🔰 Open Source Developers Community Link (OSDC) - [Curious Developers Community](https://linktr.ee/curiousdevelopers.community) \n",
    "- 🔰 Official Website - [Curious Community Ecosystem](https://curiousdevelopers.in)\n",
    "- 🔰 Getting Started WIth Pandas - [Pandas Official Docs](https://pandas.pydata.org/pandas-docs/version/1.4/getting_started/index.html)\n",
    "- 🔰Pandas Cheatsheet - [CheatSheet](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf)\n",
    "- 🔰 **Pandas all Operations** - [Pandas Best Tutorials](https://www.youtube.com/watch?v=vmEHCJofslg)"
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    "### Important Builtin Methods\n",
    "\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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     "text": [
      "My Pandas Version: 1.4.4\n"
     ]
    }
   ],
   "source": [
    "# importing a file using pandas\n",
    "import pandas as pd\n",
    "\n",
    "# printing all the builtin methods of pandas\n",
    "dir(pd)\n",
    "\n",
    "# checking pandas version\n",
    "\n",
    "print(\"My Pandas Version:\",pd.__version__)"
   ]
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    "### Creating Python pandas Dataframe with list\n",
    "\n",
    "**Syntax**\n",
    "1. imporing pandas library as pandasRefVar\n",
    "2. data = [listData]\n",
    "3. pandasRefVar.DataFrame(data)"
   ]
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     "text": [
      "      0                             1\n",
      "0  Name                        Nandan\n",
      "1   CDC  Curious Developers Community\n",
      "2    CT          Curious Technologies\n",
      "  Personal Info                   Domain Name\n",
      "0          Name                        Nandan\n",
      "1           CDC  Curious Developers Community\n",
      "2            CT          Curious Technologies\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = [[\"Name\",\"Nandan\"],[\"CDC\",\"Curious Developers Community\"],[\"CT\",\"Curious Technologies\"]]\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n",
    "\n",
    "# adding column names to a dataframe\n",
    "dfColumnName = pd.DataFrame(data,columns=[[\"Personal Info\",\"Domain Name\"]])\n",
    "print(dfColumnName)"
   ]
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    "### Creating Python pandas Dataframe with Dictionary\n",
    "\n",
    "**Syntax**\n",
    "1. imporing pandas library as pandasRefVar\n",
    "2. data = {KeyData,ValueData}\n",
    "3. pandasRefVar.DataFrame(data)"
   ]
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     "text": [
      "     name  Age Profession   Salary\n",
      "0    John   25  Developer  1000000\n",
      "1    Mike   32    Analyst  1200000\n",
      "2  Suresh   30      Admin   900000\n",
      "3   Tracy   26         HR  1100000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "        'name': ['John', 'Mike', 'Suresh', 'Tracy'],\n",
    "        'Age': [25, 32, 30, 26],\n",
    "        'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],\n",
    "        'Salary':[1000000, 1200000, 900000, 1100000]\n",
    "        }\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
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    "### creating Python pandas DataFrame from dict of lists\n",
    "\n",
    "**Syntax**\n",
    "1. imporing pandas library as pandasRefVar\n",
    "2. listData = [ListData]\n",
    "3. dictData = {KeyData,ListData}\n",
    "4. pandasRefVar.DataFrame(data)"
   ]
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     "text": [
      "     name  Age Profession   Salary\n",
      "0    John   25  Developer  1000000\n",
      "1    Mike   32    Analyst  1200000\n",
      "2  Suresh   30      Admin   900000\n",
      "3   Tracy   26         HR  1100000\n",
      "\n",
      "Index(['name', 'Age', 'Profession', 'Salary'], dtype='object')\n",
      "\n",
      "(4, 4)\n",
      "\n",
      "[['John' 25 'Developer' 1000000]\n",
      " ['Mike' 32 'Analyst' 1200000]\n",
      " ['Suresh' 30 'Admin' 900000]\n",
      " ['Tracy' 26 'HR' 1100000]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# list data\n",
    "names = ['John', 'Mike', 'Suresh', 'Tracy']\n",
