pandas.ipynb

{
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   "source": [
    "## What is Pandas\n",
    "Pandas is a popular open-source data manipulation and analysis library for Python. It provides data structures for efficiently storing and manipulating large datasets and tools for working with structured data. One of the major reasons we use pandas is because it requires less laborous work as compared to working manually with sheets. It also allows for automation and makes it very easy when working with big data\n",
    "# DataFrame\n",
    "A two-dimensional table with labeled axes (rows and columns). It is similar to a spreadsheet or a table and is one of the most commonly used Pandas data structures.\n",
    "\n",
    "# Series\n",
    "A one-dimensional labeled array capable of holding any data type. It is similar to a column in a DataFrame or a single-column table."
   ]
  },
  {
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   "execution_count": 6,
   "id": "1f65a089-86c2-4595-95a8-ffa42ebbd50a",
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    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>part_time_job</th>\n",
       "      <th>absence_days</th>\n",
       "      <th>extracurricular_activities</th>\n",
       "      <th>weekly_self_study_hours</th>\n",
       "      <th>career_aspiration</th>\n",
       "      <th>math_score</th>\n",
       "      <th>history_score</th>\n",
       "      <th>physics_score</th>\n",
       "      <th>chemistry_score</th>\n",
       "      <th>biology_score</th>\n",
       "      <th>english_score</th>\n",
       "      <th>geography_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Casey</td>\n",
       "      <td>paul.casey.1@gslingacademy.com</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>73</td>\n",
       "      <td>81</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Danielle</td>\n",
       "      <td>Sandoval</td>\n",
       "      <td>danielle.sandoval.2@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Tina</td>\n",
       "      <td>Andrews</td>\n",
       "      <td>tina.andrews.3@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Government Officer</td>\n",
       "      <td>81</td>\n",
       "      <td>97</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Tara</td>\n",
       "      <td>Clark</td>\n",
       "      <td>tara.clark.4@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Artist</td>\n",
       "      <td>71</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>89</td>\n",
       "      <td>63</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Campos</td>\n",
       "      <td>anthony.campos.5@gslingacademy.com</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>84</td>\n",
       "      <td>77</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "      <td>76</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1995</td>\n",
       "      <td>1995</td>\n",
       "      <td>1996</td>\n",
       "      <td>Alan</td>\n",
       "      <td>Reynolds</td>\n",
       "      <td>alan.reynolds.1996@gslingacademy.com</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Construction Engineer</td>\n",
       "      <td>83</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>84</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1996</td>\n",
       "      <td>1996</td>\n",
       "      <td>1997</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>thomas.gilbert.1997@gslingacademy.com</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>89</td>\n",
       "      <td>65</td>\n",
       "      <td>73</td>\n",
       "      <td>80</td>\n",
       "      <td>87</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1997</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Madison</td>\n",
       "      <td>Cross</td>\n",
       "      <td>madison.cross.1998@gslingacademy.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>97</td>\n",
       "      <td>85</td>\n",
       "      <td>63</td>\n",
       "      <td>93</td>\n",
       "      <td>68</td>\n",
       "      <td>94</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>1999</td>\n",
       "      <td>Brittany</td>\n",
       "      <td>Compton</td>\n",
       "      <td>brittany.compton.1999@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "      <td>10.0</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Business Owner</td>\n",
       "      <td>51</td>\n",
       "      <td>96</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1999</td>\n",
       "      <td>1999</td>\n",
       "      <td>2000</td>\n",
       "      <td>Natalie</td>\n",
       "      <td>Smith</td>\n",
       "      <td>natalie.smith.2000@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Accountant</td>\n",
       "      <td>82</td>\n",
       "      <td>99</td>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "      <td>83</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0.1  Unnamed: 0    id first_name last_name  \\\n",
       "0                0           0     1       Paul     Casey   \n",
       "1                1           1     2   Danielle  Sandoval   \n",
       "2                2           2     3       Tina   Andrews   \n",
