defmodule Explorer.DataFrame do
@moduledoc """
The DataFrame struct and API.
Dataframes are two-dimensional tabular data structures similar to a spreadsheet.
For example, the Iris dataset:
iex> Explorer.Datasets.iris()
#Explorer.DataFrame<
Polars[150 x 5]
sepal_length f64 [5.1, 4.9, 4.7, 4.6, 5.0, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.1, 3.6, ...]
petal_length f64 [1.4, 1.4, 1.3, 1.5, 1.4, ...]
petal_width f64 [0.2, 0.2, 0.2, 0.2, 0.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
This dataframe has 150 rows and five columns. Each column is an `Explorer.Series`
of the same size (150):
iex> df = Explorer.Datasets.iris()
iex> df["sepal_length"]
#Explorer.Series<
Polars[150]
f64 [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, ...]
>
## Creating dataframes
Dataframes can be created from normal Elixir terms. The main way you might do this is
with the `new/1` function. For example:
iex> Explorer.DataFrame.new(a: ["a", "b"], b: [1, 2])
#Explorer.DataFrame<
Polars[2 x 2]
a string ["a", "b"]
b s64 [1, 2]
>
Or with a list of maps:
iex> Explorer.DataFrame.new([%{"col1" => "a", "col2" => 1}, %{"col1" => "b", "col2" => 2}])
#Explorer.DataFrame<
Polars[2 x 2]
col1 string ["a", "b"]
col2 s64 [1, 2]
>
## Verbs
Explorer uses the idea of a consistent set of SQL-like `verbs` like
[`dplyr`](https://dplyr.tidyverse.org) which can help solve common
data manipulation challenges. These are split into single table verbs,
multiple table verbs, and row-based verbs:
### Single table verbs
Single table verbs are (unsurprisingly) used for manipulating a single dataframe.
Those operations typically driven by column names. These are:
- `select/2` for picking columns and `discard/2` to discard them
- `filter/2` for picking rows based on predicates
- `mutate/2` for adding or replacing columns that are functions of existing columns
- `sort_by/2` for changing the ordering of rows
- `distinct/2` for picking unique rows
- `summarise/2` for reducing multiple rows down to a single summary
- `pivot_longer/3` and `pivot_wider/4` for massaging dataframes into longer or
wider forms, respectively
Each of these combine with `Explorer.DataFrame.group_by/2` for operating by group.
### Multiple table verbs
Multiple table verbs are used for combining tables. These are:
- `join/3` for performing SQL-like joins
- `concat_columns/1` for horizontally "stacking" dataframes
- `concat_rows/1` for vertically "stacking" dataframes
### Row-based verbs
Those operations are driven by the row index. These are:
- `head/2` for picking the first rows
- `tail/2` for picking the last rows
- `slice/2` for slicing the dataframe by row indexes or a range
- `slice/3` for slicing a section by an offset
- `sample/2` for sampling the data-frame by row
## IO operations
Explorer supports reading and writing of:
- delimited files (such as CSV or TSV)
- [Parquet](https://databricks.com/glossary/what-is-parquet)
- [Arrow IPC](https://arrow.apache.org/docs/format/Columnar.html#ipc-file-format)
- [Arrow Streaming IPC](https://arrow.apache.org/docs/format/Columnar.html#ipc-streaming-format)
- [Newline Delimited JSON](http://ndjson.org)
- Databases via `Adbc` in `from_query/3`
The convention Explorer uses is to have `from_*` and `to_*` functions to read and write
to files in the formats above. `load_*` and `dump_*` versions are also available to read
and write those formats directly in memory.
Files can be fetched from local or remote file system, such as S3, using the following formats:
# path to a file in disk
Explorer.DataFrame.from_parquet("/path/to/file.parquet")
# path to a URL schema (with optional configuration)
Explorer.DataFrame.from_parquet("s3://bucket/file.parquet", config: FSS.S3.config_from_system_env())
# it's possible to configure using keyword lists
Explorer.DataFrame.from_parquet("s3://bucket/file.parquet", config: [access_key_id: "my-key", secret_access_key: "my-secret"])
# a FSS entry (it already includes its config)
Explorer.DataFrame.from_parquet(FSS.S3.parse("s3://bucket/file.parquet"))
The `:config` option of `from_*` functions is only required if the filename is a path
to a remote resource. In case it's a FSS entry, the requirement is that the config is passed
inside the entry struct.
For more details about the options, see the [FSS docs](https://hexdocs.pm/fss).
## Selecting columns and access
Several functions in this module, such as `select/2`, `discard/2`, `drop_nil/2`, and so
forth accept a single or multiple columns as arguments. The columns can be specified in
a variety of formats, which we describe below.
