Dataframes
WARNING
Starting with version 0.93, there is a more recent implementation of dataframes in nu_plugin_polars, which includes a newer version of polars and many bug fixes. From version 0.94 of Nushell, internal dataframes (with the dfr prefix) are going to be deprecated in favor of nu_plugin_polars.
To use nu_plugin_polars, you'll need to install cargo and then install the plugin with commands:
# Install the `polars` nushell plugin
> cargo install nu_plugin_polars
# Add the plugin's commands to your plugin registry file:
> plugin add ~/.cargo/bin/nu_plugin_polars
After installation, you will need to restart the nushell instance. If everything is successful, you should be able to see command completions for polars. For example, you should be able to execute polars into-df -h.
As we have seen so far, Nushell makes working with data its main priority. Lists and Tables are there to help you cycle through values in order to perform multiple operations or find data in a breeze. However, there are certain operations where a row-based data layout is not the most efficient way to process data, especially when working with extremely large files. Operations like group-by or join using large datasets can be costly memory-wise, and may lead to large computation times if they are not done using the appropriate data format.
For this reason, the DataFrame structure was introduced to Nushell. A DataFrame stores its data in a columnar format using as its base the Apache Arrow specification, and uses Polars as the motor for performing extremely fast columnar operations.
You may be wondering now how fast this combo could be, and how could it make working with data easier and more reliable. For this reason, let's start this page by presenting benchmarks on common operations that are done when processing data.
Benchmark comparisons
For this little benchmark exercise we will be comparing native Nushell commands, dataframe Nushell commands and Python Pandas commands. For the time being don't pay too much attention to the Dataframe commands. They will be explained in later sections of this page.
System Details: The benchmarks presented in this section were run using a machine with a processor Intel(R) Core(TM) i7-10710U (CPU @1.10GHz 1.61 GHz) and 16 gb of RAM.
All examples were run on Nushell version 0.33.1. (Command names are updated to Nushell 0.78)
File information
The file that we will be using for the benchmarks is the New Zealand business demography dataset. Feel free to download it if you want to follow these tests.
The dataset has 5 columns and 5,429,252 rows. We can check that by using the polars store-ls command:
> let df = (polars open .\Data7602DescendingYearOrder.csv)
> polars store-ls
╭───┬────────┬─────────┬─────────╮
│ # │ name │ columns │ rows │
├───┼────────┼─────────┼─────────┤
│ 0 │ $df │ 5 │ 5429252 │
╰───┴────────┴─────────┴─────────╯
We can have a look at the first lines of the file using first:
> $df | polars first
╭───┬──────────┬─────────┬──────┬───────────┬──────────╮
│ # │ anzsic06 │ Area │ year │ geo_count │ ec_count │
├───┼──────────┼─────────┼──────┼───────────┼──────────┤
│ 0 │ A │ A100100 │ 2000 │ 96 │ 130 │
╰───┴──────────┴─────────┴──────┴───────────┴──────────╯
...and finally, we can get an idea of the inferred data types:
> $df | polars dtypes
╭───┬───────────┬───────╮
│ # │ column │ dtype │
├───┼───────────┼───────┤
│ 0 │ anzsic06 │ str │
│ 1 │ Area │ str │
│ 2 │ year │ i64 │
│ 3 │ geo_count │ i64 │
│ 4 │ ec_count │ i64 │
╰───┴───────────┴───────╯
Loading the file
Let's start by comparing loading times between the various methods. First, we will load the data using Nushell's open command:
> timeit {open .\Data7602DescendingYearOrder.csv}
30sec 479ms 614us 400ns
Loading the file using native Nushell functionality took 30 seconds. Not bad for loading five million records! But we can do a bit better than that.
Let's now use Pandas. We are going to use the next script to load the file:
import pandas as pd
df = pd.read_csv("Data7602DescendingYearOrder.csv")
And the benchmark for it is:
> timeit {python load.py}
2sec 91ms 872us 900ns
That is a great improvement, from 30 seconds to 2 seconds. Nicely done, Pandas!
Probably we can load the data a bit faster. This time we will use Nushell's polars open command:
> timeit {polars open .\Data7602DescendingYearOrder.csv}
601ms 700us 700ns
This time it took us 0.6 seconds. Not bad at all.
Group-by comparison
Let's do a slightly more complex operation this time. We are going to group the data by year, and add groups using the column geo_count.
Again, we are going to start with a Nushell native command.
TIP
If you want to run this example, be aware that the next command will use a large amount of memory. This may affect the performance of your system while this is being executed.
> timeit {
open .\Data7602DescendingYearOrder.csv
| group-by year
| transpose header rows
| upsert rows { get rows | math sum }
| flatten
}
6min 30sec 622ms 312us
So, six minutes to perform this aggregated operation.
