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Chapter 5 polars - #259
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rinman57 wants to merge 17 commits into
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Chapter 5 polars#259rinman57 wants to merge 17 commits into
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- .rename requires a dictionary, will we be teaching this? - POLARS_CHANGE for the PercentFormatter
- nba_salaries.sort(pl.nth(0)).head(5) --> .sort using column indexes, cannot just use a number to sort
nothing out of the ordinary
- with or without `.alias()`: change = (us_pop_by_age.get_column("2019") - us_pop_by_age.get_column("2014")).alias("Change")
- with pl.Config(tbl_rows=-1) --> to display every row - matplotlib is used for visualization for now
- `Table().with_column('Chapters', huck_finn_chapters)` builds a one-column DataFrame from a named Series: `pl.DataFrame([pl.Series('Chapters', huck_finn_chapters)])`.
- The DataFrame display shows the first five and last five rows. A `datascience` table showed the first ten.
- `np.char.count(chapters, name)` counts a literal substring. `Series.str.count_matches(name)` treats the pattern as a regular expression, so the call passes `literal=True`. - `np.cumsum(...)` is `Series.cum_sum()` on that count Series. - `Table().with_columns([name, values, ...])` took a flat list of labels and arrays. The DataFrame is a list of named Series. - `np.arange(1, 44, 1)` and `np.arange(1, 48, 1)` are `range(1, 44)` and `range(1, 48)`, passed as a Series named `Chapter`. - `Table.with_column(name, values)` is `DataFrame.with_columns(pl.Series(name, values))`. - `cum_counts.plot(column_for_xticks=3)` plotted every other column against column 3 (`Chapter`). Polars does not plot, so each name is one `plots.plot` of `Chapter` against that name. The Little Women call used column 5, which is also `Chapter`. POLARS_CHANGE: - Comment on `literal=True`: `count_matches` treats the pattern as a regular expression unless `literal=True`. The same comment is on the Huckleberry Finn counts and the Little Women counts. - Comment on the plots: Polars does not plot, so each name is drawn against `Chapter`. - `fmt_str_lengths=160` on the Little Women chapter DataFrame, same reason as in Plotting the Classics.
- `np.char.count(chapters, '.')` counts periods. `count_matches('.')` without `literal=True` counts every character, because `.` matches any character in a regular expression.
- `Table().with_columns([name, values, ...])` is again a DataFrame built from named Series.
- `table.column(index)` is `DataFrame.to_series(index)`. The scatter still uses position 1 for the period counts and position 0 for the chapter lengths.
POLARS_CHANGE:
- Comment on `literal=True` for `'.'`: `count_matches` treats `'.'` as a regular expression unless `literal=True`.
arrays --> pl.Series
- table methods are changed to adapt to polars
- pl.int_range --> for integers - pl.linear_space --> for fractional / float steps
- added extra notes for students to understand `.product()` "The expression below has three pieces:"
arrays --> pl.Series
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