`Explorer.DataFrame` also implements the `Access` behaviour (also known as the brackets
syntax). This should be familiar for users coming from other language with dataframes
such as R or Python. For example:
iex> df = Explorer.Datasets.wine()
iex> df["class"]
#Explorer.Series<
Polars[178]
s64 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...]
>
Accessing the dataframe with a column name either as a string or an atom, will return
the column. You can also pass an integer representing the column order:
iex> df = Explorer.Datasets.wine()
iex> df[0]
#Explorer.Series<
Polars[178]
s64 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...]
>
You can also pass a list, a range, or a regex to return a dataframe matching
the given data type. For example, by passing a list:
iex> df = Explorer.Datasets.wine()
iex> df[["class", "hue"]]
#Explorer.DataFrame<
Polars[178 x 2]
class s64 [1, 1, 1, 1, 1, ...]
hue f64 [1.04, 1.05, 1.03, 0.86, 1.04, ...]
>
Or a range for the given positions:
iex> df = Explorer.Datasets.wine()
iex> df[0..2]
#Explorer.DataFrame<
Polars[178 x 3]
class s64 [1, 1, 1, 1, 1, ...]
alcohol f64 [14.23, 13.2, 13.16, 14.37, 13.24, ...]
malic_acid f64 [1.71, 1.78, 2.36, 1.95, 2.59, ...]
>
Or a regex to keep only columns matching a given pattern:
iex> df = Explorer.Datasets.wine()
iex> df[~r/(class|hue)/]
#Explorer.DataFrame<
Polars[178 x 2]
class s64 [1, 1, 1, 1, 1, ...]
hue f64 [1.04, 1.05, 1.03, 0.86, 1.04, ...]
>
Given you can also access a series using its index, you can use
multiple accesses to select a column and row at the same time:
iex> df = Explorer.Datasets.wine()
iex> df["class"][3]
1
"""
alias __MODULE__, as: DataFrame
alias Explorer.Series
alias Explorer.Shared
alias Explorer.Backend.LazySeries
alias FSS.HTTP
alias FSS.Local
alias FSS.S3
@enforce_keys [:data, :groups, :names, :dtypes]
defstruct [:data, :groups, :names, :dtypes]
@typedoc """
Represents a column name as atom or string.
"""
@type column_name :: atom() | String.t()
@typedoc """
Represents a column name or its index.
"""
@type column :: column_name() | non_neg_integer()
@typedoc """
Represents multiple columns.
The columns may be specified as one of:
* a list of columns indexes or names as atoms and strings
* a range
* a regex that keeps only the names matching the regex
* a one-arity function that receives column names and returns
true for column names to keep
* a two-arity function that receives column names and types and
returns true for column names to keep
"""
@type columns ::
[column]
| Range.t()
| (String.t() -> boolean())
| (String.t(), Explorer.Series.dtype() -> boolean())
@typedoc """
Represents multiple column names as atoms or strings.
"""
@type column_names :: [column_name]
@typedoc """
Represents a column pair where the value is a column name or
a column index, and the value is of type `value`.
"""
@type column_pairs(value) :: [{column(), value}] | %{column() => value}
@typedoc """
Represents a filesystem entry, that can be local, S3 or URL.
"""
@type fs_entry :: FSS.entry()
@typedoc """
Represents a dataframe.
"""
@type t :: %DataFrame{
data: Explorer.Backend.DataFrame.t(),
groups: [String.t()],
names: [String.t()],
dtypes: %{String.t() => Explorer.Series.dtype()}
}
@default_infer_schema_length 1000
@default_sample_nrows 5
@integer_types Explorer.Shared.integer_types()
# Guards and helpers for columns
defguardp is_column(column) when is_binary(column) or is_atom(column) or is_integer(column)
defguardp is_column_name(column) when is_binary(column) or is_atom(column)
defguardp is_column_pairs(columns) when is_list(columns) or is_map(columns)
# Normalize a column name to string
defp to_column_name(column) when is_binary(column), do: column
defp to_column_name(column) when is_atom(column), do: Atom.to_string(column)