Let's try the same operation in pandas:
import pandas as pd
df = pd.read_csv("Data7602DescendingYearOrder.csv")
res = df.groupby("year")["geo_count"].sum()
print(res)
And the result from the benchmark is:
> timeit {python .\load.py}
1sec 966ms 954us 800ns
Not bad at all. Again, pandas managed to get it done in a fraction of the time.
To finish the comparison, let's try Nushell dataframes. We are going to put all the operations in one nu file, to make sure we are doing similar operations:
let df = (polars open Data7602DescendingYearOrder.csv)
let res = ($df | polars group-by year | polars agg (polars col geo_count | polars sum))
$res
and the benchmark with dataframes is:
> timeit {source load.nu}
557ms 658us 500ns
Luckily Nushell dataframes managed to halve the time again. Isn't that great?
As you can see, Nushell's Dataframe commands are as fast as the most common tools that exist today to do data analysis. The commands that are included in this release have the potential to become your go-to tool for doing data analysis. By composing complex Nushell pipelines, you can extract information from data in a reliable way.
Working with Dataframes
After seeing a glimpse of the things that can be done with Dataframe commands, now it is time to start testing them. To begin let's create a sample CSV file that will become our sample dataframe that we will be using along with the examples. In your favorite file editor paste the next lines to create out sample csv file.
"int_1,int_2,float_1,float_2,first,second,third,word
1,11,0.1,1.0,a,b,c,first
2,12,0.2,1.0,a,b,c,second
3,13,0.3,2.0,a,b,c,third
4,14,0.4,3.0,b,a,c,second
0,15,0.5,4.0,b,a,a,third
6,16,0.6,5.0,b,a,a,second
7,17,0.7,6.0,b,c,a,third
8,18,0.8,7.0,c,c,b,eight
9,19,0.9,8.0,c,c,b,ninth
0,10,0.0,9.0,c,c,b,ninth"
| save --raw --force test_small.csv
Save the file and name it however you want to, for the sake of these examples the file will be called test_small.csv.
Now, to read that file as a dataframe use the polars open command like this:
> let df = polars open test_small.csv
This should create the value $df in memory which holds the data we just created.
TIP
The command polars open can read either csv or parquet files.
To see all the dataframes that are stored in memory you can use
> polars store-ls
╭───────────────┬─────────┬─────────┬──────┬───────────┬───────────────┬───────────────┬────────────┬──────────┬────────────────╮
│ key │ created │ columns │ rows │ type │ estimated_... │ span_contents │ span_start │ span_end │ reference_c... │
├───────────────┼─────────┼─────────┼──────┼───────────┼───────────────┼───────────────┼────────────┼──────────┼────────────────┤
│ 43f53faa-9... │ now │ 8 │ 10 │ DataFrame │ 403 B │ polars open │ 1987476 │ 1987487 │ 1 │
╰───────────────┴─────────┴─────────┴──────┴───────────┴───────────────┴───────────────┴────────────┴──────────┴────────────────╯
As you can see, the command shows the created dataframes together with basic information about them.
And if you want to see a preview of the loaded dataframe you can send the dataframe variable to the stream
> $df
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
│ 0 │ 1 │ 11 │ 0.10 │ 1.00 │ a │ b │ c │ first │
│ 1 │ 2 │ 12 │ 0.20 │ 1.00 │ a │ b │ c │ second │
│ 2 │ 3 │ 13 │ 0.30 │ 2.00 │ a │ b │ c │ third │
│ 3 │ 4 │ 14 │ 0.40 │ 3.00 │ b │ a │ c │ second │
│ 4 │ 0 │ 15 │ 0.50 │ 4.00 │ b │ a │ a │ third │
│ 5 │ 6 │ 16 │ 0.60 │ 5.00 │ b │ a │ a │ second │
│ 6 │ 7 │ 17 │ 0.70 │ 6.00 │ b │ c │ a │ third │
│ 7 │ 8 │ 18 │ 0.80 │ 7.00 │ c │ c │ b │ eight │
│ 8 │ 9 │ 19 │ 0.90 │ 8.00 │ c │ c │ b │ ninth │
│ 9 │ 0 │ 10 │ 0.00 │ 9.00 │ c │ c │ b │ ninth │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
With the dataframe in memory we can start doing column operations with the DataFrame
TIP
If you want to see all the dataframe commands that are available you can use scope commands | where category =~ dataframe
Basic aggregations
Let's start with basic aggregations on the dataframe. Let's sum all the columns that exist in df by using the aggregate command
> $df | polars sum
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬──────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼──────┤
│ 0 │ 40 │ 145 │ 4.50 │ 46.00 │ │ │ │ │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴──────╯
As you can see, the aggregate function computes the sum for those columns where a sum makes sense. If you want to filter out the text column, you can select the columns you want by using the polars select command
> $df | polars sum | polars select int_1 int_2 float_1 float_2
╭───┬───────┬───────┬─────────┬─────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │
├───┼───────┼───────┼─────────┼─────────┤
│ 0 │ 40 │ 145 │ 4.50 │ 46.00 │
╰───┴───────┴───────┴─────────┴─────────╯
You can even store the result from this aggregation as you would store any other Nushell variable
> let res = $df | polars sum | polars select int_1 int_2 float_1 float_2
TIP
Type let res = ( !! ) and press enter. This will auto complete the previously executed command. Note the space between ( and !!.
