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railbotan
reviewed
Mar 5, 2022
| "indexes = np.argmax(matrix, axis=1)\n", | ||
| "print(indexes)\n", | ||
| "print(...)" | ||
| "print([matrix[i, j] for i, j in enumerate(indexes)])" |
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Вместо этого np.max(matrix, axis=1)
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5 баллов |
railbotan
reviewed
Mar 11, 2022
| "id": "rK9SwvI_utTj" | ||
| }, | ||
| "source": [ | ||
| "**6. Правда ли, что люди, которые получают больше 50k, имеют как минимум высшее образование? (признак *education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* или *Doctorate*)**" |
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ed = ['Bachelors', 'Prof-school', 'Assoc-acdm', 'Assoc-voc', 'Masters', 'Doctorate']
data[data.education.isin(ed)].salary.value_counts()['<=50K'] == 0
railbotan
reviewed
Mar 11, 2022
| "id": "cn-jYXhzutTl" | ||
| }, | ||
| "source": [ | ||
| "**8. Среди кого больше доля зарабатывающих много (>50K): среди женатых или холостых мужчин (признак *marital-status*)? Женатыми считаем тех, у кого *marital-status* начинается с *Married* (Married-civ-spouse, Married-spouse-absent или Married-AF-spouse), остальных считаем холостыми.**" |
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data['married'] = data['marital-status'].apply(lambda row: 1 if row.startswith('Married') else 0)
data[data.sex == 'Male'].groupby('married').salary.value_counts(normalize=True)
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5 баллов |
railbotan
reviewed
Mar 18, 2022
| "id": "HgiqBXtb05qR" | ||
| }, | ||
| "source": [ | ||
| "data.groupby('Sex')['Age'].min()" |
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data[data.Year == 1992].groupby('Sex')['Age'].min()
railbotan
reviewed
Mar 18, 2022
| }, | ||
| "source": [ | ||
| "len(data[(data['Sex'] == 'M') & (data['Year'] == 2012) & (data['Sport'] == 'Basketball')]) / \\\n", | ||
| "len(data[(data['Sex'] == 'M') & (data['Year'] == 2012)])" |
railbotan
reviewed
Mar 18, 2022
| "id": "Y754OGI-05q3" | ||
| }, | ||
| "source": [ | ||
| "len(data[(data['Year'] == 2008) & (data['Team'] == 'Switzerland') & (data['Medal'] == 'Gold')])" |
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4 балла |
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