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Updated dataframes documentation (#1522)
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* updated dataframes documentation

* Update book/dataframes.md

---------

Co-authored-by: Stefan Holderbach <[email protected]>
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@ayax79 @sholderbach
ayax79 and sholderbach authored Aug 24, 2024
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Expand Up @@ -49,7 +49,7 @@ sections of this page.
> System Details: The benchmarks presented in this section were run using a
> Macbook with a processor M1 pro and 32gb of ram
>
> All examples were run on Nushell version 0.93 using `nu_plugin_polars 0.93`
> All examples were run on Nushell version 0.97 using `nu_plugin_polars 0.97`
### File information

Expand All @@ -61,7 +61,7 @@ The dataset has 5 columns and 5,429,252 rows. We can check that by using the
`polars store-ls` command:

```nu
> let df_0 = polars open Data7602DescendingYearOrder.csv
> let df_0 = polars open --eager Data7602DescendingYearOrder.csv
> polars store-ls | select key type columns rows estimated_size
╭──────────────────────────────────────┬───────────┬─────────┬─────────┬────────────────╮
│ key │ type │ columns │ rows │ estimated_size │
Expand All @@ -70,6 +70,11 @@ The dataset has 5 columns and 5,429,252 rows. We can check that by using the
╰──────────────────────────────────────┴───────────┴─────────┴─────────┴────────────────╯
```

::: tip
As of nushell 0.97, `polars open` will open as a lazy dataframe instead of a eager dataframe.
To open as an eager dataframe, use the `--eager` flag.
:::

We can have a look at the first lines of the file using [`first`](/commands/docs/first.md):

```nu
Expand Down Expand Up @@ -225,7 +230,7 @@ Now, to read that file as a dataframe use the `polars open` command like
this:

```nu
> let df_1 = polars open test_small.csv
> let df_1 = polars open --eager test_small.csv
```

This should create the value `$df_1` in memory which holds the data we just
Expand Down Expand Up @@ -354,7 +359,7 @@ are going to call it `test_small_a.csv`)
We use the `polars open` command to create the new variable

```nu
> let df_2 = polars open test_small_a.csv
> let df_2 = polars open --eager test_small_a.csv
```

Now, with the second dataframe loaded in memory we can join them using the
Expand Down Expand Up @@ -759,10 +764,6 @@ Another operation that can be done with masks is setting or replacing a value
from a series. For example, we can change the value in the column `first` where
the value is equal to `a`

::: warning
This is example is not updated to recent Nushell versions.
:::

```nu
> $df_1 | polars get first | polars set new --mask ($df_1.first =~ a)
╭───┬────────╮
Expand Down Expand Up @@ -1224,6 +1225,7 @@ This list may be outdated. To get the up-to-date command list, see [Dataframe](/
| polars reverse | dataframe | Reverses the LazyFrame | |
| polars rolling | dataframe | Rolling calculation for a series. | |
| polars sample | dataframe | Create sample dataframe. | |
| polars save | dataframe | Saves a dataframe to disk. For lazy dataframes a sink operation will be used if the file type supports it (parquet, ipc/arrow, csv, and ndjson).| |
| polars schema | dataframe | Show schema for a dataframe. | |
| polars select | dataframe | Selects columns from lazyframe. | select |
| polars set | dataframe | Sets value where given mask is true. | |
Expand All @@ -1242,11 +1244,6 @@ This list may be outdated. To get the up-to-date command list, see [Dataframe](/
| polars sum | expression, dataframe | Creates a sum expression for an aggregation or aggregates columns to their sum value. | |
| polars summary | dataframe | For a dataframe, produces descriptive statistics (summary statistics) for its numeric columns. | |
| polars take | dataframe | Creates new dataframe using the given indices. | |
| polars to-arrow | dataframe | Saves dataframe to arrow file. | |
| polars to-avro | dataframe | Saves dataframe to avro file. | |
| polars to-csv | dataframe | Saves dataframe to CSV file. | |
| polars to-jsonl | dataframe | Saves dataframe to a JSON lines file. | |
| polars to-parquet | dataframe | Saves dataframe to parquet file. | |
| polars unique | dataframe | Returns unique values from a dataframe. | uniq |
| polars uppercase | dataframe | Uppercase the strings in the column. | |
| polars value-counts | dataframe | Returns a dataframe with the counts for unique values in series. | |
Expand Down

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