And now we have two dataframes stored in memory
> polars store-ls
╭───────────────┬─────────┬─────────┬──────┬───────────┬───────────────┬───────────────┬────────────┬──────────┬────────────────╮
│ key │ created │ columns │ rows │ type │ estimated_... │ span_contents │ span_start │ span_end │ reference_c... │
├───────────────┼─────────┼─────────┼──────┼───────────┼───────────────┼───────────────┼────────────┼──────────┼────────────────┤
│ 43f53faa-9... │ now │ 8 │ 10 │ DataFrame │ 403 B │ polars open │ 1987476 │ 1987487 │ 1 │
│ 69bca897-0... │ now │ 4 │ 1 │ DataFrame │ 32 B │ polars select │ 1988496 │ 1988509 │ 1 │
╰───────────────┴─────────┴─────────┴──────┴───────────┴───────────────┴───────────────┴────────────┴──────────┴────────────────╯
Pretty neat, isn't it?
You can perform several aggregations on the dataframe in order to extract basic information from the dataframe and do basic data analysis on your brand new dataframe.
Joining a DataFrame
It is also possible to join two dataframes using a column as reference. We are going to join our mini dataframe with another mini dataframe. Copy these lines in another file and create the corresponding dataframe (for these examples we are going to call it test_small_a.csv)
"int_1,int_2,float_1,float_2,first
9,14,0.4,3.0,a
8,13,0.3,2.0,a
7,12,0.2,1.0,a
6,11,0.1,0.0,b"
| save --raw --force test_small_a.csv
We use the polars open command to create the new variable
> let df_a = polars open test_small_a.csv
Now, with the second dataframe loaded in memory we can join them using the column called int_1 from the left dataframe and the column int_1 from the right dataframe
> $df | polars join $df_a int_1 int_1
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────┬─────────┬───────────┬───────────┬─────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │ int_2_x │ float_1_x │ float_2_x │ first_x │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┼─────────┼───────────┼───────────┼─────────┤
│ 0 │ 6 │ 16 │ 0.60 │ 5.00 │ b │ a │ a │ second │ 11 │ 0.10 │ 0.00 │ b │
│ 1 │ 7 │ 17 │ 0.70 │ 6.00 │ b │ c │ a │ third │ 12 │ 0.20 │ 1.00 │ a │
│ 2 │ 8 │ 18 │ 0.80 │ 7.00 │ c │ c │ b │ eight │ 13 │ 0.30 │ 2.00 │ a │
│ 3 │ 9 │ 19 │ 0.90 │ 8.00 │ c │ c │ b │ ninth │ 14 │ 0.40 │ 3.00 │ a │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────┴─────────┴───────────┴───────────┴─────────╯
TIP
In Nu when a command has multiple arguments that are expecting multiple values we use brackets [] to enclose those values. In the case of polars join we can join on multiple columns as long as they have the same type.
For example:
> $df | polars join $df_a [int_1 first] [int_1 first]
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────┬─────────┬───────────┬───────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │ int_2_x │ float_1_x │ float_2_x │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┼─────────┼───────────┼───────────┤
│ 0 │ 6 │ 16 │ 0.60 │ 5.00 │ b │ a │ a │ second │ 11 │ 0.10 │ 0.00 │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────┴─────────┴───────────┴───────────╯
By default, the join command does an inner join, meaning that it will keep the rows where both dataframes share the same value. You can select a left join to keep the missing rows from the left dataframe. You can also save this result in order to use it for further operations.
DataFrame group-by
One of the most powerful operations that can be performed with a DataFrame is the polars group-by. This command will allow you to perform aggregation operations based on a grouping criteria. In Nushell, a GroupBy is a type of object that can be stored and reused for multiple aggregations. This is quite handy, since the creation of the grouped pairs is the most expensive operation while doing group-by and there is no need to repeat it if you are planning to do multiple operations with the same group condition.
