| { |
| "language": "python", |
| "category": "data_science", |
| "training_data": [ |
| { |
| "input": "merge pandas 0", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 1", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 2", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 3", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 4", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 5", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 6", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 7", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 8", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 9", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 10", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 11", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 12", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 13", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 14", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 15", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 16", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 17", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 18", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 19", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 20", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 21", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 22", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 23", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 24", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 25", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 26", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 27", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 28", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 29", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 30", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 31", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 32", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 33", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 34", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 35", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 36", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 37", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 38", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 39", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 40", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 41", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 42", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 43", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 44", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 45", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 46", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 47", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 48", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 49", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 50", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 51", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 52", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 53", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 54", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 55", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 56", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 57", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 58", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 59", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 60", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 61", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 62", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 63", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 64", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 65", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 66", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 67", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 68", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 69", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 70", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 71", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 72", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 73", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 74", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 75", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 76", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 77", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 78", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 79", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 80", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 81", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 82", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 83", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 84", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 85", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 86", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 87", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 88", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 89", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 90", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 91", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 92", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 93", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 94", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 95", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 96", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 97", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 98", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 99", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 100", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 101", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 102", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 103", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 104", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 105", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 106", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 107", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 108", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 109", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 110", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 111", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 112", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 113", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 114", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 115", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 116", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 117", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 118", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 119", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 120", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 121", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 122", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 123", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 124", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 125", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 126", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 127", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 128", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 129", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 130", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 131", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 132", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 133", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 134", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 135", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 136", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 137", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 138", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 139", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 140", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 141", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 142", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 143", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 144", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 145", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 146", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 147", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 148", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 149", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 150", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 151", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 152", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 153", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 154", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 155", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 156", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 157", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 158", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 159", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 160", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 161", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 162", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 163", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 164", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 165", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 166", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 167", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 168", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 169", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 170", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 171", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 172", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 173", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 174", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 175", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 176", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 177", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "groupby pandas 178", |
| "output": "df.groupby('category').sum()\ndf.groupby('category')['value'].mean()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 179", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 180", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 181", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 182", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 183", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 184", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 185", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 186", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 187", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 188", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 189", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 190", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "merge pandas 191", |
| "output": "pd.merge(df1, df2, on='key', how='inner')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "read csv pandas 192", |
| "output": "import pandas as pd\ndf = pd.read_csv('file.csv')", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "drop pandas 193", |
| "output": "df.drop('column', axis=1)\ndf.dropna()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "select column pandas 194", |
| "output": "df['column_name']\ndf[['col1', 'col2']]", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "filter rows pandas 195", |
| "output": "df[df['age'] > 18]\ndf.loc[df['name'] == 'John']", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 196", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "describe pandas 197", |
| "output": "df.describe()\ndf.info()", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "dataframe pandas 198", |
| "output": "df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "sort pandas 199", |
| "output": "df.sort_values('age', ascending=False)", |
| "tags": [ |
| "pandas", |
| "data_analysis" |
| ] |
| }, |
| { |
| "input": "arange numpy 0", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 1", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 2", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 3", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 4", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 5", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 6", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 7", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 8", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 9", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 10", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 11", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 12", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 13", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 14", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 15", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 16", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 17", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 18", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 19", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 20", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 21", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 22", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 23", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 24", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 25", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 26", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 27", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 28", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 29", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 30", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 31", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 32", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 33", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 34", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 35", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 36", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 37", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 38", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 39", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 40", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 41", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 42", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 43", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 44", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 45", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 46", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 47", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 48", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 49", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 50", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 51", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 52", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 53", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 54", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 55", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 56", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 57", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 58", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 59", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 60", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 61", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 62", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 63", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 64", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 65", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 66", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 67", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 68", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 69", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 70", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 71", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 72", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 73", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 74", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 75", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 76", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 77", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 78", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 79", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 80", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 81", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 82", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 83", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 84", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 85", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 86", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 87", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 88", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 89", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 90", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 91", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 92", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 93", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 94", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 95", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 96", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 97", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 98", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 99", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 100", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 101", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 102", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 103", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 104", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 105", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 106", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 107", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 108", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 109", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 110", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 111", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 112", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 113", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 114", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 115", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 116", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 117", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 118", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 119", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 120", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 121", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 122", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 123", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 124", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 125", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 126", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 127", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 128", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 129", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 130", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 131", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 132", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 133", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 134", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "array numpy 135", |