# Normalize pairs of `{column, value}` where value can be anything.
# The `column` is only validated if it's an integer. We check that the index is present.
# The function allows to change the `value` for each pair.
defp to_column_pairs(df, pairs, value_fun)
when is_column_pairs(pairs) and is_function(value_fun, 1) do
existing_columns = df.names
pairs
|> Enum.map_reduce(nil, fn
{column, value}, maybe_map when is_integer(column) ->
map = maybe_map || column_index_map(existing_columns)
existing_column = fetch_column_at!(map, column)
{{existing_column, value_fun.(value)}, map}
{column, value}, maybe_map when is_atom(column) ->
column = Atom.to_string(column)
{{column, value_fun.(value)}, maybe_map}
{column, value}, maybe_map when is_binary(column) ->
{{column, value_fun.(value)}, maybe_map}
end)
|> then(fn {pairs, _} -> pairs end)
end
defp fetch_column_at!(map, index) do
normalized = if index < 0, do: index + map_size(map), else: index
case map do
%{^normalized => column} -> column
%{} -> raise ArgumentError, "no column exists at index #{index}"
end
end
defp column_index_map(names),
do: for({name, idx} <- Enum.with_index(names), into: %{}, do: {idx, name})
# Normalizes column names and raise if column does not exist.
defp to_existing_columns(df, columns, raise? \\ true) do
Explorer.Shared.to_existing_columns(df, columns, raise?)
end
# Normalizes the "columns" option for some IO operations.
defp to_columns_for_io(nil), do: nil
defp to_columns_for_io([h | _] = columns) when is_integer(h) do
if Enum.all?(columns, &is_integer/1) do
columns
else
raise ArgumentError,
"expected :columns to be a list of only integers, only atoms, or only binaries, " <>
"got: #{inspect(columns)}"
end
end
defp to_columns_for_io([h | _] = columns) when is_column_name(h),
do: Enum.map(columns, &to_column_name/1)
defp to_columns_for_io(other) do
raise ArgumentError,
"expected :columns to be a list of only integers, only atoms, or only binaries, " <>
"got: #{inspect(other)}"
end
defp check_dtypes!(dtypes) do
Map.new(dtypes, fn
{key, value} when is_atom(key) ->
{Atom.to_string(key), check_dtype!(key, value)}
{key, value} when is_binary(key) ->
{key, check_dtype!(key, value)}
_ ->
raise ArgumentError,
"dtypes must be a list/map of column names as keys and types as values, " <>
"where the keys are atoms or binaries. Got: #{inspect(dtypes)}"
end)
end
defp check_dtype!(key, value) do
if dtype = Shared.normalise_dtype(value) do
dtype
else
raise ArgumentError,
"invalid dtype #{inspect(value)} for #{inspect(key)} (expected one of #{inspect(Shared.dtypes())})"
end
end
# Access
@behaviour Access
@compile {:no_warn_undefined, Nx}
@impl true
def fetch(df, column) when is_column(column) do
{:ok, pull(df, column)}
end
def fetch(df, columns) do
columns = to_existing_columns(df, columns)
{:ok, select(df, columns)}
end
@impl true
def pop(df, column) when is_column(column) do
[column] = to_existing_columns(df, [column])
{pull_existing(df, column), discard(df, [column])}
end
def pop(df, columns) do
columns = to_existing_columns(df, columns)
{select(df, columns), discard(df, columns)}
end
# Notice that the resultant series is going to have the `:name` field
# equals to the column name.
@impl true
def get_and_update(df, column, fun) when is_column(column) do
[column] = to_existing_columns(df, [column])
value = pull_existing(df, column)
{current_value, new_value} = fun.(value)
new_df = put(df, column, new_value)
{%{current_value | name: column}, new_df}
end
# IO
@doc """
Reads data from a query.
`conn` must be a `Adbc.Connection` process. `sql` is a string representing
the sql query and `params` is the list of query parameters. See `Adbc`
for more information.
## Example
In order to read data from a database, you must list `:adbc` as a dependency,
download the relevant driver, and start both database and connection processes
in your supervision tree.
First, add `:adbc` as a dependency in your `mix.exs`:
{:adbc, "~> 0.1"}
Now, in your config/config.exs, configure the drivers you are going to use
(see `Adbc` module docs for more information on supported drivers):
config :adbc, :drivers, [:sqlite]
If you are using a notebook or scripting, you can also use `Adbc.download_driver!/1`
to dynamically download one.
Then start the database and the relevant connection processes in your
supervision tree:
children = [
{Adbc.Database,
driver: :sqlite,
process_options: [name: MyApp.DB]},
{Adbc.Connection,
database: MyApp.DB,
process_options: [name: MyApp.Conn]}
]
Supervisor.start_link(children, strategy: :one_for_one)
In a notebook, the above would look like this:
db = Kino.start_child!({Adbc.Database, driver: :sqlite})
conn = Kino.start_child!({Adbc.Connection, database: db})
And now you can make queries with:
# For named connections
{:ok, _} = Explorer.DataFrame.from_query(MyApp.Conn, "SELECT 123", [])
# When using the conn PID directly
{:ok, _} = Explorer.DataFrame.from_query(conn, "SELECT 123", [])
## Options
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
"""
@doc type: :io
@spec from_query(
Adbc.Connection.t(),
query :: String.t(),
params :: list(term),
opts :: Keyword.t()
) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def from_query(conn, query, params, opts \\ [])
if Code.ensure_loaded?(Adbc) do
def from_query(conn, query, params, opts)
when is_binary(query) and is_list(params) and is_list(opts) do
backend = backend_from_options!(opts)
backend.from_query(conn, query, params)
end
else
def from_query(_conn, _query, _params, _opts) do
raise "you must install :adbc as a dependency in order to use from_query/3"
end
end
@doc """
Similar to `from_query/4` but raises if there is an error.
