{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1Ejl0E1kN-_nyrvnugujV1cBVnT3rDGdz","authorship_tag":"ABX9TyOEUU1RaxEslwdc8McS6Fyn"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Discussion 1\n","\n","**11 Sept 2023**\n","\n","This notebook contains the questions and the solutions to the topics covered in Discussion 1."],"metadata":{"id":"Gmr_SvOaratM"}},{"cell_type":"code","source":["#importing libraries\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt"],"metadata":{"id":"1zgC9e8IraZJ","executionInfo":{"status":"ok","timestamp":1694896141260,"user_tz":240,"elapsed":569,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":["### Review of numpy"],"metadata":{"id":"IdeqMsIYrowG"}},{"cell_type":"code","source":["#create a numpy array with elements from 1 to 5\n","arr1 = np.array([1,2,3,4,5])\n","arr1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-_cUDAxwraW6","executionInfo":{"status":"ok","timestamp":1694896143161,"user_tz":240,"elapsed":4,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"e1373ddf-ed8e-4b38-9e0a-2b2823fc766b"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 2, 3, 4, 5])"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["#create an array with elements from 0 to 10\n","arr2 = np.array(range(11))\n","arr2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8n7ZtM3DvD1q","executionInfo":{"status":"ok","timestamp":1694896147709,"user_tz":240,"elapsed":183,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"ec97af78-7c93-4c83-a090-bfb88cb55a1e"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["#create an array with even numbers from 0 to 18\n","arr = np.arange(0,20,2)\n","arr"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JNdHoytFvDzC","executionInfo":{"status":"ok","timestamp":1694896149657,"user_tz":240,"elapsed":148,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"a0fbb5a2-7507-4c83-fec7-e422952b19e7"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["#create an array with 10 equally spaced elements from 0 to 6\n","arr = np.linspace(0,6,10)\n","arr"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kq66BPHsvDxK","executionInfo":{"status":"ok","timestamp":1694896150903,"user_tz":240,"elapsed":163,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"830ab2d1-6cdc-4adc-a238-77cbfd7dcbd6"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.        , 0.66666667, 1.33333333, 2.        , 2.66666667,\n","       3.33333333, 4.        , 4.66666667, 5.33333333, 6.        ])"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["**Creating a 2D matrix as follows:**\n","\n","[[1,2,3]\n","\n","[4,5,6]\n","\n","[7,8,9]]"],"metadata":{"id":"wg8OhUqkw-tL"}},{"cell_type":"code","source":["matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])\n","matrix"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gFRU9YogvDuo","executionInfo":{"status":"ok","timestamp":1694896168143,"user_tz":240,"elapsed":160,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"497134d2-3142-48ca-cd9b-5ed5bfb291d1"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1, 2, 3],\n","       [4, 5, 6],\n","       [7, 8, 9]])"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["matrix.T"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kAuovfxOvDsS","executionInfo":{"status":"ok","timestamp":1694896170784,"user_tz":240,"elapsed":155,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"cbd321ea-43f0-4831-dc1b-af6f3b8c28db"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1, 4, 7],\n","       [2, 5, 8],\n","       [3, 6, 9]])"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["data = np.random.randint(1,101,(5,5))"],"metadata":{"id":"iCbzq9aVvDqL","executionInfo":{"status":"ok","timestamp":1694896170943,"user_tz":240,"elapsed":2,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BEXQnXI4x7u7","executionInfo":{"status":"ok","timestamp":1694896171432,"user_tz":240,"elapsed":5,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"f4cc3ccd-e0df-4aea-c9a0-a2ee170dc65e"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[68, 62,  5, 37, 93],\n","       [31, 38, 60, 47, 72],\n","       [78, 93, 87, 90, 78],\n","       [92, 11, 27, 20, 26],\n","       [93, 63, 41, 44, 58]])"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["**Problem 1: Calculate the sum, mean, median and standard deviation of 'data'.