| "output": "import numpy as np\narr = np.array([1,2,3,4,5])", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 136", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 137", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 138", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 139", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 140", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 141", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 142", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 143", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 144", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 145", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 146", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 147", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 148", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 149", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 150", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 151", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 152", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 153", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 154", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 155", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 156", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 157", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 158", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 159", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 160", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 161", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 162", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 163", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 164", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 165", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 166", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 167", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 168", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 169", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 170", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 171", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 172", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 173", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 174", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 175", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 176", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 177", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 178", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 179", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 180", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 181", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "arange numpy 182", |
| "output": "np.arange(0, 10, 2) # [0,2,4,6,8]", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 183", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 184", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 185", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 186", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 187", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 188", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 189", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 190", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "argmax numpy 191", |
| "output": "np.argmax(arr)\nnp.argsort(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "operations numpy 192", |
| "output": "arr + 5\narr * 2\narr ** 2", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 193", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 194", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 195", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "mean median numpy 196", |
| "output": "np.mean(arr)\nnp.median(arr)\nnp.std(arr)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 197", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "reshape numpy 198", |
| "output": "arr.reshape(2, 3)", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linspace numpy 199", |
| "output": "np.linspace(0, 10, 100) # 100 values from 0 to 10", |
| "tags": [ |
| "numpy", |
| "numerical" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 0", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 1", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 2", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 3", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 4", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 5", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 6", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 7", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 8", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 9", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 10", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 11", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 12", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 13", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 14", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 15", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 16", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 17", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 18", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 19", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 20", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 21", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 22", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 23", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 24", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 25", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 26", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 27", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 28", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 29", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 30", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 31", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 32", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 33", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 34", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 35", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 36", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 37", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 38", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 39", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 40", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 41", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 42", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 43", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 44", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 45", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 46", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 47", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 48", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 49", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 50", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 51", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 52", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 53", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 54", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 55", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 56", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 57", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 58", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 59", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 60", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 61", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 62", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 63", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 64", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 65", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 66", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 67", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 68", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 69", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 70", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 71", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 72", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 73", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 74", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 75", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 76", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 77", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 78", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 79", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 80", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 81", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 82", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 83", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 84", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 85", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 86", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 87", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 88", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 89", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 90", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 91", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 92", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 93", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 94", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 95", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 96", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 97", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 98", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 99", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 100", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 101", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 102", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 103", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 104", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 105", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 106", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 107", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 108", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 109", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 110", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 111", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 112", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 113", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 114", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 115", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 116", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 117", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 118", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 119", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 120", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 121", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 122", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 123", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 124", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 125", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 126", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 127", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 128", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 129", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 130", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 131", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 132", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 133", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 134", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 135", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 136", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 137", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 138", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 139", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 140", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 141", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 142", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 143", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 144", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 145", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 146", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 147", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 148", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 149", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 150", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 151", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 152", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 153", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 154", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 155", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 156", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 157", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 158", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 159", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 160", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 161", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 162", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 163", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 164", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 165", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 166", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 167", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 168", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 169", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 170", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 171", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 172", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 173", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 174", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 175", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 176", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 177", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 178", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 179", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 180", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 181", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 182", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 183", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 184", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 185", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 186", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 187", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 188", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 189", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 190", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 191", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "classification sklearn 192", |
| "output": "from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 193", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 194", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 195", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "train test split sklearn 196", |
| "output": "from sklearn.model_selection import train_test_split\nX_train, X_test = train_test_split(X)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "accuracy sklearn 197", |
| "output": "from sklearn.metrics import accuracy_score\naccuracy_score(y_true, y_pred)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "linear regression sklearn 198", |
| "output": "from sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| }, |
| { |
| "input": "kmeans sklearn 199", |
| "output": "from sklearn.cluster import KMeans\nmodel = KMeans(n_clusters=3)", |
| "tags": [ |
| "sklearn", |
| "machine_learning" |
| ] |
| } |
| ] |
| } |
