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Remove mutating examples for adding new columns #2434

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Sep 22, 2020
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Remove mutating examples for adding new columns #2434

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89b0386
Remove mutating examples for adding new columns
tk3369 Sep 16, 2020
7783d13
Add mutating operations
tk3369 Sep 20, 2020
c38ce1d
Add `insertcols!` example
tk3369 Sep 22, 2020
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21 changes: 18 additions & 3 deletions docs/src/man/comparisons.md
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Expand Up @@ -69,13 +69,13 @@ rows having the index value of `'c'`.
| Reduce multiple values | `df['z'].mean(skipna = False)` | `mean(df.z)` |
| | `df['z'].mean()` | `mean(skipmissing(df.z))` |
| | `df[['z']].agg(['mean'])` | `combine(df, :z => mean ∘ skipmissing)` |
| Add new columns | `df.assign(z1 = df['z'] + 1)` | `df.z1 = df.z .+ 1` |
| | | `insertcols!(df, :z1 => df.z .+ 1)` |
| | | `transform(df, :z => (v -> v .+ 1) => :z1)` |
| Add new columns | `df.assign(z1 = df['z'] + 1)` | `transform(df, :z => (v -> v .+ 1) => :z1)` |
| Rename columns | `df.rename(columns = {'x': 'x_new'})` | `rename(df, :x => :x_new)` |
| Pick & transform columns | `df.assign(x_mean = df['x'].mean())[['x_mean', 'y']]` | `select(df, :x => mean, :y)` |
| Sort rows | `df.sort_values(by = 'x')` | `sort(df, :x)` |
| | `df.sort_values(by = ['grp', 'x'], ascending = [True, False])` | `sort(df, [:grp, order(:x, rev = true)])` |
| Drop missing rows | `df.dropna()` | `dropmissing(df)` |
| Select unique rows | `df.drop_duplicates()` | `unique(df)` |

Note that pandas skips `NaN` values in its analytic functions by default. By contrast,
Julia functions do not skip `NaN`'s. If necessary, you can filter out
Expand All @@ -93,6 +93,21 @@ examples above do not synchronize the column names between pandas and DataFrames
(you can pass `renamecols=false` keyword argument to `select`, `transform` and
`combine` functions to retain old column names).

### Mutating operations

| Operation | pandas | DataFrames.jl |
| :----------------- | :---------------------------------------------------- | :------------------------------------------- |
| Add new columns | `df['z1'] = df['z'] + 1` | `df.z1 = df.z .+ 1` |
| | | `transform!(df, :z => (x -> x .+ 1) => :z1)` |
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| | `df.insert(1, 'const', 10)` | `insertcols!(df, 2, :const => 10)` |
| Rename columns | `df.rename(columns = {'x': 'x_new'}, inplace = True)` | `rename!(df, :x => :x_new)` |
| Sort rows | `df.sort_values(by = 'x', inplace = True)` | `sort!(df, :x)` |
| Drop missing rows | `df.dropna(inplace = True)` | `dropmissing!(df)` |
| Select unique rows | `df.drop_duplicates(inplace = True)` | `unique!(df)` |

Generally speaking, DataFrames.jl follows the Julia convention of using `!` in the
function name to indicate mutation behavior.

### Grouping data and aggregation

DataFrames.jl provides a `groupby` function to apply operations
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