**"],"metadata":{"id":"A9waNZ_urzrg"}},{"cell_type":"code","source":["#problem 1\n","#sum, mean, median, std dev\n","mean_data = np.mean(data)\n","sum_data = np.sum(data)\n","median_data = np.median(data)\n","std_data = np.std(data)\n","\n","print(mean_data)\n","print(sum_data)\n","print(median_data)\n","print(std_data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gGT-DydjvDnw","executionInfo":{"status":"ok","timestamp":1694896237208,"user_tz":240,"elapsed":176,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"58dbaa68-63fc-49c9-8261-e11155636c02"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["56.56\n","1414\n","60.0\n","27.13533489750956\n"]}]},{"cell_type":"code","source":["#finding the dot product of 2 random 5x5 arrays\n","arr1 = np.random.rand(5,5)\n","arr2 = np.random.rand(5,5)"],"metadata":{"id":"gM_-B9KAvDi5","executionInfo":{"status":"ok","timestamp":1694896257688,"user_tz":240,"elapsed":149,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["#method 1\n","print(arr1.dot(arr2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-ktr6LAovDgm","executionInfo":{"status":"ok","timestamp":1694896261760,"user_tz":240,"elapsed":156,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"82b599b5-7e31-4b6e-926f-6bb36c2d4909"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.84130372 0.51712601 0.70269174 0.98516978 0.6591656 ]\n"," [1.48246524 1.40536053 1.83921681 1.81719543 0.77959355]\n"," [2.05402451 1.39425896 1.78866475 1.92846364 1.62198328]\n"," [1.98210725 1.63396388 1.71610661 2.17720699 1.12929985]\n"," [0.89137678 0.72233889 1.02014441 0.98467356 0.69416711]]\n"]}]},{"cell_type":"code","source":["#method 2\n","print(arr1@arr2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N9BCnYqlvDek","executionInfo":{"status":"ok","timestamp":1694896264800,"user_tz":240,"elapsed":142,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"d6f33492-81e6-457f-dc7e-a55300849ff0"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.84130372 0.51712601 0.70269174 0.98516978 0.6591656 ]\n"," [1.48246524 1.40536053 1.83921681 1.81719543 0.77959355]\n"," [2.05402451 1.39425896 1.78866475 1.92846364 1.62198328]\n"," [1.98210725 1.63396388 1.71610661 2.17720699 1.12929985]\n"," [0.89137678 0.72233889 1.02014441 0.98467356 0.69416711]]\n"]}]},{"cell_type":"code","source":["#method 3\n","print(np.dot(arr1, arr2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GE0qd4ECvDcG","executionInfo":{"status":"ok","timestamp":1694896269528,"user_tz":240,"elapsed":191,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"aa330606-75f9-4c12-f42e-a56b15052688"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.84130372 0.51712601 0.70269174 0.98516978 0.6591656 ]\n"," [1.48246524 1.40536053 1.83921681 1.81719543 0.77959355]\n"," [2.05402451 1.39425896 1.78866475 1.92846364 1.62198328]\n"," [1.98210725 1.63396388 1.71610661 2.17720699 1.12929985]\n"," [0.89137678 0.72233889 1.02014441 0.98467356 0.69416711]]\n"]}]},{"cell_type":"markdown","source":["**Problem 2: Calculate the mean of 10 elements from the matrix called 'populatiion' defined below without replacement.**"],"metadata":{"id":"iUjJ2daGsLKA"}},{"cell_type":"code","source":["population = np.arange(1,101)"],"metadata":{"id":"Hya47DxKvDZx","executionInfo":{"status":"ok","timestamp":1694896309840,"user_tz":240,"elapsed":160,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["#problem 2\n","#calculate the mean of 10 elements from population (without replacement)\n","\n","\n","#it is a good idea to break the problem in parts\n","\n","#part 1 is to first take a random sample\n","random_sample = np.random.choice(population, 10, replace=False)"],"metadata":{"id":"tU5AWwZcvDXl","executionInfo":{"status":"ok","timestamp":1694896328930,"user_tz":240,"elapsed":3,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["#part 2 is to find the mean of that sample\n","mean_sample = np.mean(random_sample)\n","mean_sample"],"metadata":{"id":"ioRPUIDFvDVW","executionInfo":{"status":"ok","timestamp":1694896342458,"user_tz":240,"elapsed":3,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"d51b785c-c11d-44bf-ad2c-54b181c842a8"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["57.9"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["## Review of pandas"],"metadata":{"id":"0S3JHJKWsdcZ"}},{"cell_type":"code","source":["#reading the csv file (available on Piazza)\n","df = pd.read_csv('INSERT THE PATH TO THE FILE HERE/SuperheroDataset.csv')"],"metadata":{"id":"lAd8OI63vDQt","executionInfo":{"status":"ok","timestamp":1694896377616,"user_tz":240,"elapsed":417,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":637},"id":"eJpxBgBHvDOb","executionInfo":{"status":"ok","timestamp":1694896386785,"user_tz":240,"elapsed":155,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"5c0ecb60-602d-4348-faad-90201a5b827d"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0.1  Unnamed: 0         Name                  Url  Intelligence  \\\n","0             0           0      3-D Man     /3-d-man/10-226/          80.0   \n","1             1           1       A-Bomb    /a-bomb/10-10060/          75.0   \n","2             2           2   Abe Sapien  /abe-sapien/10-956/          95.0   \n","3             3           3     Abin Sur   /abin-sur/10-1460/          80.0   \n","4             4           4  Abomination   /abomination/10-1/          85.0   \n","\n","   Strength  Speed  Durability  Power  Combat  ...         Height  \\\n","0      35.0   45.0        35.0   25.0    55.0  ...  6'2 // 188 cm   \n","1     100.0   20.0        80.0   25.0    65.0  ...  6'8 // 203 cm   \n","2      30.0   35.0        65.0  100.0    85.0  ...  6'3 // 191 cm   \n","3      90.0   55.0        65.0  100.0    65.0  ...  6'1 // 185 cm   \n","4      80.0   55.0        90.0   65.0    95.0  ...  6'8 // 203 cm   \n","\n","             Weight Eye color Hair color  \\\n","0   200 lb // 90 kg     Brown       Grey   \n","1  980 lb // 441 kg    Yellow    No Hair   \n","2   145 lb // 65 kg      Blue    No Hair   \n","3   200 lb // 90 kg      Blue    No Hair   \n","4  980 lb // 441 kg     Green    No Hair   \n","\n","                                          Occupation    Base  \\\n","0                             Test pilot, adventurer       -   \n","1  Musician, adventurer, author; formerly talk sh...       -   \n","2                            Paranormal Investigator       -   \n","3            Green Lantern, former history professor      Oa   \n","4                                             Ex-Spy  Mobile   \n","\n","                                    Team Affiliation  \\\n","0    Agents of Atlas, Asgardians, Formerly: Avengers   \n","1  Teen Brigade (Leader), Ultimate Fantastic Four...   \n","2         Bureau for Paranormal Research and Defense   \n","3  Legion of Super-Heroes, Formerly: Green Lanter...   \n","4  Annihilators (Leader), Wrecking Crew, Masters ...   \n","\n","                                           Relatives Skin color Total Power  \n","0  Hal Chandler (brother), Peggy Clark (sister-in...        NaN       275.0  \n","1  Marlo Chandler-Jones (wife); Polly (aunt); Mrs...        NaN       365.0  \n","2                      Edith Howard (wife, deceased)       Blue       410.0  \n","3  Amon Sur (son), Arin Sur (sister), Thaal Sines...        Red       455.0  \n","4            Nadia Dornova Blonsky (wife, separated)        NaN       470.0  \n","\n","[5 rows x 29 columns]"],"text/html":["\n","  <div id=\"df-bf3a0045-56ef-426d-91c3-ff865664812a\" class=\"colab-df-container\">\n","    <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>Name</th>\n","      <th>Url</th>\n","      <th>Intelligence</th>\n","      <th>Strength</th>\n","      <th>Speed</th>\n","      <th>Durability</th>\n","      <th>Power</th>\n","      <th>Combat</th>\n","      <th>...</th>\n","      <th>Height</th>\n","      <th>Weight</th>\n","      <th>Eye color</th>\n","      <th>Hair color</th>\n","      <th>Occupation</th>\n","      <th>Base</th>\n","      <th>Team Affiliation</th>\n","      <th>Relatives</th>\n","      <th>Skin color</th>\n","      <th>Total Power</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3-D Man</td>\n","      <td>/3-d-man/10-226/</td>\n","      <td>80.0</td>\n","      <td>35.0</td>\n","      <td>45.0</td>\n","      <td>35.0</td>\n","      <td>25.0</td>\n","      <td>55.0</td>\n","      <td>...</td>\n","      <td>6'2 // 188 cm</td>\n","      <td>200 lb // 90 kg</td>\n","      <td>Brown</td>\n","      <td>Grey</td>\n","      <td>Test pilot, adventurer</td>\n","      <td>-</td>\n","      <td>Agents of Atlas, Asgardians, Formerly: Avengers</td>\n","      <td>Hal Chandler (brother), Peggy Clark (sister-in...</td>\n","      <td>NaN</td>\n","      <td>275.