October 2017
Intermediate to advanced
532 pages
16h 10m
English
Content preview from Pandas Cookbook
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How to do it...
- Read the college dataset with the institution name as the index:
>>> college = pd.read_csv('data/college.csv', index_col='INSTNM')>>> college.dtypesCITY object
STABBR object
HBCU float64
MENONLY float64
...
PCTFLOAN float64
UG25ABV float64
MD_EARN_WNE_P10 object
GRAD_DEBT_MDN_SUPP object
Length: 26, dtype: object
- All the other columns besides CITY and STABBR appear to be numeric. Examining the data types from the preceding step reveals unexpectedly that the MD_EARN_WNE_P10 and GRAD_DEBT_MDN_SUPP columns are of type object and not numeric. To help get a better idea of what kind of values are in these columns, let's examine their first value:
>>> college.MD_EARN_WNE_P10.iloc[0]'30300'>>> college.GRAD_DEBT_MDN_SUPP.iloc[0] ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.