    "ages =  [25, 32, 30, 26]\n",
    "Professions = ['Developer', 'Analyst', 'Admin', 'HR']\n",
    "Salaries = [1000000, 1200000, 900000, 1100000]\n",
    "\n",
    "# dictionary data\n",
    "table = {'name': names, 'Age': ages, 'Profession': Professions, 'Salary': Salaries}\n",
    "\n",
    "df = pd.DataFrame(table)\n",
    "print(df)\n",
    "\n",
    "print()\n",
    "# printing the column names\n",
    "print(df.columns)\n",
    "\n",
    "print()\n",
    "# checking size of dataframe\n",
    "print(df.shape) #gives the number of rows and columns\n",
    "\n",
    "print()\n",
    "# printing all the values\n",
    "print(df.values)"
   ]
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   "source": [
    "### Working with Real Data"
   ]
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      "(8, 10)\n",
      "            Artist   Length\n",
      "0  Michael Jackson  0:42:19\n",
      "1            AC/DC  0:42:11\n",
      "2       Pink Floyd  0:42:49\n",
      "3  Whitney Houston  0:57:44\n",
      "4        Meat Loaf  0:46:33\n",
      "5           Eagles  0:43:08\n",
      "6         Bee Gees  1:15:54\n",
      "7    Fleetwood Mac  0:40:01\n",
      "\n",
      "Michael Jackson\n",
      "Michael Jackson\n",
      "Index(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'], dtype='object')\n",
      "\n",
      "Artist                               object\n",
      "Album                                object\n",
      "Released                              int64\n",
      "Length                               object\n",
      "Genre                                object\n",
      "Music Recording Sales (millions)    float64\n",
      "Claimed Sales (millions)              int64\n",
      "Released.1                           object\n",
      "Soundtrack                           object\n",
      "Rating                              float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# importing a file using pandas\n",
    "import pandas as pd\n",
    "\n",
    "# storing the filepath in variable\n",
    "csv_path = 'C:/Users/NANDANGN/Desktop/Python Programming/Python_Programming/TopSellingAlbums.csv'\n",
    "\n",
    "# reading a file (dataframe)\n",
    "df = pd.read_csv(csv_path)\n",
    "\n",
    "# printing head()\n",
    "df.head()\n",
    "\n",
    "# understanding the size of the dataframe\n",
    "print(df.shape)\n",
    "\n",
    "# accessing the required column\n",
    "x = df[['Artist','Length']]\n",
    "type(x)\n",
    "print(x)\n",
    "\n",
    "print()\n",
    "\n",
    "# iloc - accessing the individual values based on rows and columns\n",
    "firstArtist_way1 = df.iloc[0,0]\n",
    "print(firstArtist_way1)\n",
    "\n",
    "# loc - Access the column's value using the coulmn name\n",
    "print(df.loc[0,'Artist'])\n",
    "\n",
    "# loc[] - slicing the dataframe\n",
    "\n",
    "slicedData = df.loc[0:2,'Artist':'Released']\n",
    "slicedData\n",
    "\n",
    "# changing the index\n",
    "new_index=['a','b','c','d','e','f','g','h']\n",
    "\n",
    "reFormattedIndex = df\n",
    "reFormattedIndex.index = new_index\n",
    "\n",
    "print(df.index)\n",
    "\n",
    "print()\n",
    "\n",
    "# printing the datatypes of each column\n",
    "print(df.dtypes)"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Artist</th>\n",
       "      <th>Album</th>\n",
       "      <th>Released</th>\n",
       "      <th>Length</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Music Recording Sales (millions)</th>\n",
       "      <th>Claimed Sales (millions)</th>\n",
       "      <th>Released.1</th>\n",
       "      <th>Soundtrack</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Jackson</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>1982</td>\n",
       "      <td>0:42:19</td>\n",
       "      <td>pop, rock, R&amp;B</td>\n",
       "      <td>46.0</td>\n",
       "      <td>65</td>\n",
       "      <td>30-Nov-82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC/DC</td>\n",
       "      <td>Back in Black</td>\n",
       "      <td>1980</td>\n",
       "      <td>0:42:11</td>\n",
       "      <td>hard rock</td>\n",
       "      <td>26.1</td>\n",