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>A-Bomb</td>\n","      <td>/a-bomb/10-10060/</td>\n","      <td>75.0</td>\n","      <td>100.0</td>\n","      <td>20.0</td>\n","      <td>80.0</td>\n","      <td>25.0</td>\n","      <td>65.0</td>\n","      <td>...</td>\n","      <td>6'8 // 203 cm</td>\n","      <td>980 lb // 441 kg</td>\n","      <td>Yellow</td>\n","      <td>No Hair</td>\n","      <td>Musician, adventurer, author; formerly talk sh...</td>\n","      <td>-</td>\n","      <td>Teen Brigade (Leader), Ultimate Fantastic Four...</td>\n","      <td>Marlo Chandler-Jones (wife); Polly (aunt); Mrs...</td>\n","      <td>NaN</td>\n","      <td>365.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>Abe Sapien</td>\n","      <td>/abe-sapien/10-956/</td>\n","      <td>95.0</td>\n","      <td>30.0</td>\n","      <td>35.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>85.0</td>\n","      <td>...</td>\n","      <td>6'3 // 191 cm</td>\n","      <td>145 lb // 65 kg</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Paranormal Investigator</td>\n","      <td>-</td>\n","      <td>Bureau for Paranormal Research and Defense</td>\n","      <td>Edith Howard (wife, deceased)</td>\n","      <td>Blue</td>\n","      <td>410.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>Abin Sur</td>\n","      <td>/abin-sur/10-1460/</td>\n","      <td>80.0</td>\n","      <td>90.0</td>\n","      <td>55.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>65.0</td>\n","      <td>...</td>\n","      <td>6'1 // 185 cm</td>\n","      <td>200 lb // 90 kg</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Green Lantern, former history professor</td>\n","      <td>Oa</td>\n","      <td>Legion of Super-Heroes, Formerly: Green Lanter...</td>\n","      <td>Amon Sur (son), Arin Sur (sister), Thaal Sines...</td>\n","      <td>Red</td>\n","      <td>455.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>Abomination</td>\n","      <td>/abomination/10-1/</td>\n","      <td>85.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>90.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>...</td>\n","      <td>6'8 // 203 cm</td>\n","      <td>980 lb // 441 kg</td>\n","      <td>Green</td>\n","      <td>No Hair</td>\n","      <td>Ex-Spy</td>\n","      <td>Mobile</td>\n","      <td>Annihilators (Leader), Wrecking Crew, Masters ...</td>\n","      <td>Nadia Dornova Blonsky (wife, separated)</td>\n","      <td>NaN</td>\n","      <td>470.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 29 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf3a0045-56ef-426d-91c3-ff865664812a')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-bf3a0045-56ef-426d-91c3-ff865664812a button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Replacing the NaN values with 0"],"metadata":{"id":"tPQxEnl6su9V"}},{"cell_type":"code","source":["#printing column names for easier accessibility\n","column_names = df.columns\n","print(column_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"re_a0pyqvDMZ","executionInfo":{"status":"ok","timestamp":1694896471864,"user_tz":240,"elapsed":183,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"627041e7-d84d-4107-cdbd-06f96a89ac18"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['Unnamed: 0.1', 'Unnamed: 0', 'Name', 'Url', 'Intelligence', 'Strength',\n","       'Speed', 'Durability', 'Power', 'Combat', 'Full name', 'Alter Egos',\n","       'Aliases', 'Place of birth', 'First appearance', 'Creator', 'Alignment',\n","       'Gender', 'Race', 'Height', 'Weight', 'Eye color', 'Hair color',\n","       'Occupation', 'Base', 'Team Affiliation', 'Relatives', 'Skin color',\n","       'Total Power'],\n","      dtype='object')\n"]}]},{"cell_type":"code","source":["cols_to_drop = ['Unnamed: 0.1', 'Unnamed: 0','Url', 'Full name', 'Alter Egos',\n","       'Aliases', 'Place of birth', 'First appearance','Height', 'Weight','Occupation', 'Base', 'Team Affiliation', 'Relatives']\n","\n","df = df.drop(columns = cols_to_drop)"],"metadata":{"id":"XcoNoUl3vDJ_","executionInfo":{"status":"ok","timestamp":1694896498698,"user_tz":240,"elapsed":180,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":258},"id":"j46CitI1vDHt","executionInfo":{"status":"ok","timestamp":1694896503323,"user_tz":240,"elapsed":6,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"120dc280-419d-486f-aaab-7fe1ce71da63"},"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          