To create a GroupBy object you only need to use the polars_group-by command
> let group = $df | polars group-by first
> $group
╭─────────────┬──────────────────────────────────────────────╮
│ LazyGroupBy │ apply aggregation to complete execution plan │
╰─────────────┴──────────────────────────────────────────────╯
When printing the GroupBy object we can see that it is in the background a lazy operation waiting to be completed by adding an aggregation. Using the GroupBy we can create aggregations on a column
> $group | polars agg (polars col int_1 | polars sum)
╭────────────────┬───────────────────────────────────────────────────────────────────────────────────────╮
│ plan │ AGGREGATE │
│ │ [col("int_1").sum()] BY [col("first")] FROM │
│ │ DF ["int_1", "int_2", "float_1", "float_2"]; PROJECT */8 COLUMNS; SELECTION: "None" │
│ optimized_plan │ AGGREGATE │
│ │ [col("int_1").sum()] BY [col("first")] FROM │
│ │ DF ["int_1", "int_2", "float_1", "float_2"]; PROJECT 2/8 COLUMNS; SELECTION: "None" │
╰────────────────┴───────────────────────────────────────────────────────────────────────────────────────╯
or we can define multiple aggregations on the same or different columns
$group | polars agg [
(polars col int_1 | polars n-unique)
(polars col int_2 | polars min)
(polars col float_1 | polars sum)
(polars col float_2 | polars count)
] | polars sort-by first
╭────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ plan │ SORT BY [col("first")] │
│ │ AGGREGATE │
│ │ [col("int_1").n_unique(), col("int_2").min(), col("float_1").sum()... │
│ optimized_plan │ SORT BY [col("first")] │
│ │ AGGREGATE │
│ │ [col("int_1").n_unique(), col("int_2").min(), col("float_1").sum()... │
╰────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
As you can see, the GroupBy object is a very powerful variable and it is worth keeping in memory while you explore your dataset.
Creating Dataframes
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 polars into-df.
> let a = ([[a b]; [1 2] [3 4] [5 6]] | polars into-df)
> $a
╭───┬───┬───╮
│ # │ a │ b │
├───┼───┼───┤
│ 0 │ 1 │ 2 │
│ 1 │ 3 │ 4 │
│ 2 │ 5 │ 6 │
╰───┴───┴───╯
TIP
For the time being, not all of Nushell primitives can be converted into a dataframe. This will change in the future, as the dataframe feature matures
We can append columns to a dataframe in order to create a new variable. As an example, let's append two columns to our mini dataframe $a
> let a2 = $a | polars with-column $a.a --name a2 | polars with-column $a.a --name a3
> $a2
╭───┬───┬───┬────┬────╮
│ # │ a │ b │ a2 │ a3 │
├───┼───┼───┼────┼────┤
│ 0 │ 1 │ 2 │ 1 │ 1 │
│ 1 │ 3 │ 4 │ 3 │ 3 │
│ 2 │ 5 │ 6 │ 5 │ 5 │
╰───┴───┴───┴────┴────╯
Nushell's powerful piping syntax allows us to create new dataframes by taking data from other dataframes and appending it to them. Now, if you list your dataframes you will see in total four dataframes
> polars store-ls
╭──────────────┬─────────┬─────────┬──────┬─────────────┬───────────────┬───────────────┬────────────┬──────────┬───────────────╮
│ key │ created │ columns │ rows │ type │ estimated_... │ span_contents │ span_start │ span_end │ reference_... │
├──────────────┼─────────┼─────────┼──────┼─────────────┼───────────────┼───────────────┼────────────┼──────────┼───────────────┤
│ 69bca897-... │ now │ 4 │ 1 │ DataFrame │ 32 B │ polars select │ 1988496 │ 1988509 │ 1 │
│ d87fcac0-... │ ❎ │ │ │ LazyGroupBy │ │ polars gro... │ 1991702 │ 1991717 │ 1 │
│ 43f53faa-... │ now │ 8 │ 10 │ DataFrame │ 403 B │ polars open │ 1987476 │ 1987487 │ 1 │
│ 16009092-... │ now │ 5 │ 4 │ DataFrame │ 132 B │ polars open │ 1989333 │ 1989344 │ 1 │
│ a76be7b3-... │ now │ 4 │ 3 │ DataFrame │ 96 B │ polars wit... │ 1991468 │ 1991486 │ 1 │
│ cb6b04ca-... │ now │ 2 │ 3 │ DataFrame │ 48 B │ polars int... │ 1991136 │ 1991150 │ 1 │
╰──────────────┴─────────┴─────────┴──────┴─────────────┴───────────────┴───────────────┴────────────┴──────────┴───────────────╯
One thing that is important to mention is how the memory is being optimized while working with dataframes, and this is thanks to Apache Arrow and Polars. In a very simple representation, each column in a DataFrame is an Arrow Array, which is using several memory specifications in order to maintain the data as packed as possible (check Arrow columnar format). 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 polars 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.
Working with Series
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 polars into-df command:
> let new = ([9 8 4] | polars into-df)
> $new
╭───┬───╮
│ # │ 0 │
├───┼───┤
│ 0 │ 9 │
│ 1 │ 8 │
│ 2 │ 4 │
╰───┴───╯
We have created a new series from a list of integers (we could have done the same using floats or strings)
Series have their own basic operations defined, and they can be used to create other Series. Let's create a new Series by doing some arithmetic on the previously created column.