       "3                3           3     4       Tara     Clark   \n",
       "4                4           4     5    Anthony    Campos   \n",
       "...            ...         ...   ...        ...       ...   \n",
       "1995          1995        1995  1996       Alan  Reynolds   \n",
       "1996          1996        1996  1997     Thomas   Gilbert   \n",
       "1997          1997        1997  1998    Madison     Cross   \n",
       "1998          1998        1998  1999   Brittany   Compton   \n",
       "1999          1999        1999  2000    Natalie     Smith   \n",
       "\n",
       "                                        email  gender  part_time_job  \\\n",
       "0              paul.casey.1@gslingacademy.com    male          False   \n",
       "1       danielle.sandoval.2@gslingacademy.com  female          False   \n",
       "2            tina.andrews.3@gslingacademy.com  female          False   \n",
       "3              tara.clark.4@gslingacademy.com  female          False   \n",
       "4          anthony.campos.5@gslingacademy.com    male          False   \n",
       "...                                       ...     ...            ...   \n",
       "1995     alan.reynolds.1996@gslingacademy.com    male          False   \n",
       "1996    thomas.gilbert.1997@gslingacademy.com    male          False   \n",
       "1997     madison.cross.1998@gslingacademy.com     NaN          False   \n",
       "1998  brittany.compton.1999@gslingacademy.com  female           True   \n",
       "1999     natalie.smith.2000@gslingacademy.com  female          False   \n",
       "\n",
       "      absence_days  extracurricular_activities  weekly_self_study_hours  \\\n",
       "0              3.0                       False                     27.0   \n",
       "1              2.0                       False                     47.0   \n",
       "2              9.0                        True                     13.0   \n",
       "3              5.0                       False                      3.0   \n",
       "4              5.0                       False                     10.0   \n",
       "...            ...                         ...                      ...   \n",
       "1995           2.0                       False                     30.0   \n",
       "1996           2.0                       False                     20.0   \n",
       "1997           5.0                       False                     14.0   \n",
       "1998          10.0                        True                      5.0   \n",
       "1999           5.0                       False                     27.0   \n",
       "\n",
       "          career_aspiration  math_score  history_score  physics_score  \\\n",
       "0                    Lawyer          73             81             93   \n",
       "1                    Doctor          90             86             96   \n",
       "2        Government Officer          81             97             95   \n",
       "3                    Artist          71             74             88   \n",
       "4                   Unknown          84             77             65   \n",
       "...                     ...         ...            ...            ...   \n",
       "1995  Construction Engineer          83             77             84   \n",
       "1996      Software Engineer          89             65             73   \n",
       "1997      Software Engineer          97             85             63   \n",
       "1998         Business Owner          51             96             72   \n",
       "1999             Accountant          82             99             91   \n",
       "\n",
       "      chemistry_score  biology_score  english_score  geography_score  \n",
       "0                  97             63             80               87  \n",
       "1                 100             90             88               90  \n",
       "2                  96             65             77               94  \n",
       "3                  80             89             63               86  \n",
       "4                  65             80             74               76  \n",
       "...               ...            ...            ...              ...  \n",
       "1995               73             75             84               82  \n",
       "1996               80             87             67               73  \n",
       "1997               93             68             94               78  \n",
       "1998               89             95             88               75  \n",
       "1999               69             83             93              100  \n",
       "\n",
       "[2000 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# This is an unclean file of students in your class\n",
    "# named 'studentdata.csv'\n",
    "\n",
    "# load the csv file\n",
    "file = pd.read_csv('studentdata.csv')\n",
    "\n",
    "# create a pandas dataframe\n",
    "df = pd.DataFrame(file)\n",
    "\n",
    "# display the csv file\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af95580-d950-4952-8512-4240527c4ff3",
   "metadata": {},
   "source": [