Name  Intelligence  Strength  Speed  Durability  Power  Combat  \\\n","0      3-D Man          80.0      35.0   45.0        35.0   25.0    55.0   \n","1       A-Bomb          75.0     100.0   20.0        80.0   25.0    65.0   \n","2   Abe Sapien          95.0      30.0   35.0        65.0  100.0    85.0   \n","3     Abin Sur          80.0      90.0   55.0        65.0  100.0    65.0   \n","4  Abomination          85.0      80.0   55.0        90.0   65.0    95.0   \n","\n","             Creator Alignment Gender               Race Eye color Hair color  \\\n","0      Marvel Comics      good   Male                  -     Brown       Grey   \n","1      Marvel Comics      good   Male              Human    Yellow    No Hair   \n","2  Dark Horse Comics      good   Male      Icthyo Sapien      Blue    No Hair   \n","3          DC Comics      good   Male            Ungaran      Blue    No Hair   \n","4      Marvel Comics       bad   Male  Human / Radiation     Green    No Hair   \n","\n","  Skin color  Total Power  \n","0        NaN        275.0  \n","1        NaN        365.0  \n","2       Blue        410.0  \n","3        Red        455.0  \n","4        NaN        470.0  "],"text/html":["\n","  <div id=\"df-8c0a1ce8-9785-4f6a-95ac-a3e041bf36b5\" class=\"colab-df-container\">\n","    <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>Name</th>\n","      <th>Intelligence</th>\n","      <th>Strength</th>\n","      <th>Speed</th>\n","      <th>Durability</th>\n","      <th>Power</th>\n","      <th>Combat</th>\n","      <th>Creator</th>\n","      <th>Alignment</th>\n","      <th>Gender</th>\n","      <th>Race</th>\n","      <th>Eye color</th>\n","      <th>Hair color</th>\n","      <th>Skin color</th>\n","      <th>Total Power</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3-D Man</td>\n","      <td>80.0</td>\n","      <td>35.0</td>\n","      <td>45.0</td>\n","      <td>35.0</td>\n","      <td>25.0</td>\n","      <td>55.0</td>\n","      <td>Marvel Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>-</td>\n","      <td>Brown</td>\n","      <td>Grey</td>\n","      <td>NaN</td>\n","      <td>275.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>A-Bomb</td>\n","      <td>75.0</td>\n","      <td>100.0</td>\n","      <td>20.0</td>\n","      <td>80.0</td>\n","      <td>25.0</td>\n","      <td>65.0</td>\n","      <td>Marvel Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Human</td>\n","      <td>Yellow</td>\n","      <td>No Hair</td>\n","      <td>NaN</td>\n","      <td>365.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Abe Sapien</td>\n","      <td>95.0</td>\n","      <td>30.0</td>\n","      <td>35.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>85.0</td>\n","      <td>Dark Horse Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Icthyo Sapien</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Blue</td>\n","      <td>410.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Abin Sur</td>\n","      <td>80.0</td>\n","      <td>90.0</td>\n","      <td>55.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>65.0</td>\n","      <td>DC Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Ungaran</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Red</td>\n","      <td>455.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Abomination</td>\n","      <td>85.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>90.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>Marvel Comics</td>\n","      <td>bad</td>\n","      <td>Male</td>\n","      <td>Human / Radiation</td>\n","      <td>Green</td>\n","      <td>No Hair</td>\n","      <td>NaN</td>\n","      <td>470.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c0a1ce8-9785-4f6a-95ac-a3e041bf36b5')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    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  25.0    55.0   \n","1       A-Bomb          75.0     100.0   20.0        80.0   25.0    65.0   \n","2   Abe Sapien          95.0      30.0   35.0        65.0  100.0    85.0   \n","3     Abin Sur          80.0      90.0   55.0        65.0  100.0    65.0   \n","4  Abomination          85.0      80.0   55.0        90.0   65.0    95.0   \n","\n","             Creator Alignment Gender               Race Eye color Hair color  \\\n","0      Marvel Comics      good   