> let new_2 = ($new * 3 + 10)
> $new_2
╭───┬────╮
│ # │ 0 │
├───┼────┤
│ 0 │ 37 │
│ 1 │ 34 │
│ 2 │ 22 │
╰───┴────╯
Now we have a new Series that was constructed by doing basic operations on the previous variable.
TIP
If you want to see how many variables you have stored in memory you can use scope variables
Let's rename our previous Series so it has a memorable name
> let new_2 = $new_2 | polars rename "0" memorable
> $new_2
╭───┬───────────╮
│ # │ memorable │
├───┼───────────┤
│ 0 │ 37 │
│ 1 │ 34 │
│ 2 │ 22 │
╰───┴───────────╯
We can also do basic operations with two Series as long as they have the same data type
> $new - $new_2
╭───┬─────────────────╮
│ # │ sub_0_memorable │
├───┼─────────────────┤
│ 0 │ -28 │
│ 1 │ -26 │
│ 2 │ -18 │
╰───┴─────────────────╯
And we can add them to previously defined dataframes
> let new_df = $a | polars with-column $new --name new_col
> $new_df
╭───┬───┬───┬─────────╮
│ # │ a │ b │ new_col │
├───┼───┼───┼─────────┤
│ 0 │ 1 │ 2 │ 9 │
│ 1 │ 3 │ 4 │ 8 │
│ 2 │ 5 │ 6 │ 4 │
╰───┴───┴───┴─────────╯
The Series stored in a Dataframe can also be used directly, for example, we can multiply columns a and b to create a new Series
> $new_df.a * $new_df.b
╭───┬─────────╮
│ # │ mul_a_b │
├───┼─────────┤
│ 0 │ 2 │
│ 1 │ 12 │
│ 2 │ 30 │
╰───┴─────────╯
and we can start piping things in order to create new columns and dataframes
> let $new_df = $new_df | polars with-column ($new_df.a * $new_df.b / $new_df.new_col) --name my_sum
> $new_df
╭───┬───┬───┬─────────┬────────╮
│ # │ a │ b │ new_col │ my_sum │
├───┼───┼───┼─────────┼────────┤
│ 0 │ 1 │ 2 │ 9 │ 0 │
│ 1 │ 3 │ 4 │ 8 │ 1 │
│ 2 │ 5 │ 6 │ 4 │ 7 │
╰───┴───┴───┴─────────┴────────╯
Nushell's piping system can help you create very interesting workflows.
Series and masks
Series have another key use in when working with DataFrames, and it is the fact that we can build boolean masks out of them. Let's start by creating a simple mask using the equality operator
> let mask = $new == 8
> $mask
╭───┬───────╮
│ # │ 0 │
├───┼───────┤
│ 0 │ false │
│ 1 │ true │
│ 2 │ false │
╰───┴───────╯
and with this mask we can now filter a dataframe, like this
> $new_df | polars filter-with $mask
╭───┬───┬───┬─────────┬────────╮
│ # │ a │ b │ new_col │ my_sum │
├───┼───┼───┼─────────┼────────┤
│ 0 │ 3 │ 4 │ 8 │ 1 │
╰───┴───┴───┴─────────┴────────╯
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:
> let mask1 = [true true false] | polars into-df
> $new_df | polars filter-with $mask1
╭───┬───┬───┬─────────┬────────╮
│ # │ a │ b │ new_col │ my_sum │
├───┼───┼───┼─────────┼────────┤
│ 0 │ 1 │ 2 │ 9 │ 0 │
│ 1 │ 3 │ 4 │ 8 │ 1 │
╰───┴───┴───┴─────────┴────────╯
To create complex masks, we have the AND
> $mask and $mask1
╭───┬─────────╮
│ # │ and_0_0 │
├───┼─────────┤
│ 0 │ false │
│ 1 │ true │
│ 2 │ false │
╰───┴─────────╯
and OR operations
> $mask or $mask1
╭───┬────────╮
│ # │ or_0_0 │
├───┼────────┤
│ 0 │ true │
│ 1 │ true │
│ 2 │ false │
╰───┴────────╯
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
> let mask3 = $df | polars col first | polars is-in [b c]
> $mask3
╭──────────┬────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ input │ [table 2 rows] │
│ function │ Boolean(IsIn) │
│ options │ FunctionOptions { collect_groups: ElementWise, fmt_str: "", input_wildcard_expansion: false, returns_scalar: fa... │
╰──────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
and this new mask can be used to filter the dataframe
> $df | polars filter-with $mask3
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
│ 0 │ 4 │ 14 │ 0.40 │ 3.00 │ b │ a │ c │ second │
│ 1 │ 0 │ 15 │ 0.50 │ 4.00 │ b │ a │ a │ third │
│ 2 │ 6 │ 16 │ 0.60 │ 5.00 │ b │ a │ a │ second │
│ 3 │ 7 │ 17 │ 0.70 │ 6.00 │ b │ c │ a │ third │
│ 4 │ 8 │ 18 │ 0.80 │ 7.00 │ c │ c │ b │ eight │
│ 5 │ 9 │ 19 │ 0.90 │ 8.00 │ c │ c │ b │ ninth │
│ 6 │ 0 │ 10 │ 0.00 │ 9.00 │ c │ c │ b │ ninth │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
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.