    "# cleaning the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c07b029-e861-4fa7-9f16-d609a970df86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we will start off with cleaning the DataFrame\n",
    "\n",
    "\n",
    "# as you can see, there are two columns that have no point of being there. Lets delete them\n",
    "\n",
    "# we use axis to specify the axis: axis = 1 for columns and axis = 0 for rows | inplace = True allows changes to be saved\n",
    "df.drop('Unnamed: 0.1', axis = 1, inplace = True)\n",
    "\n",
    "# similarly delete the second column\n",
    "df.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "\n",
    "# this is how to rename columns of a df if you need to\n",
    "df.rename(columns = {'id': 'roll_no'}, inplace=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d37da38f-424d-4084-aec5-f15d498b5a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the column 'math_score'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# drop the last row\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bf1ae0c-fc60-4cca-8808-c654635e71f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this sets the index to any column that you specify\n",
    "df.set_index('roll_no', inplace =True)\n",
    "\n",
    "# this resets the indexes to their default state\n",
    "df.reset_index(inplace = True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "714a134a-18b7-406d-b07e-5d2ae64a58a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the roll numbers as the indices and display them\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5bccbf6-7888-4926-be22-80b56ff7c47c",
   "metadata": {},
   "source": [
    "# null values\n",
    "a dataframe might contain null or missing values\n",
    "these values cause alot of problems when performing mathematical operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da3a2764-b1c5-492c-bb10-81453da545fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is how you can check if a df contains null values\n",
    "df.isna()\n",
    "\n",
    "# this gives the sum of all null values present in the df\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16cab2d9-bda7-4f6c-b6e8-adfc8a6632a5",
   "metadata": {},
   "source": [
    "# removing null values:\n",
    "there are various ways to remove null values in a df,\n",
    "we will discuss three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "079617fc-483f-493c-9c1a-55ccb45e190b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fillna has alot of options, we will discuss three: 'bfill', 'ffill', 'mean'\n",
    "\n",
    "# ffill replaces the value with the anteceding value in the same column\n",
    "df['example_column'].fillna(method = 'ffill', inplace = True)\n",
    "\n",
    "# bfill replaces the value with the preceding value in the same column\n",
    "df['example_column'].fillna(method = 'bfill', inplace = True)\n",
    "\n",
    "# sometimes we require null values to be filled by the mean\n",
    "\n",
    "# here is how to calculate the mean:\n",
    "mean = df['example_column'].mean()\n",
    "# we will then fill the nan values with the mean\n",
    "df['example_column'].fillna(mean, inplace = True)\n",
    "\n",
    "# checking for null vals\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea5ec84a-39ef-4a85-8090-e011a1799ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the nan values from the column 'absence_days' with the sum of its preceding and anteceding values\n",
    "\n",
    "\n",
    "\n",
    "# do the same for the remaining columns with null vals\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53276f82-aa04-47eb-9d44-f4e0c9a7c8ce",
   "metadata": {},
   "source": [
    "# data retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f99447ef-a717-4879-b302-77aa7ab47a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this will give the count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum values for each numeric column in the DataFrame.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d579077-2d5a-48e6-83f8-df56d5c243cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this will give us a concise summary of the data inside the df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66619fba-4e0f-40ef-a126-06f295a2fb5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# displays the first five rows of the frame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab2a0828-5e29-4dfb-b164-c77a5a3764d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# displays the last five rows of the frame\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "045c32c1-77f8-4a06-9025-543faabd4b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# iloc retrieves data from the specified DEFAULT INDEX NUMBER\n",
    "df.iloc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "424d05c0-d12e-47bc-b29a-043fa3eaf2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loc retrieves data from the specified USER-SET INDEX\n",
    "df.loc[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc78b59c-42eb-4725-a424-23a7e2263d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# condition based retrieval\n",
    "absence = df[df['absence_days'] > 3]\n",
    "\n",