"""
@doc type: :io
@spec from_query!(
Adbc.Connection.t(),
query :: String.t(),
params :: list(term),
opts :: Keyword.t()
) ::
DataFrame.t()
def from_query!(conn, query, params, opts \\ []) do
case from_query(conn, query, params, opts) do
{:ok, df} -> df
{:error, error} -> raise error
end
end
@doc """
Reads a delimited file into a dataframe.
It accepts a filename that can be a local file, a "s3://" schema, or
a `FSS` entry like `FSS.S3.Entry`.
If the CSV is compressed, it is automatically decompressed.
## Options
* `:delimiter` - A single character used to separate fields within a record. (default: `","`)
* `:dtypes` - A list/map of `{"column_name", dtype}` tuples. Any non-specified column has its type
imputed from the first 1000 rows. (default: `[]`)
* `:header` - Does the file have a header of column names as the first row or not? (default: `true`)
* `:max_rows` - Maximum number of lines to read. (default: `nil`)
* `:nil_values` - A list of strings that should be interpreted as a nil values. (default: `[]`)
* `:skip_rows` - The number of lines to skip at the beginning of the file. (default: `0`)
* `:skip_rows_after_header` - The number of lines to skip after the header row. (default: `0`)
* `:columns` - A list of column names or indexes to keep.
If present, only these columns are read into the dataframe. (default: `nil`)
* `:infer_schema_length` Maximum number of rows read for schema inference.
Setting this to nil will do a full table scan and will be slow (default: `1000`).
* `:parse_dates` - Automatically try to parse dates/ datetimes and time.
If parsing fails, columns remain of dtype `string`
* `:eol_delimiter` - A single character used to represent new lines. (default: `"\n"`)
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
* `:encoding` - Encoding to use when reading the file. For now, the only possible values are `utf8` and `utf8-lossy`.
The utf8-lossy option means that invalid utf8 values are replaced with � characters. (default: `"utf8"`)
"""
@doc type: :io
@spec from_csv(filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def from_csv(filename, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
config: nil,
delimiter: ",",
dtypes: [],
encoding: "utf8",
header: true,
max_rows: nil,
nil_values: [],
skip_rows: 0,
skip_rows_after_header: 0,
columns: nil,
infer_schema_length: @default_infer_schema_length,
parse_dates: false,
eol_delimiter: nil
)
backend = backend_from_options!(backend_opts)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
backend.from_csv(
entry,
check_dtypes!(opts[:dtypes]),
opts[:delimiter],
opts[:nil_values],
opts[:skip_rows],
opts[:skip_rows_after_header],
opts[:header],
opts[:encoding],
opts[:max_rows],
to_columns_for_io(opts[:columns]),
opts[:infer_schema_length],
opts[:parse_dates],
opts[:eol_delimiter]
)
end
end
@doc """
Similar to `from_csv/2` but raises if there is a problem reading the CSV.
"""
@doc type: :io
@spec from_csv!(filename :: String.t() | fs_entry(), opts :: Keyword.t()) :: DataFrame.t()
def from_csv!(filename, opts \\ []) do
case from_csv(filename, opts) do
{:ok, df} ->
df
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "from_csv failed: #{inspect(e.message)}"
{:error, error} ->
raise "from_csv failed: #{inspect(error)}"
end
end
@doc """
Reads a representation of a CSV file into a dataframe.
If the CSV is compressed, it is automatically decompressed.
## Options
* `:delimiter` - A single character used to separate fields within a record. (default: `","`)
* `:dtypes` - A list/map of `{"column_name", dtype}` tuples. Any non-specified column has its type
imputed from the first 1000 rows. (default: `[]`)
* `:header` - Does the file have a header of column names as the first row or not? (default: `true`)
* `:max_rows` - Maximum number of lines to read. (default: `nil`)
* `:nil_values` - A list of strings that should be interpreted as a nil values. (default: `[]`)
* `:skip_rows` - The number of lines to skip at the beginning of the file. (default: `0`)
* `:skip_rows_after_header` - The number of lines to skip after the heqader row. (default: `0`)
* `:columns` - A list of column names or indexes to keep. If present, only these columns are read into the dataframe. (default: `nil`)
* `:infer_schema_length` Maximum number of rows read for schema inference. Setting this to nil will do a full table scan and will be slow (default: `1000`).
