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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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Converting scikit-learn dataset to Pandas DataFrame

Pandas
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Creating DataFrames Cookbook
schedule Jul 1, 2022
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To convert a scikit-learn dataset to Pandas DataFrame:

from sklearn import datasets
import pandas as pd

# load_boston() returns sklearn.utils.Bunch
boston_data = datasets.load_boston()
# boston_data.data is a 2D array
# boston_data.feature_names is an array of columns labels
df_boston = pd.DataFrame(boston_data.data, columns=boston_data.feature_names)
df_boston['target'] = pd.Series(boston_data.target)
df_boston.head()
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT target
0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 15.3 396.90 4.98 24.0
1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 17.8 396.90 9.14 21.6
2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 17.8 392.83 4.03 34.7
3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 18.7 394.63 2.94 33.4
4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 18.7 396.90 5.33 36.2
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Published by Isshin Inada
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