> $df | polars get first | polars set new --mask ($df.first =~ a)
╭───┬────────╮
│ # │ string │
├───┼────────┤
│ 0 │ new │
│ 1 │ new │
│ 2 │ new │
│ 3 │ b │
│ 4 │ b │
│ 5 │ b │
│ 6 │ b │
│ 7 │ c │
│ 8 │ c │
│ 9 │ c │
╰───┴────────╯
Series as indices
Series can be also used as a way of filtering a dataframe by using them as a list of indices. For example, let's say that we want to get rows 1, 4, and 6 from our original dataframe. With that in mind, we can use the next command to extract that information
> let indices = [1 4 6] | polars into-df
> $df | polars take $indices
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
│ 0 │ 2 │ 12 │ 0.20 │ 1.00 │ a │ b │ c │ second │
│ 1 │ 0 │ 15 │ 0.50 │ 4.00 │ b │ a │ a │ third │
│ 2 │ 7 │ 17 │ 0.70 │ 6.00 │ b │ c │ a │ third │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
The command polars take is very handy, especially if we mix it with other commands. Let's say that we want to extract all rows for the first duplicated element for column first. In order to do that, we can use the command polars arg-unique as shown in the next example
> let indices = $df | polars get first | polars arg-unique
> $df | polars take $indices
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
│ 0 │ 1 │ 11 │ 0.10 │ 1.00 │ a │ b │ c │ first │
│ 1 │ 4 │ 14 │ 0.40 │ 3.00 │ b │ a │ c │ second │
│ 2 │ 8 │ 18 │ 0.80 │ 7.00 │ c │ c │ b │ eight │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
Or what if we want to create a new sorted dataframe using a column in specific. We can use the arg-sort to accomplish that. In the next example we can sort the dataframe by the column word
TIP
The same result could be accomplished using the command sort
> let indices = $df | polars get word | polars arg-sort
> $df | polars take $indices
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
│ 0 │ 8 │ 18 │ 0.80 │ 7.00 │ c │ c │ b │ eight │
│ 1 │ 1 │ 11 │ 0.10 │ 1.00 │ a │ b │ c │ first │
│ 2 │ 9 │ 19 │ 0.90 │ 8.00 │ c │ c │ b │ ninth │
│ 3 │ 0 │ 10 │ 0.00 │ 9.00 │ c │ c │ b │ ninth │
│ 4 │ 2 │ 12 │ 0.20 │ 1.00 │ a │ b │ c │ second │
│ 5 │ 4 │ 14 │ 0.40 │ 3.00 │ b │ a │ c │ second │
│ 6 │ 6 │ 16 │ 0.60 │ 5.00 │ b │ a │ a │ second │
│ 7 │ 3 │ 13 │ 0.30 │ 2.00 │ a │ b │ c │ third │
│ 8 │ 0 │ 15 │ 0.50 │ 4.00 │ b │ a │ a │ third │
│ 9 │ 7 │ 17 │ 0.70 │ 6.00 │ b │ c │ a │ third │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
And finally, we can create new Series by setting a new value in the marked indices. Have a look at the next command
> let indices = [0 2] | polars into-df
> $df | polars get int_1 | polars set-with-idx 123 --indices $indices
╭───┬───────╮
│ # │ int_1 │
├───┼───────┤
│ 0 │ 123 │
│ 1 │ 2 │
│ 2 │ 123 │
│ 3 │ 4 │
│ 4 │ 0 │
│ 5 │ 6 │
│ 6 │ 7 │
│ 7 │ 8 │
│ 8 │ 9 │
│ 9 │ 0 │
╰───┴───────╯
Unique values
Another operation that can be done with Series is to search for unique values in a list or column. Lets use again the first dataframe we created to test these operations.
The first and most common operation that we have is value_counts. This command calculates a count of the unique values that exist in a Series. For example, we can use it to count how many occurrences we have in the column first
> $df | polars get first | polars value-counts
╭───┬───────┬───────╮
│ # │ first │ count │
├───┼───────┼───────┤
│ 0 │ b │ 4 │
│ 1 │ a │ 3 │
│ 2 │ c │ 3 │
╰───┴───────┴───────╯
As expected, the command returns a new dataframe that can be used to do more queries.