       "      <td>50</td>\n",
       "      <td>25-Jul-80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>The Dark Side of the Moon</td>\n",
       "      <td>1973</td>\n",
       "      <td>0:42:49</td>\n",
       "      <td>progressive rock</td>\n",
       "      <td>24.2</td>\n",
       "      <td>45</td>\n",
       "      <td>01-Mar-73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Whitney Houston</td>\n",
       "      <td>The Bodyguard</td>\n",
       "      <td>1992</td>\n",
       "      <td>0:57:44</td>\n",
       "      <td>R&amp;B, soul, pop</td>\n",
       "      <td>27.4</td>\n",
       "      <td>44</td>\n",
       "      <td>17-Nov-92</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Meat Loaf</td>\n",
       "      <td>Bat Out of Hell</td>\n",
       "      <td>1977</td>\n",
       "      <td>0:46:33</td>\n",
       "      <td>hard rock, progressive rock</td>\n",
       "      <td>20.6</td>\n",
       "      <td>43</td>\n",
       "      <td>21-Oct-77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Eagles</td>\n",
       "      <td>Their Greatest Hits (1971-1975)</td>\n",
       "      <td>1976</td>\n",
       "      <td>0:43:08</td>\n",
       "      <td>rock, soft rock, folk rock</td>\n",
       "      <td>32.2</td>\n",
       "      <td>42</td>\n",
       "      <td>17-Feb-76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bee Gees</td>\n",
       "      <td>Saturday Night Fever</td>\n",
       "      <td>1977</td>\n",
       "      <td>1:15:54</td>\n",
       "      <td>disco</td>\n",
       "      <td>20.6</td>\n",
       "      <td>40</td>\n",
       "      <td>15-Nov-77</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fleetwood Mac</td>\n",
       "      <td>Rumours</td>\n",
       "      <td>1977</td>\n",
       "      <td>0:40:01</td>\n",
       "      <td>soft rock</td>\n",
       "      <td>27.9</td>\n",
       "      <td>40</td>\n",
       "      <td>04-Feb-77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "            Artist                            Album  Released   Length  \\\n",
       "0  Michael Jackson                         Thriller      1982  0:42:19   \n",
       "1            AC/DC                    Back in Black      1980  0:42:11   \n",
       "2       Pink Floyd        The Dark Side of the Moon      1973  0:42:49   \n",
       "3  Whitney Houston                    The Bodyguard      1992  0:57:44   \n",
       "4        Meat Loaf                  Bat Out of Hell      1977  0:46:33   \n",
       "5           Eagles  Their Greatest Hits (1971-1975)      1976  0:43:08   \n",
       "6         Bee Gees             Saturday Night Fever      1977  1:15:54   \n",
       "7    Fleetwood Mac                          Rumours      1977  0:40:01   \n",
       "\n",
       "                         Genre  Music Recording Sales (millions)  \\\n",
       "0               pop, rock, R&B                              46.0   \n",
       "1                    hard rock                              26.1   \n",
       "2             progressive rock                              24.2   \n",
       "3               R&B, soul, pop                              27.4   \n",
       "4  hard rock, progressive rock                              20.6   \n",
       "5   rock, soft rock, folk rock                              32.2   \n",
       "6                        disco                              20.6   \n",
       "7                    soft rock                              27.9   \n",
       "\n",
       "   Claimed Sales (millions) Released.1 Soundtrack  Rating  \n",
       "0                        65  30-Nov-82        NaN    10.0  \n",
       "1                        50  25-Jul-80        NaN     9.5  \n",
       "2                        45  01-Mar-73        NaN     9.0  \n",
       "3                        44  17-Nov-92          Y     8.5  \n",
       "4                        43  21-Oct-77        NaN     8.0  \n",
       "5                        42  17-Feb-76        NaN     7.5  \n",
       "6                        40  15-Nov-77          Y     7.0  \n",
       "7                        40  04-Feb-77        NaN     6.5  "
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