    "female = df[df['gender'] == 'female']\n",
    "\n",
    "# returns data frames "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bab513bc-faf6-4cf5-9f63-293d4179479c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# locate the student with the roll number '39'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# locate 'Anthony Smith' and retrieve his data\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f20bf8-7bd2-48f7-b65e-c1f00d960fa2",
   "metadata": {},
   "source": [
    "# grouping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b322af38-ac8c-40d3-9aa7-8a2acda3eb6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can group data on common characteristics using the group by function\n",
    "\n",
    "group = df.groupby('career_aspiration')\n",
    "group.get_group('Lawyer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c81847b4-5237-4b9f-a188-dca48e677841",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the groupby funciton calculate the total absence days of students that aspire to be Doctors\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57b52c1-87dd-47e0-b239-ff142555d877",
   "metadata": {},
   "source": [
    "## mathematical ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b286e50-773f-46d4-a450-8e391f69d999",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this gives the sum of all the columns in the df\n",
    "# notice that it will return non-integer columns as a concatenation as there is no way to add string/char/bool values\n",
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c39424c-6294-464f-b08a-b17b9e8fd47a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# return the sum of the values in the specified column\n",
    "df['weekly_self_study_hours'].sum()\n",
    "\n",
    "# find the total number of absent days:\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c43cb8d3-bfde-41f8-9b80-12ab45464fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding values to a column\n",
    "df['absence_days'] += 1\n",
    "\n",
    "# subtracting values from the entire column\n",
    "df['absence_days'] -= 1\n",
    "\n",
    "# multiplies the column with a scalar\n",
    "df['absence_days'] *= 10\n",
    "\n",
    "# divides the column with a scalar\n",
    "df['absence_days'] /= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0684ce4e-c498-453d-b379-8b6ed1a12d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# consider a student with the roll number '1174' whose attendance was marked incorrectly, fix the attendance by\n",
    "# subtracting 3 from the column 'absence_days'\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6d06d71-96c4-45e5-ba50-0939db848dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# return the mean of the specified column\n",
    "df['absence_days'].mean()\n",
    "\n",
    "# returns the mode\n",
    "df['absence_days'].mode()\n",
    "\n",
    "# returns the median \n",
    "df['absence_days'].median()\n",
    "\n",
    "# returns the max value of the column\n",
    "df['absence_days'].max()\n",
    "\n",
    "# returns the minimum value of the column\n",
    "df['absence_days'].min()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5dd498d-e0e7-4adb-907b-f4d44da809cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the average marks scored in geography class and scale the scores of all students by a factor of 1.7\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# find the student with the highest score in biology class\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8bd36e-233d-48af-b1ba-201d1b64ee41",
   "metadata": {},
   "source": [
    "# interquartile range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240287dc-bc5e-4e1a-b48b-4fc67dbdfd25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting the quantile values from the column 'absence days'\n",
    "q1 = df['absence_days'].quantile(0.25)\n",
    "q3 = df['absence_days'].quantile(0.75)\n",
    "\n",
    "# find the IQR and display it\n",
    "IQR = q3 - q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8363657f-0c42-462c-bac8-4a45586f0f12",
   "metadata": {},
   "source": [
    "# -> data visualization\n",
    "we can visualize data using matplotlib, a popular data visualization library in python. It offers a multitude of 2D and 3D visualization functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c9e95ee-0fcd-403b-8d91-6495a3fac52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the library from system | assigning it to 'plt' makes it easier to type \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e75147-1365-4518-b8e3-bc7b04eb629b",
   "metadata": {},
   "source": [
    "# bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d4ec85c7-34ec-4225-aba1-da392b5a531c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar graph (vertical)\n",
    "short = df.head()\n",
    "\n",
    "plt.bar(short['first_name'], short['biology_score'], color = 'r')\n",
    "plt.xlabel('names')\n",
    "plt.ylabel('score')\n",
    "plt.title('biology score of students')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b33ed1a7-30c9-4723-a4b4-b514061cc23f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar graph (horizontal)\n",
    "short = df.head()\n",