Continuing with our exploration of Series, the next thing that we can do is to only get the unique unique values from a series, like this
> $df | polars get first | polars unique
╭───┬───────╮
│ # │ first │
├───┼───────┤
│ 0 │ c │
│ 1 │ a │
│ 2 │ b │
╰───┴───────╯
Or we can get a mask that we can use to filter out the rows where data is unique or duplicated. For example, we can select the rows for unique values in column word
> $df | polars filter-with ($df | polars get word | polars is-unique)
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬───────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼───────┤
│ 0 │ 1 │ 11 │ 0.10 │ 1.00 │ a │ b │ c │ first │
│ 1 │ 8 │ 18 │ 0.80 │ 7.00 │ c │ c │ b │ eight │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴───────╯
Or all the duplicated ones
> $df | polars filter-with ($df | polars get word | polars is-duplicated)
╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
│ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │ word │
├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
│ 0 │ 2 │ 12 │ 0.20 │ 1.00 │ a │ b │ c │ second │
│ 1 │ 3 │ 13 │ 0.30 │ 2.00 │ a │ b │ c │ third │
│ 2 │ 4 │ 14 │ 0.40 │ 3.00 │ b │ a │ c │ second │
│ 3 │ 0 │ 15 │ 0.50 │ 4.00 │ b │ a │ a │ third │
│ 4 │ 6 │ 16 │ 0.60 │ 5.00 │ b │ a │ a │ second │
│ 5 │ 7 │ 17 │ 0.70 │ 6.00 │ b │ c │ a │ third │
│ 6 │ 9 │ 19 │ 0.90 │ 8.00 │ c │ c │ b │ ninth │
│ 7 │ 0 │ 10 │ 0.00 │ 9.00 │ c │ c │ b │ ninth │
╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
Lazy Dataframes
Lazy dataframes are a way to query data by creating a logical plan. The advantage of this approach is that the plan never gets evaluated until you need to extract data. This way you could chain together aggregations, joins and selections and collect the data once you are happy with the selected operations.
Let's create a small example of a lazy dataframe
> let a = [[a b]; [1 a] [2 b] [3 c] [4 d]] | polars into-lazy
> $a
╭────────────────┬───────────────────────────────────────────────────────╮
│ plan │ DF ["a", "b"]; PROJECT */2 COLUMNS; SELECTION: "None" │
│ optimized_plan │ DF ["a", "b"]; PROJECT */2 COLUMNS; SELECTION: "None" │
╰────────────────┴───────────────────────────────────────────────────────╯
As you can see, the resulting dataframe is not yet evaluated, it stays as a set of instructions that can be done on the data. If you were to collect that dataframe you would get the next result
> $a | polars collect
╭───┬───┬───╮
│ # │ a │ b │
├───┼───┼───┤
│ 0 │ 1 │ a │
│ 1 │ 2 │ b │
│ 2 │ 3 │ c │
│ 3 │ 4 │ d │
╰───┴───┴───╯
as you can see, the collect command executes the plan and creates a nushell table for you.
All dataframes operations should work with eager or lazy dataframes. They are converted in the background for compatibility. However, to take advantage of lazy operations if is recommended to only use lazy operations with lazy dataframes.
To find all lazy dataframe operations you can use
scope commands | where category =~ lazyframe | select name category usage
With your lazy frame defined we can start chaining operations on it. For example this
$a |
polars reverse |
polars with-column [
((polars col a) * 2 | polars as double_a)
((polars col a) / 2 | polars as half_a)
] | polars collect
╭───┬───┬───┬──────────┬────────╮
│ # │ a │ b │ double_a │ half_a │
├───┼───┼───┼──────────┼────────┤
│ 0 │ 4 │ d │ 8 │ 2 │
│ 1 │ 3 │ c │ 6 │ 1 │
│ 2 │ 2 │ b │ 4 │ 1 │
│ 3 │ 1 │ a │ 2 │ 0 │
╰───┴───┴───┴──────────┴────────╯
TIP
You can use the line buffer editor to format your queries (ctr + o) easily
This query uses the lazy reverse command to invert the dataframe and the polars with-column command to create new two columns using expressions. An expression is used to define an operation that is executed on the lazy frame. When put together they create the whole set of instructions used by the lazy commands to query the data. To list all the commands that generate an expression you can use
scope commands | where category =~ expression | select name category usage
In our previous example, we use the polars col command to indicate that column a will be multiplied by 2 and then it will be aliased to the name double_a. In some cases the use of the polars col command can be inferred. For example, using the polars select command we can use only a string
> $a | polars select a | polars collect
╭───┬───╮
│ # │ a │
├───┼───┤
│ 0 │ 1 │
│ 1 │ 2 │
│ 2 │ 3 │
│ 3 │ 4 │
╰───┴───╯
or the polars col command
> $a | polars select (polars col a) | polars collect
╭───┬───╮
│ # │ a │
├───┼───┤
│ 0 │ 1 │
│ 1 │ 2 │
│ 2 │ 3 │
│ 3 │ 4 │
╰───┴───╯
Let's try something more complicated and create aggregations from a lazy dataframe
let a = ( [[name value]; [one 1] [two 2] [one 1] [two 3]] | polars into-lazy )
$a
| polars group-by name
| polars agg [
(polars col value | polars sum | polars as sum)
(polars col value | polars mean | polars as mean)
