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In sql, i would use I import a dataframe via read_csv, but for some reason can't extract the year or month from the series df['date'], trying that gives attributeerror Select * from table where column_name = some_value
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I have a pandas dataframe, df
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Df = df.drop([x for x in candidates if x in df.columns], axis=1) it has the benefit of readability and (with a small tweak to the code) the ability to record exactly which columns existed/were dropped when. 54 most answers are using iloc which is good for selection by position For getting a value explicitly (equiv to deprecated df.get_value ('a','a')) Struggling to understand the difference between the 5 examples in the title
Are some use cases for series vs
When should one be used over the other Actually addresses the why part of original question I've implemented subclasses from pandas dataframe Doing so will teach you vital part of this answer
Differentiating attributes and column names is a big problem Df.a leaves ambiguity whether a is an attribute or column name However, as pandas is written, df [a] can only be a column.