Male                  -     Brown       Grey   \n","1      Marvel Comics      good   Male              Human    Yellow    No Hair   \n","2  Dark Horse Comics      good   Male      Icthyo Sapien      Blue    No Hair   \n","3          DC Comics      good   Male            Ungaran      Blue    No Hair   \n","4      Marvel Comics       bad   Male  Human / Radiation     Green    No Hair   \n","\n","  Skin color  Total Power  \n","0          0        275.0  \n","1          0        365.0  \n","2       Blue        410.0  \n","3        Red        455.0  \n","4          0        470.0  "],"text/html":["\n","  <div id=\"df-023cacab-8b75-462c-95df-4f96e85a7471\" class=\"colab-df-container\">\n","    <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>Name</th>\n","      <th>Intelligence</th>\n","      <th>Strength</th>\n","      <th>Speed</th>\n","      <th>Durability</th>\n","      <th>Power</th>\n","      <th>Combat</th>\n","      <th>Creator</th>\n","      <th>Alignment</th>\n","      <th>Gender</th>\n","      <th>Race</th>\n","      <th>Eye color</th>\n","      <th>Hair color</th>\n","      <th>Skin color</th>\n","      <th>Total Power</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3-D Man</td>\n","      <td>80.0</td>\n","      <td>35.0</td>\n","      <td>45.0</td>\n","      <td>35.0</td>\n","      <td>25.0</td>\n","      <td>55.0</td>\n","      <td>Marvel Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>-</td>\n","      <td>Brown</td>\n","      <td>Grey</td>\n","      <td>0</td>\n","      <td>275.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>A-Bomb</td>\n","      <td>75.0</td>\n","      <td>100.0</td>\n","      <td>20.0</td>\n","      <td>80.0</td>\n","      <td>25.0</td>\n","      <td>65.0</td>\n","      <td>Marvel Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Human</td>\n","      <td>Yellow</td>\n","      <td>No Hair</td>\n","      <td>0</td>\n","      <td>365.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Abe Sapien</td>\n","      <td>95.0</td>\n","      <td>30.0</td>\n","      <td>35.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>85.0</td>\n","      <td>Dark Horse Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Icthyo Sapien</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Blue</td>\n","      <td>410.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Abin Sur</td>\n","      <td>80.0</td>\n","      <td>90.0</td>\n","      <td>55.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>65.0</td>\n","      <td>DC Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Ungaran</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Red</td>\n","      <td>455.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Abomination</td>\n","      <td>85.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>90.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>Marvel Comics</td>\n","      <td>bad</td>\n","      <td>Male</td>\n","      <td>Human / Radiation</td>\n","      <td>Green</td>\n","      <td>No Hair</td>\n","      <td>0</td>\n","      <td>470.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-023cacab-8b75-462c-95df-4f96e85a7471')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["**Problem 4 - Perform EDA by making any 2 plots to explore the data better.**"],"metadata":{"id":"Rk12EPGKtIJg"}},{"cell_type":"code","source":["#this can be different for everyone, here's just an example\n","#histogram for 'Total Power' distribution\n","plt.hist(df['Total Power'], bins=20, edgecolor='k', color = 'pink')\n","plt.title('Distribution of Total Power')\n","plt.xlabel('Total Power')\n","plt.ylabel('Frequency')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"MubElvLutM3y","executionInfo":{"status":"ok","timestamp":1694896727381,"user_tz":240,"elapsed":1030,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"2adf4d7d-aa10-4634-8ef3-768187ea4e94"},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#scatter plot of 'Strength' vs. 'Combat'\n","plt.scatter(df['Strength'], df['Combat'], alpha=0.5)\n","plt.title('Relationship between Strength and Combat')\n","plt.xlabel('Strength')\n","plt.ylabel('Combat')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"mKhHu5JotM1T","executionInfo":{"status":"ok","timestamp":1694896654065,"user_tz":240,"elapsed":485,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"e41d8f4f-32bf-49d5-8350-1e20ee2970a4"},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["**Problem 5: Make a separate column called 'avg' in the same dataframe consisting of average of intelligence, strength, speed, durability, power and combat.