* `:parse_dates` - Automatically try to parse dates/ datetimes and time. If parsing fails, columns remain of dtype `string`
* `:eol_delimiter` - A single character used to represent new lines. (default: `"\n"`)
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
* `:encoding` - Encoding to use when reading the file. For now, the only possible values are
`utf8` and `utf8-lossy`. The utf8-lossy option means that invalid utf8 values are
replaced with � characters. (default: `"utf8"`)
"""
@doc type: :io
@spec load_csv(contents :: String.t(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def load_csv(contents, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
delimiter: ",",
dtypes: [],
encoding: "utf8",
header: true,
max_rows: nil,
nil_values: [],
skip_rows: 0,
skip_rows_after_header: 0,
columns: nil,
infer_schema_length: @default_infer_schema_length,
parse_dates: false,
eol_delimiter: nil
)
backend = backend_from_options!(backend_opts)
backend.load_csv(
contents,
check_dtypes!(opts[:dtypes]),
opts[:delimiter],
opts[:nil_values],
opts[:skip_rows],
opts[:skip_rows_after_header],
opts[:header],
opts[:encoding],
opts[:max_rows],
to_columns_for_io(opts[:columns]),
opts[:infer_schema_length],
opts[:parse_dates],
opts[:eol_delimiter]
)
end
@doc """
Similar to `load_csv/2` but raises if there is a problem reading the CSV.
"""
@doc type: :io
@spec load_csv!(contents :: String.t(), opts :: Keyword.t()) :: DataFrame.t()
def load_csv!(contents, opts \\ []) do
case load_csv(contents, opts) do
{:ok, df} -> df
{:error, error} -> raise "load_csv failed: #{inspect(error)}"
end
end
@doc """
Reads a parquet file into a dataframe.
It accepts a filename that can be a local file, a "s3://" schema, or
a `FSS` entry like `FSS.S3.Entry`.
## Options
* `:max_rows` - Maximum number of lines to read. (default: `nil`)
* `:columns` - A list of column names or indexes to keep. If present,
only these columns are read into the dataframe. (default: `nil`)
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
"""
@doc type: :io
@spec from_parquet(filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def from_parquet(filename, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
max_rows: nil,
columns: nil,
config: nil
)
backend = backend_from_options!(backend_opts)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
backend.from_parquet(
entry,
opts[:max_rows],
to_columns_for_io(opts[:columns])
)
end
end
defp normalise_entry(%_{} = entry, config) when config != nil do
{:error,
ArgumentError.message(
":config key is only supported when the argument is a string, got #{inspect(entry)} with config #{inspect(config)}"
)}
end
defp normalise_entry(%Local.Entry{} = entry, nil), do: {:ok, entry}
defp normalise_entry(%S3.Entry{config: %S3.Config{}} = entry, nil), do: {:ok, entry}
defp normalise_entry("s3://" <> _rest = entry, config) do
S3.parse(entry, config: config)
end
defp normalise_entry("file://" <> path, _config), do: {:ok, Local.from_path(path)}
defp normalise_entry("http://" <> _rest = url, config) do
HTTP.parse(url, config: config)
end
defp normalise_entry("https://" <> _rest = url, config) do
HTTP.parse(url, config: config)
end
defp normalise_entry(filepath, _config) when is_binary(filepath) do
{:ok, Local.from_path(filepath)}
end
@doc """
Similar to `from_parquet/2` but raises if there is a problem reading the Parquet file.
"""
@doc type: :io
@spec from_parquet!(filename :: String.t() | fs_entry(), opts :: Keyword.t()) :: DataFrame.t()
def from_parquet!(filename, opts \\ []) do
case from_parquet(filename, opts) do
{:ok, df} ->
df
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "from_parquet failed: #{inspect(e.message)}"
{:error, error} ->
raise "from_parquet failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a parquet file.
Groups are ignored if the dataframe is using any.
## Options
* `:compression` - The compression algorithm to use when writing files.
Where a compression level is available, this can be passed as a tuple,
such as `{:zstd, 3}`. Supported options are:
* `nil` (uncompressed, default)
* `:snappy`
* `:gzip` (with levels 1-9)
* `:brotli` (with levels 1-11)
* `:zstd` (with levels -7-22)
* `:lz4raw`.
* `:streaming` - Tells the backend if it should use streaming, which means
that the dataframe is not loaded to the memory at once, and instead it is
written in chunks from a lazy dataframe.
