trainity-data-analyst-python-master-class-7.ipynb

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"}}},{"cell_type":"code","source":"import 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   2\nc    3\nd    4\ne    5\nf    6\ng    7\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"series_2.iloc[0:10:2] ## Odd values","metadata":{"execution":{"iopub.status.busy":"2024-06-14T05:54:57.922218Z","iopub.execute_input":"2024-06-14T05:54:57.922623Z","iopub.status.idle":"2024-06-14T05:54:57.934135Z","shell.execute_reply.started":"2024-06-14T05:54:57.922592Z","shell.execute_reply":"2024-06-14T05:54:57.933080Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"a    1\nc    3\ne    5\ng    7\ni    9\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"series_2.iloc[1:10:2] ## Even 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int64"},"metadata":{}}]},{"cell_type":"code","source":"!kaggle datasets download -d yasserh/bmidataset","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:00:24.270785Z","iopub.execute_input":"2024-06-14T06:00:24.271784Z","iopub.status.idle":"2024-06-14T06:00:26.257022Z","shell.execute_reply.started":"2024-06-14T06:00:24.271745Z","shell.execute_reply":"2024-06-14T06:00:26.255591Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"Dataset URL: https://www.kaggle.com/datasets/yasserh/bmidataset\nLicense(s): CC0-1.0\nDownloading bmidataset.zip to /kaggle/working\n  0%|                                               | 0.00/2.19k [00:00<?, ?B/s]\n100%|██████████████████████████████████████| 2.19k/2.19k [00:00<00:00, 4.50MB/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]], columns = ['A','B','C'], index = ['a','b','c'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:03:36.604681Z","iopub.execute_input":"2024-06-14T06:03:36.605098Z","iopub.status.idle":"2024-06-14T06:03:36.611293Z","shell.execute_reply.started":"2024-06-14T06:03:36.605066Z","shell.execute_reply":"2024-06-14T06:03:36.610190Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:03:38.768806Z","iopub.execute_input":"2024-06-14T06:03:38.769206Z","iopub.status.idle":"2024-06-14T06:03:38.780237Z","shell.execute_reply.started":"2024-06-14T06:03:38.769171Z","shell.execute_reply":"2024-06-14T06:03:38.779096Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"   A  B  C\na  1  2  3\nb  4  5  6\nc  7  8  9","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>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>7</td>\n      <td>8</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"dict_2 = {\n    'A' : [1,2,3],\n    'B' : [4,5,6],\n    'C' : [7,8,9]\n}","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:05:28.677854Z","iopub.execute_input":"2024-06-14T06:05:28.678255Z","iopub.status.idle":"2024-06-14T06:05:28.683542Z","shell.execute_reply.started":"2024-06-14T06:05:28.678226Z","shell.execute_reply":"2024-06-14T06:05:28.682317Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"df_3 = pd.DataFrame(dict_2)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:05:42.477340Z","iopub.execute_input":"2024-06-14T06:05:42.477705Z","iopub.status.idle":"2024-06-14T06:05:42.483440Z","shell.execute_reply.started":"2024-06-14T06:05:42.477678Z","shell.execute_reply":"2024-06-14T06:05:42.482225Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"df_3","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:05:46.349966Z","iopub.execute_input":"2024-06-14T06:05:46.350386Z","iopub.status.idle":"2024-06-14T06:05:46.361671Z","shell.execute_reply.started":"2024-06-14T06:05:46.350357Z","shell.execute_reply":"2024-06-14T06:05:46.360391Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"   A  B  C\n0  1  4  7\n1  2  5  8\n2  3  6  9","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>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>4</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>5</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>6</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2 = pd.read_csv('/kaggle/working/bmidataset.zip')","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:02:33.515619Z","iopub.execute_input":"2024-06-14T06:02:33.516053Z","iopub.status.idle":"2024-06-14T06:02:33.531435Z","shell.execute_reply.started":"2024-06-14T06:02:33.515993Z","shell.execute_reply":"2024-06-14T06:02:33.530136Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"df_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:02:48.111750Z","iopub.execute_input":"2024-06-14T06:02:48.112169Z","iopub.status.idle":"2024-06-14T06:02:48.124004Z","shell.execute_reply.started":"2024-06-14T06:02:48.112137Z","shell.execute_reply":"2024-06-14T06:02:48.122767Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"   Gender  Height  Weight  Index\n0    Male     174      96      4\n1    Male     189      87      2\n2  Female     185     110      4\n3  Female     195     104      3\n4    Male     149      61      3","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>Gender</th>\n      <th>Height</th>\n      <th>Weight</th>\n      <th>Index</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>96</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>87</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>185</td>\n      <td>110</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>195</td>\n      <td>104</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>149</td>\n      <td>61</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2['Gender'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:04:14.625380Z","iopub.execute_input":"2024-06-14T06:04:14.625788Z","iopub.status.idle":"2024-06-14T06:04:14.641591Z","shell.execute_reply.started":"2024-06-14T06:04:14.625755Z","shell.execute_reply":"2024-06-14T06:04:14.640509Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"Gender\nFemale    255\nMale      245\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_2.