[x] and -> & to combine conditions => add tip for this
Pandas 3b indexing:
[x] update setting with copy warning section with more explanation (temporary variable, boolean selection on population, show both and that the one does not work countries.loc[countries['population'] > 50, 'population'] = 50)
Pandas 4:
[ ] Ways to shorten this a bit?
Pandas 6 - groupby:
[x] The color=C0 in the last exercise is not needed anymore
pandas 7 reshape:
[x] last exercise pivot_table mean -> median
Visualization - plotnine:
[x] the by in the first pandas example not doing anything?
Visualization - landscape:
[x] update: Bokeh now supports pdf
Case 1 - bike count:
[x] have raw data as backup
Case 2 - biodiversity processing:
[x] groupby doesn't count NaNs, value_counts does -> add a note to explain that difference?
Case 2 - biodiversity analysis:
[x] don't use built-in sum
[x] subselection_sex -> make subtask to create this variable
Case 3 bacterial resistance:
[x] intial tidying: we loose the "experiment id" or "repetion id" in the original data (multiple repeptitions for same phage / genotype, which now is a single row) -> that information is lost
[x] creation of density_mean -> select column 'optical_density' before .mean() -> no mean of survival etc .. (in solution)
General:
%matplotlib inline
still needed?Slides:
Pandas 1:
Pandas 3 selecting:
and
->&
to combine conditions => add tip for thisPandas 3b indexing:
countries.loc[countries['population'] > 50, 'population'] = 50
)Pandas 4:
Pandas 6 - groupby:
color=C0
in the last exercise is not needed anymorepandas 7 reshape:
Visualization - plotnine:
by
in the first pandas example not doing anything?Visualization - landscape:
Case 1 - bike count:
Case 2 - biodiversity processing:
Case 2 - biodiversity analysis:
Case 3 bacterial resistance:
density_mean
-> select column 'optical_density' before.mean()
-> no mean of survival etc .. (in solution)