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Jun 17, 2022
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Standard command name #511

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Merge branch 'main' of https://github.com/nushell/nushell.github.io i…
elferherrera Jun 17, 2022
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update name command
elferherrera Jun 17, 2022
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32 changes: 16 additions & 16 deletions book/dataframes.md
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Expand Up @@ -486,10 +486,10 @@ worth keeping in memory while you explore your dataset.

It is also possible to construct dataframes from basic Nushell primitives, such
as integers, decimals, or strings. Let's create a small dataframe using the
command `to-df`.
command `into df`.

```shell
> let a = ([[a b]; [1 2] [3 4] [5 6]] | to-df)
> let a = ([[a b]; [1 2] [3 4] [5 6]] | into df)
> $a

───┬───┬───
Expand Down Expand Up @@ -547,7 +547,7 @@ format](https://arrow.apache.org/docs/format/Columnar.html)). The other
optimization trick is the fact that whenever possible, the columns from the
dataframes are shared between dataframes, avoiding memory duplication for the
same data. This means that dataframes `$a` and `$a2` are sharing the same two
columns we created using the `to-df` command. For this reason, it isn't
columns we created using the `into df` command. For this reason, it isn't
possible to change the value of a column in a dataframe. However, you can
create new columns based on data from other columns or dataframes.

Expand All @@ -557,11 +557,11 @@ A `Series` is the building block of a `DataFrame`. Each Series represents a
column with the same data type, and we can create multiple Series of different
types, such as float, int or string.

Let's start our exploration with Series by creating one using the `to-df`
Let's start our exploration with Series by creating one using the `into df`
command:

```shell
> let new = ([9 8 4] | to-df)
> let new = ([9 8 4] | into df)
> $new

───┬───
Expand Down Expand Up @@ -714,7 +714,7 @@ Now we have a new dataframe with only the values where the mask was true.
The masks can also be created from Nushell lists, for example:

```shell
> let mask1 = ([true true false] | to-df)
> let mask1 = ([true true false] | into df)
> $new_df | filter-with $mask1

───┬───┬───┬─────────┬────────
Expand Down Expand Up @@ -757,7 +757,7 @@ We can also create a mask by checking if some values exist in other Series.
Using the first dataframe that we created we can do something like this

```shell
> let mask3 = ($df.first | is-in ([b c] | to-df))
> let mask3 = ($df.first | is-in ([b c] | into df))

───┬──────
# │ first
Expand Down Expand Up @@ -824,7 +824,7 @@ from our original dataframe. With that in mind, we can use the next command to
extract that information

```shell
> let indices = ([1 4 6] | to-df)
> let indices = ([1 4 6] | into df)
> $df | take $indices

───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────
Expand Down Expand Up @@ -886,7 +886,7 @@ And finally, we can create new Series by setting a new value in the marked
indices. Have a look at the next command

```shell
> let indices = ([0 2] | to-df);
> let indices = ([0 2] | into df);
> $df.int_1 | set-with-idx 123 --indices $indices

───┬───────
Expand Down Expand Up @@ -991,7 +991,7 @@ operations.
Let's create a small example of a lazy dataframe

```shell
> let a = ([[a b]; [1 a] [2 b] [3 c] [4 d]] | to-lazy)
> let a = ([[a b]; [1 a] [2 b] [3 c] [4 d]] | into lazy)
> $a
────────────────┬────────────────────────────────────────────────
plan │ DATAFRAME(in-memory): ["a", "b"];
Expand Down Expand Up @@ -1088,7 +1088,7 @@ Let's try something more complicated and create aggregations from a lazy
dataframe

```shell
> let a = ( [[name value]; [one 1] [two 2] [one 1] [two 3]] | to-lazy )
> let a = ( [[name value]; [one 1] [two 2] [one 1] [two 3]] | into lazy )
> $a
::: | group-by name
::: | agg [
Expand All @@ -1108,7 +1108,7 @@ And we could join on a lazy dataframe that hasn't being collected. Let's join
the resulting group by to the original lazy frame

```shell
> let a = ( [[name value]; [one 1] [two 2] [one 1] [two 3]] | to-lazy )
> let a = ( [[name value]; [one 1] [two 2] [one 1] [two 3]] | into lazy )
> let group = ($a
::: | group-by name
::: | agg [
Expand Down Expand Up @@ -1181,10 +1181,10 @@ whenever possible, their analogous Nushell command.
| slice | DataFrame | Creates new dataframe from a slice of rows | |
| sort-by | DataFrame, Series | Creates new sorted dataframe or series | sort |
| take | DataFrame, Series | Creates new dataframe using the given indices | |
| to-csv | DataFrame | Saves dataframe to csv file | to csv |
| to-df | | Converts a pipelined Table or List into Dataframe | |
| to-dummies | DataFrame | Creates a new dataframe with dummy variables | |
| to-parquet | DataFrame | Saves dataframe to parquet file | |
| to csv | DataFrame | Saves dataframe to csv file | to csv |
| into df | | Converts a pipelined Table or List into Dataframe | |
| dummies | DataFrame | Creates a new dataframe with dummy variables | |
| to parquet | DataFrame | Saves dataframe to parquet file | |
| unique | Series | Returns unique values from a series | uniq |
| value-counts | Series | Returns a dataframe with the counts for unique values in series | |
| where | DataFrame | Filter dataframe to match the condition | where |
Expand Down