    "\n",
    "plt.barh(short['first_name'], short['biology_score'], color = 'b')\n",
    "plt.xlabel('names')\n",
    "plt.ylabel('score')\n",
    "plt.title('biology score of students')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "44a6ba24-e952-4932-9ca3-81a816cbca5d",
   "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>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>part_time_job</th>\n",
       "      <th>absence_days</th>\n",
       "      <th>extracurricular_activities</th>\n",
       "      <th>weekly_self_study_hours</th>\n",
       "      <th>career_aspiration</th>\n",
       "      <th>math_score</th>\n",
       "      <th>history_score</th>\n",
       "      <th>physics_score</th>\n",
       "      <th>chemistry_score</th>\n",
       "      <th>biology_score</th>\n",
       "      <th>english_score</th>\n",
       "      <th>geography_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Casey</td>\n",
       "      <td>paul.casey.1@gslingacademy.com</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>73</td>\n",
       "      <td>81</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Danielle</td>\n",
       "      <td>Sandoval</td>\n",
       "      <td>danielle.sandoval.2@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Tina</td>\n",
       "      <td>Andrews</td>\n",
       "      <td>tina.andrews.3@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Government Officer</td>\n",
       "      <td>81</td>\n",
       "      <td>97</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Tara</td>\n",
       "      <td>Clark</td>\n",
       "      <td>tara.clark.4@gslingacademy.com</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Artist</td>\n",
       "      <td>71</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>89</td>\n",
       "      <td>63</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Campos</td>\n",
       "      <td>anthony.campos.5@gslingacademy.com</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>84</td>\n",
       "      <td>77</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0  id first_name last_name  \\\n",
       "0             0           0   1       Paul     Casey   \n",
       "1             1           1   2   Danielle  Sandoval   \n",
       "2             2           2   3       Tina   Andrews   \n",
       "3             3           3   4       Tara     Clark   \n",
       "4             4           4   5    Anthony    Campos   \n",
       "\n",
       "                                   email  gender  part_time_job  absence_days  \\\n",
       "0         paul.casey.1@gslingacademy.com    male          False           3.0   \n",
       "1  danielle.sandoval.2@gslingacademy.com  female          False           2.0   \n",
       "2       tina.andrews.3@gslingacademy.com  female          False           9.0   \n",
       "3         tara.clark.4@gslingacademy.com  female          False           5.0   \n",
       "4     anthony.campos.5@gslingacademy.com    male          False           5.0   \n",
       "\n",
       "   extracurricular_activities  weekly_self_study_hours   career_aspiration  \\\n",
       "0                       False                     27.0              Lawyer   \n",
       "1                       False                     47.0              Doctor   \n",
       "2                        True                     13.0  Government Officer   \n",
       "3                       False                      3.0              Artist   \n",
       "4                       False                     10.0             Unknown   \n",
       "\n",
       "   math_score  history_score  physics_score  chemistry_score  biology_score  \\\n",
       "0          73             81             93               97             63   \n",
       "1          90             86             96              100             90   \n",
       "2          81             97             95               96             65   \n",
       "3          71             74             88               80             89   \n",
       "4          84             77             65               65             80   \n",
       "\n",
       "   english_score  geography_score  \n",
       "0             80               87  \n",
       "1             88               90  \n",
       "2             77               94  \n",
       "3             63               86  \n",
       "4             74               76  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c622bd-7307-4957-a125-034fa7715c00",
   "metadata": {},
   "source": [
    "# pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "32aab515-97c2-4751-89f0-6990b58013d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(short['biology_score'], labels = short['first_name'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "583e598a-a3a9-4cf4-a31e-0b5291f45028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 0., 0., 0., 0., 0., 1., 0., 0., 2.]),\n",
       " array([63. , 65.7, 68.4, 71.1, 73.8, 76.5, 79.2, 81.9, 84.6, 87.3, 90. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.hist(short['biology_score']./,)#, labels= short['first_name'])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}