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:04:44.314434Z","iopub.execute_input":"2024-06-14T06:04:44.315561Z","iopub.status.idle":"2024-06-14T06:04:44.333939Z","shell.execute_reply.started":"2024-06-14T06:04:44.315524Z","shell.execute_reply":"2024-06-14T06:04:44.332725Z"},"trusted":true},"execution_count":55,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 500 entries, 0 to 499\nData columns (total 4 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   Gender  500 non-null    object\n 1   Height  500 non-null    int64 \n 2   Weight  500 non-null    int64 \n 3   Index   500 non-null    int64 \ndtypes: int64(3), object(1)\nmemory usage: 15.8+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df_2.describe()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:07:13.534371Z","iopub.execute_input":"2024-06-14T06:07:13.535175Z","iopub.status.idle":"2024-06-14T06:07:13.557962Z","shell.execute_reply.started":"2024-06-14T06:07:13.535140Z","shell.execute_reply":"2024-06-14T06:07:13.556883Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"           Height      Weight       Index\ncount  500.000000  500.000000  500.000000\nmean   169.944000  106.000000    3.748000\nstd     16.375261   32.382607    1.355053\nmin    140.000000   50.000000    0.000000\n25%    156.000000   80.000000    3.000000\n50%    170.500000  106.000000    4.000000\n75%    184.000000  136.000000    5.000000\nmax    199.000000  160.000000    5.000000","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>Height</th>\n      <th>Weight</th>\n      <th>Index</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>500.000000</td>\n      <td>500.000000</td>\n      <td>500.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>169.944000</td>\n      <td>106.000000</td>\n      <td>3.748000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>16.375261</td>\n      <td>32.382607</td>\n      <td>1.355053</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>140.000000</td>\n      <td>50.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>156.000000</td>\n      <td>80.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>170.500000</td>\n      <td>106.000000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>184.000000</td>\n      <td>136.000000</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>199.000000</td>\n      <td>160.000000</td>\n      <td>5.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.shape","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:09:45.785029Z","iopub.execute_input":"2024-06-14T06:09:45.785414Z","iopub.status.idle":"2024-06-14T06:09:45.796251Z","shell.execute_reply.started":"2024-06-14T06:09:45.785382Z","shell.execute_reply":"2024-06-14T06:09:45.795091Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"(500, 4)"},"metadata":{}}]},{"cell_type":"code","source":"df_2.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:09:52.743957Z","iopub.execute_input":"2024-06-14T06:09:52.744394Z","iopub.status.idle":"2024-06-14T06:09:52.752498Z","shell.execute_reply.started":"2024-06-14T06:09:52.744362Z","shell.execute_reply":"2024-06-14T06:09:52.751295Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"Gender    object\nHeight     int64\nWeight     int64\nIndex      int64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df_2.iloc[4,2]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:15:26.982127Z","iopub.execute_input":"2024-06-14T06:15:26.982510Z","iopub.status.idle":"2024-06-14T06:15:26.989392Z","shell.execute_reply.started":"2024-06-14T06:15:26.982480Z","shell.execute_reply":"2024-06-14T06:15:26.988386Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"61"},"metadata":{}}]},{"cell_type":"code","source":"df_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:15:12.917878Z","iopub.execute_input":"2024-06-14T06:15:12.918316Z","iopub.status.idle":"2024-06-14T06:15:12.930173Z","shell.execute_reply.started":"2024-06-14T06:15:12.918272Z","shell.execute_reply":"2024-06-14T06:15:12.928984Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"   Gender  Height  Weight  Index\n0    Male     174      96      4\n1    Male     189      87      2\n2  Female     185     110      4\n3  Female     195     104      3\n4    Male     149      61      3","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>Gender</th>\n      <th>Height</th>\n      <th>Weight</th>\n      <th>Index</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>96</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>87</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>185</td>\n      <td>110</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>195</td>\n      <td>104</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>149</td>\n      <td>61</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.iloc[0:20, :]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:15:55.612517Z","iopub.execute_input":"2024-06-14T06:15:55.613346Z","iopub.status.idle":"2024-06-14T06:15:55.625283Z","shell.execute_reply.started":"2024-06-14T06:15:55.613308Z","shell.execute_reply":"2024-06-14T06:15:55.624202Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"    Gender  Height  Weight  Index\n0     Male     174      96      4\n1     Male     189      87      2\n2   Female     185     110      4\n3   Female     195     104      3\n4     Male     149      61      3\n5     Male     189     104      3\n6     Male     147      92      5\n7     Male     154     111      5\n8     Male     174      90      3\n9   Female     169     103      4\n10    Male     195      81      2\n11  Female     159      80      4\n12  Female     192     101      3\n13    Male     155      51      2\n14    Male     191      79      2\n15  Female     153     107      5\n16  Female     157     110      5\n17    Male     140     129      5\n18    Male     144     145      5\n19    Male     172     139      5","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>Gender</th>\n      <th>Height</th>\n      <th>Weight</th>\n      <th>Index</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>96</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>87</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>185</td>\n      <td>110</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>195</td>\n      <td>104</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>149</td>\n      <td>61</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>104</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Male</td>\n      <td>147</td>\n      <td>92</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Male</td>\n      <td>154</td>\n      <td>111</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>90</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Female</td>\n      <td>169</td>\n      <td>103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Male</td>\n      <td>195</td>\n      <td>81</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Female</td>\n      <td>159</td>\n      <td>80</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Female</td>\n      <td>192</td>\n      <td>101</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Male</td>\n      <td>155</td>\n      <td>51</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Male</td>\n      <td>191</td>\n      <td>79</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Female</td>\n      <td>153</td>\n      <td>107</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Female</td>\n      <td>157</td>\n      <td>110</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Male</td>\n      <td>140</td>\n      <td>129</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Male</td>\n      <td>144</td>\n      <td>145</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Male</td>\n      <td>172</td>\n      <td>139</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.iloc[0:10, 0:3]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:16:28.511669Z","iopub.execute_input":"2024-06-14T06:16:28.512542Z","iopub.status.idle":"2024-06-14T06:16:28.523496Z","shell.execute_reply.started":"2024-06-14T06:16:28.512501Z","shell.execute_reply":"2024-06-14T06:16:28.522234Z"},"trusted":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"   Gender  Height  Weight\n0    Male     174      96\n1    Male     189      87\n2  Female     185     110\n3  Female     195     104\n4    Male     149      61\n5    Male     189     104\n6    Male     147      92\n7    Male     154     111\n8    Male     174      90\n9  Female     169     103","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>Gender</th>\n      <th>Height</th>\n      <th>Weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>185</td>\n      <td>110</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>195</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>149</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Male</td>\n      <td>147</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Male</td>\n      <td>154</td>\n      <td>111</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Female</td>\n      <td>169</td>\n      <td>103</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.iloc[:10, :3]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:16:45.498350Z","iopub.execute_input":"2024-06-14T06:16:45.498737Z","iopub.status.idle":"2024-06-14T06:16:45.509340Z","shell.execute_reply.started":"2024-06-14T06:16:45.498708Z","shell.execute_reply":"2024-06-14T06:16:45.508291Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"   Gender  Height  Weight\n0    Male     174      96\n1    Male     189      87\n2  Female     185     110\n3  Female     195     104\n4    Male     149      61\n5    Male     189     104\n6    Male     147      92\n7    Male     154     111\n8    Male     174      90\n9  Female     169     103","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>Gender</th>\n      <th>Height</th>\n      <th>Weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>185</td>\n      <td>110</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>195</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>149</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Male</td>\n      <td>147</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Male</td>\n      <td>154</td>\n      <td>111</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Female</td>\n      <td>169</td>\n      <td>103</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.loc[2:8, 'Gender':'Weight']","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:18:01.368032Z","iopub.execute_input":"2024-06-14T06:18:01.368422Z","iopub.status.idle":"2024-06-14T06:18:01.381626Z","shell.execute_reply.started":"2024-06-14T06:18:01.368392Z","shell.execute_reply":"2024-06-14T06:18:01.380389Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"   Gender  Height  Weight\n2  Female     185     110\n3  Female     195     104\n4    Male     149      61\n5    Male     189     104\n6    Male     147      92\n7    Male     154     111\n8    Male     174      90","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>Gender</th>\n      <th>Height</th>\n      <th>Weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>185</td>\n      <td>110</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>195</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>149</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Male</td>\n      <td>189</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Male</td>\n      <td>147</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Male</td>\n      <td>154</td>\n      <td>111</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Male</td>\n      <td>174</td>\n      <td>90</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.index","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:19:11.218383Z","iopub.execute_input":"2024-06-14T06:19:11.218777Z","iopub.status.idle":"2024-06-14T06:19:11.226474Z","shell.execute_reply.started":"2024-06-14T06:19:11.218749Z","shell.execute_reply":"2024-06-14T06:19:11.225196Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"RangeIndex(start=0, stop=500, step=1)"},"metadata":{}}]},{"cell_type":"code","source":"df_2.columns","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:19:23.394485Z","iopub.execute_input":"2024-06-14T06:19:23.394918Z","iopub.status.idle":"2024-06-14T06:19:23.402746Z","shell.execute_reply.started":"2024-06-14T06:19:23.394888Z","shell.execute_reply":"2024-06-14T06:19:23.401487Z"},"trusted":true},"execution_count":73,"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"Index(['Gender', 'Height', 'Weight', 'Index'], dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}