This option has no effect on eager - the default - dataframes.
It defaults to `true`.
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
"""
@doc type: :io
@spec to_parquet(df :: DataFrame.t(), filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
:ok | {:error, Exception.t()}
def to_parquet(%DataFrame{} = df, filename, opts \\ []) do
opts = Keyword.validate!(opts, compression: nil, streaming: true, config: nil)
compression = parquet_compression(opts[:compression])
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
Shared.apply_impl(df, :to_parquet, [entry, compression, opts[:streaming]])
end
end
defp parquet_compression(nil), do: {nil, nil}
defp parquet_compression(algorithm) when algorithm in ~w(snappy gzip brotli zstd lz4raw)a do
{algorithm, nil}
end
for {algorithm, min, max} <- [{:gzip, 1, 9}, {:brotli, 1, 11}, {:zstd, -7, 22}] do
defp parquet_compression({unquote(algorithm), level}) do
if level in unquote(min)..unquote(max) or is_nil(level) do
{unquote(algorithm), level}
else
raise ArgumentError,
"#{unquote(algorithm)} compression level must be between #{unquote(min)} and #{unquote(max)} inclusive or nil, got #{level}"
end
end
end
defp parquet_compression(other) do
raise ArgumentError, "unsupported :compression #{inspect(other)} for Parquet"
end
@doc """
Similar to `to_parquet/3`, but raises in case of error.
"""
@doc type: :io
@spec to_parquet!(df :: DataFrame.t(), filename :: String.t() | fs_entry()) :: :ok
def to_parquet!(df, filename, opts \\ []) do
case to_parquet(df, filename, opts) do
:ok ->
:ok
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "to_parquet failed: #{inspect(e.message)}"
{:error, error} ->
raise "to_parquet failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a binary representation of a Parquet file.
Groups are ignored if the dataframe is using any.
## Options
* `:compression` - The compression algorithm to use when writing files.
Where a compression level is available, this can be passed as a tuple,
such as `{:zstd, 3}`. Supported options are:
* `nil` (uncompressed, default)
* `:snappy`
* `:gzip` (with levels 1-9)
* `:brotli` (with levels 1-11)
* `:zstd` (with levels -7-22)
* `:lz4raw`.
"""
@doc type: :io
@spec dump_parquet(df :: DataFrame.t(), opts :: Keyword.t()) ::
{:ok, binary()} | {:error, Exception.t()}
def dump_parquet(df, opts \\ []) do
opts = Keyword.validate!(opts, compression: nil)
compression = parquet_compression(opts[:compression])
Shared.apply_impl(df, :dump_parquet, [compression])
end
@doc """
Similar to `dump_parquet/2`, but raises in case of error.
"""
@doc type: :io
@spec dump_parquet!(df :: DataFrame.t(), opts :: Keyword.t()) :: binary()
def dump_parquet!(df, opts \\ []) do
case dump_parquet(df, opts) do
{:ok, parquet} -> parquet
{:error, error} -> raise "dump_parquet failed: #{inspect(error)}"
end
end
@doc """
Reads a binary representation of a parquet file into a dataframe.
"""
@doc type: :io
@spec load_parquet(contents :: binary(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def load_parquet(contents, opts \\ []) do
backend = backend_from_options!(opts)
backend.load_parquet(contents)
end
@doc """
Similar to `load_parquet/2` but raises if there is a problem reading the Parquet file.
"""
@doc type: :io
@spec load_parquet!(contents :: binary(), opts :: Keyword.t()) :: DataFrame.t()
def load_parquet!(contents, opts \\ []) do
case load_parquet(contents, opts) do
{:ok, df} -> df
{:error, error} -> raise "load_parquet failed: #{inspect(error)}"
end
end
@doc """
Reads an IPC file into a dataframe.
It accepts a filename that can be a local file, a "s3://" schema, or
a `FSS` entry like `FSS.S3.Entry`.
## Options
* `:columns` - List with the name or index of columns to be selected.
Defaults to all columns.
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
"""
@doc type: :io
@spec from_ipc(filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def from_ipc(filename, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
columns: nil,
config: nil
)
backend = backend_from_options!(backend_opts)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
backend.from_ipc(
entry,
to_columns_for_io(opts[:columns])
)
end
end
@doc """
Similar to `from_ipc/2` but raises if there is a problem reading the IPC file.
"""
@doc type: :io
@spec from_ipc!(filename :: String.t() | fs_entry(), opts :: Keyword.t()) :: DataFrame.t()
def from_ipc!(filename, opts \\ []) do
case from_ipc(filename, opts) do
{:ok, df} ->
df
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "from_ipc failed: #{inspect(e.message)}"
{:error, error} ->
raise "from_ipc failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to an IPC file.