**"],"metadata":{"id":"ppvsy7ectpOj"}},{"cell_type":"code","source":["df['avg'] = df[['Intelligence', 'Strength', 'Speed', 'Durability', 'Power', 'Combat']].mean(axis=1)\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":293},"id":"hjDhIltItMs-","executionInfo":{"status":"ok","timestamp":1694896681001,"user_tz":240,"elapsed":178,"user":{"displayName":"Navya Jain","userId":"15572204226898981846"}},"outputId":"bab2fd49-65b4-4a70-9558-ef3fb77117a8"},"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          Name  Intelligence  Strength  Speed  Durability  Power  Combat  \\\n","0      3-D Man          80.0      35.0   45.0        35.0   25.0    55.0   \n","1       A-Bomb          75.0     100.0   20.0        80.0   25.0    65.0   \n","2   Abe Sapien          95.0      30.0   35.0        65.0  100.0    85.0   \n","3     Abin Sur          80.0      90.0   55.0        65.0  100.0    65.0   \n","4  Abomination          85.0      80.0   55.0        90.0   65.0    95.0   \n","\n","             Creator Alignment Gender               Race Eye color Hair color  \\\n","0      Marvel Comics      good   Male                  -     Brown       Grey   \n","1      Marvel Comics      good   Male              Human    Yellow    No Hair   \n","2  Dark Horse Comics      good   Male      Icthyo Sapien      Blue    No Hair   \n","3          DC Comics      good   Male            Ungaran      Blue    No Hair   \n","4      Marvel Comics       bad   Male  Human / Radiation     Green    No Hair   \n","\n","  Skin color  Total Power        avg  \n","0          0        275.0  45.833333  \n","1          0        365.0  60.833333  \n","2       Blue        410.0  68.333333  \n","3        Red        455.0  75.833333  \n","4          0        470.0  78.333333  "],"text/html":["\n","  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  <th>0</th>\n","      <td>3-D Man</td>\n","      <td>80.0</td>\n","      <td>35.0</td>\n","      <td>45.0</td>\n","      <td>35.0</td>\n","      <td>25.0</td>\n","      <td>55.0</td>\n","      <td>Marvel Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>-</td>\n","      <td>Brown</td>\n","      <td>Grey</td>\n","      <td>0</td>\n","      <td>275.0</td>\n","      <td>45.833333</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>A-Bomb</td>\n","      <td>75.0</td>\n","      <td>100.0</td>\n","      <td>20.0</td>\n","      <td>80.0</td>\n","      <td>25.0</td>\n","      <td>65.0</td>\n","      <td>Marvel Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Human</td>\n","      <td>Yellow</td>\n","      <td>No Hair</td>\n","      <td>0</td>\n","      <td>365.0</td>\n","      <td>60.833333</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Abe Sapien</td>\n","      <td>95.0</td>\n","      <td>30.0</td>\n","      <td>35.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>85.0</td>\n","      <td>Dark Horse Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Icthyo Sapien</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Blue</td>\n","      <td>410.0</td>\n","      <td>68.333333</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Abin Sur</td>\n","      <td>80.0</td>\n","      <td>90.0</td>\n","      <td>55.0</td>\n","      <td>65.0</td>\n","      <td>100.0</td>\n","      <td>65.0</td>\n","      <td>DC Comics</td>\n","      <td>good</td>\n","      <td>Male</td>\n","      <td>Ungaran</td>\n","      <td>Blue</td>\n","      <td>No Hair</td>\n","      <td>Red</td>\n","      <td>455.0</td>\n","      <td>75.833333</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Abomination</td>\n","      <td>85.0</td>\n","      <td>80.0</td>\n","      <td>55.0</td>\n","      <td>90.0</td>\n","      <td>65.0</td>\n","      <td>95.0</td>\n","      <td>Marvel Comics</td>\n","      <td>bad</td>\n","      <td>Male</td>\n","      <td>Human / Radiation</td>\n","      <td>Green</td>\n","      <td>No Hair</td>\n","      <td>0</td>\n","      <td>470.0</td>\n","      <td>78.333333</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16c9ec8c-a991-4aa2-bcf5-de7000488bdf')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  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