] | polars collect
╭───┬──────┬─────┬──────╮
│ # │ name │ sum │ mean │
├───┼──────┼─────┼──────┤
│ 0 │ one │ 2 │ 1.00 │
│ 1 │ two │ 5 │ 2.50 │
╰───┴──────┴─────┴──────╯
And we could join on a lazy dataframe that hasn't being collected. Let's join the resulting group by to the original lazy frame
let a = ( [[name value]; [one 1] [two 2] [one 1] [two 3]] | polars into-lazy )
let group = ($a
| polars group-by name
| polars agg [
(polars col value | polars sum | polars as sum)
(polars col value | polars mean | polars as mean)
]
)
$a | polars join $group name name | polars collect
╭───┬──────┬───────┬─────┬──────╮
│ # │ name │ value │ sum │ mean │
├───┼──────┼───────┼─────┼──────┤
│ 0 │ one │ 1 │ 2 │ 1.00 │
│ 1 │ two │ 2 │ 5 │ 2.50 │
│ 2 │ one │ 1 │ 2 │ 1.00 │
│ 3 │ two │ 3 │ 5 │ 2.50 │
╰───┴──────┴───────┴─────┴──────╯
As you can see lazy frames are a powerful construct that will let you query data using a flexible syntax, resulting in blazing fast results.
Dataframe commands
So far we have seen quite a few operations that can be done using DataFrames commands. However, the commands we have used so far are not all the commands available to work with data and be assured that there will be more as the feature becomes more stable.
The next list shows the available dataframe commands with their descriptions, and whenever possible, their analogous Nushell command.
WARNING
This list may be outdated. To get the up-to-date command list, see DataframeLazyframe and Dataframe Or Lazyframe command categories.
| Command Name | Applies To | Description | Nushell Equivalent |
|---|---|---|---|
| aggregate | DataFrame, GroupBy, Series | Performs an aggregation operation on a dataframe, groupby or series object | math |
| all-false | Series | Returns true if all values are false | |
| all-true | Series | Returns true if all values are true | all |
| arg-max | Series | Return index for max value in series | |
| arg-min | Series | Return index for min value in series | |
| arg-sort | Series | Returns indexes for a sorted series | |
| arg-true | Series | Returns indexes where values are true | |
| arg-unique | Series | Returns indexes for unique values | |
| count-null | Series | Counts null values | |
| count-unique | Series | Counts unique value | |
| drop | DataFrame | Creates a new dataframe by dropping the selected columns | drop |
| drop-duplicates | DataFrame | Drops duplicate values in dataframe | |
| drop-nulls | DataFrame, Series | Drops null values in dataframe | |
| dtypes | DataFrame | Show dataframe data types | |
| filter-with | DataFrame | Filters dataframe using a mask as reference | |
| first | DataFrame | Creates new dataframe with first rows | first |
| get | DataFrame | Creates dataframe with the selected columns | get |
| group-by | DataFrame | Creates a groupby object that can be used for other aggregations | group-by |
| is-duplicated | Series | Creates mask indicating duplicated values | |
| is-in | Series | Checks if elements from a series are contained in right series | in |
| is-not-null | Series | Creates mask where value is not null | |
| is-null | Series | Creates mask where value is null | <column_name> == null |
| is-unique | Series | Creates mask indicating unique values | |
| join | DataFrame | Joins a dataframe using columns as reference | |
| last | DataFrame | Creates new dataframe with last rows | last |
| ls-df | Lists stored dataframes | ||
| melt | DataFrame | Unpivot a DataFrame from wide to long format | |
| not | Series Inverts boolean mask | ||
| open | Loads dataframe form csv file | open | |
| pivot | GroupBy | Performs a pivot operation on a groupby object | pivot |
| rename | Dataframe, Series | Renames a series | rename |
| sample | DataFrame | Create sample dataframe | |
| select | DataFrame | Creates a new dataframe with the selected columns | select |
| set | Series | Sets value where given mask is true | |
| set-with-idx | Series | Sets value in the given index | |
| shift | Series | Shifts the values by a given period | |
| show | DataFrame | Converts a section of the dataframe to a Table or List value | |
| 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 |
| 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 |
| with-column | DataFrame | Adds a series to the dataframe | insert <column_name> <value> | upsert <column_name> { <new_value> } |
Future of Dataframes
We hope that by the end of this page you have a solid grasp of how to use the dataframe commands. As you can see they offer powerful operations that can help you process data faster and natively.
However, the future of these dataframes is still very experimental. New commands and tools that take advantage of these commands will be added as they mature.
Keep visiting this book in order to check the new things happening to dataframes and how they can help you process data faster and efficiently.