Apache IPC is a language-agnostic columnar data structure that can be used to store dataframes.
It excels as a format for quickly exchange data between different programming languages.
Groups are ignored if the dataframe is using any.
## Options
* `:compression` - The compression algorithm to use when writing files.
Supported options are:
* `nil` (uncompressed, default)
* `:zstd`
* `:lz4`.
* `:streaming` - Tells the backend if it should use streaming, which means
that the dataframe is not loaded to the memory at once, and instead it is
written in chunks from a lazy dataframe.
This option has no effect on eager - the default - dataframes.
It defaults to `true`.
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
"""
@doc type: :io
@spec to_ipc(df :: DataFrame.t(), filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
:ok | {:error, Exception.t()}
def to_ipc(df, filename, opts \\ []) do
opts = Keyword.validate!(opts, compression: nil, streaming: true, config: nil)
compression = ipc_compression(opts[:compression])
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
Shared.apply_impl(df, :to_ipc, [entry, compression, opts[:streaming]])
end
end
defp ipc_compression(nil), do: {nil, nil}
defp ipc_compression(algorithm) when algorithm in ~w(zstd lz4)a, do: {algorithm, nil}
defp ipc_compression(other),
do: raise(ArgumentError, "unsupported :compression #{inspect(other)} for IPC")
@doc """
Similar to `to_ipc/3`, but raises in case of error.
"""
@doc type: :io
@spec to_ipc!(df :: DataFrame.t(), filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
:ok
def to_ipc!(df, filename, opts \\ []) do
case to_ipc(df, filename, opts) do
:ok ->
:ok
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "to_ipc failed: #{inspect(e.message)}"
{:error, error} ->
raise "to_ipc failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a binary representation of an IPC file.
Groups are ignored if the dataframe is using any.
## Options
* `:compression` - The compression algorithm to use when writing files.
Supported options are:
* `nil` (uncompressed, default)
* `:zstd`
* `:lz4`.
"""
@doc type: :io
@spec dump_ipc(df :: DataFrame.t(), opts :: Keyword.t()) ::
{:ok, binary()} | {:error, Exception.t()}
def dump_ipc(df, opts \\ []) do
opts = Keyword.validate!(opts, compression: nil)
compression = ipc_compression(opts[:compression])
Shared.apply_impl(df, :dump_ipc, [compression])
end
@doc """
Similar to `dump_ipc/2`, but raises in case of error.
"""
@doc type: :io
@spec dump_ipc!(df :: DataFrame.t(), opts :: Keyword.t()) :: binary()
def dump_ipc!(df, opts \\ []) do
case dump_ipc(df, opts) do
{:ok, ipc} -> ipc
{:error, error} -> raise "dump_ipc failed: #{inspect(error)}"
end
end
@doc """
Reads a binary representing an IPC file into a dataframe.
## Options
* `:columns` - List with the name or index of columns to be selected. Defaults to all columns.
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
"""
@doc type: :io
@spec load_ipc(contents :: binary(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def load_ipc(contents, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
columns: nil
)
backend = backend_from_options!(backend_opts)
backend.load_ipc(
contents,
to_columns_for_io(opts[:columns])
)
end
@doc """
Similar to `load_ipc/2` but raises if there is a problem reading the IPC file.
"""
@doc type: :io
@spec load_ipc!(contents :: binary(), opts :: Keyword.t()) :: DataFrame.t()
def load_ipc!(contents, opts \\ []) do
case load_ipc(contents, opts) do
{:ok, df} -> df
{:error, error} -> raise "load_ipc failed: #{inspect(error)}"
end
end
@doc """
Reads an IPC Streaming file into a dataframe.
It's possible to read from an IPC Streaming file using the lazy Polars
backend, but the implementation is not truly lazy. We are going to read it
first using the eager backend, and then convert the dataframe to lazy.
## Options
* `:columns` - List with the name or index of columns to be selected.
Defaults to all columns.
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
"""
@doc type: :io
@spec from_ipc_stream(filename :: String.t() | fs_entry()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def from_ipc_stream(filename, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts = Keyword.validate!(opts, columns: nil, config: nil)
backend = backend_from_options!(backend_opts)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
backend.from_ipc_stream(
entry,
to_columns_for_io(opts[:columns])
)
end
end
@doc """
Similar to `from_ipc_stream/2` but raises if there is a problem reading the IPC Stream file.
"""
@doc type: :io
@spec from_ipc_stream!(filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
DataFrame.t()
def from_ipc_stream!(filename, opts \\ []) do
case from_ipc_stream(filename, opts) do
{:ok, df} ->
df
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "from_ipc_stream failed: #{inspect(e.message)}"
{:error, error} ->
raise "from_ipc_stream failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to an IPC Stream file.
