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Python Data Analysis by Ivan Idris

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pandas

The following are useful pandas functions:

  • pandas.date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None, closed=None): This function creates a fixed frequency date-time index
  • pandas.isnull(obj): This function finds NaN and None values
  • pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True): This function merges the DataFrame objects with a database-like join on columns or indices
  • pandas.pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', fill_value=None, margins=False, dropna=True): This function creates a spreadsheet-like pivot table as a pandas DataFrame
  • pandas.read_csv(filepath_or_buffer, ...

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