Arrow IPC Streams provide a streaming protocol or “format" for sending an arbitrary
length sequence of record batches.
The format must be processed from start to end, and does not support random access.
## Options
* `:compression` - The compression algorithm to use when writing files.
Supported options are:
* `nil` (uncompressed, default)
* `:zstd`
* `:lz4`.
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
"""
@doc type: :io
@spec to_ipc_stream(df :: DataFrame.t(), filename :: String.t() | fs_entry()) ::
:ok | {:error, Exception.t()}
def to_ipc_stream(df, filename, opts \\ []) do
opts = Keyword.validate!(opts, compression: nil, config: nil)
compression = ipc_compression(opts[:compression])
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
Shared.apply_impl(df, :to_ipc_stream, [entry, compression])
end
end
@doc """
Similar to `to_ipc_stream/3`, but raises in case of error.
"""
@doc type: :io
@spec to_ipc_stream!(
df :: DataFrame.t(),
filename :: String.t() | fs_entry(),
opts :: Keyword.t()
) :: :ok
def to_ipc_stream!(df, filename, opts \\ []) do
case to_ipc_stream(df, filename, opts) do
:ok ->
:ok
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "to_ipc_stream failed: #{inspect(e.message)}"
{:error, error} ->
raise "to_ipc_stream failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a binary representation of an IPC Stream file.
Groups are ignored if the dataframe is using any.
## Options
* `:compression` - The compression algorithm to use when writing files.
Supported options are:
* `nil` (uncompressed, default)
* `:zstd`
* `:lz4`.
"""
@doc type: :io
@spec dump_ipc_stream(df :: DataFrame.t(), opts :: Keyword.t()) ::
{:ok, binary()} | {:error, Exception.t()}
def dump_ipc_stream(df, opts \\ []) do
opts = Keyword.validate!(opts, compression: nil)
compression = ipc_compression(opts[:compression])
Shared.apply_impl(df, :dump_ipc_stream, [compression])
end
@doc """
Similar to `dump_ipc_stream/2`, but raises in case of error.
"""
@doc type: :io
@spec dump_ipc_stream!(df :: DataFrame.t(), opts :: Keyword.t()) :: binary()
def dump_ipc_stream!(df, opts \\ []) do
case dump_ipc_stream(df, opts) do
{:ok, ipc} -> ipc
{:error, error} -> raise "dump_ipc_stream failed: #{inspect(error)}"
end
end
@doc """
Reads a binary representing an IPC Stream file into a dataframe.
## Options
* `:columns` - List with the name or index of columns to be selected. Defaults to all columns.
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
"""
@doc type: :io
@spec load_ipc_stream(contents :: binary(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def load_ipc_stream(contents, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
columns: nil
)
backend = backend_from_options!(backend_opts)
backend.load_ipc_stream(
contents,
to_columns_for_io(opts[:columns])
)
end
@doc """
Similar to `load_ipc_stream/2` but raises if there is a problem.
"""
@doc type: :io
@spec load_ipc_stream!(contents :: binary(), opts :: Keyword.t()) :: DataFrame.t()
def load_ipc_stream!(contents, opts \\ []) do
case load_ipc_stream(contents, opts) do
{:ok, df} -> df
{:error, error} -> raise "load_ipc_stream failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a delimited file.
Groups are ignored if the dataframe is using any.
## Options
* `:header` - Should the column names be written as the first line of the file? (default: `true`)
* `:delimiter` - A single character used to separate fields within a record. (default: `","`)
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
* `:streaming` - Tells the backend if it should use streaming, which means
that the dataframe is not loaded to the memory at once, and instead it is
written in chunks from a lazy dataframe. Defaults to true for local filesystems,
ignored on all others.
"""
@doc type: :io
@spec to_csv(df :: DataFrame.t(), filename :: fs_entry() | String.t(), opts :: Keyword.t()) ::
:ok | {:error, Exception.t()}
def to_csv(df, filename, opts \\ []) do
opts = Keyword.validate!(opts, header: true, delimiter: ",", streaming: true, config: nil)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
Shared.apply_impl(df, :to_csv, [entry, opts[:header], opts[:delimiter], opts[:streaming]])
end
end
@doc """
Similar to `to_csv/3` but raises if there is a problem reading the CSV.
"""
@doc type: :io
@spec to_csv!(df :: DataFrame.t(), filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
:ok
def to_csv!(df, filename, opts \\ []) do
case to_csv(df, filename, opts) do
:ok ->
:ok
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "to_csv failed: #{inspect(e.message)}"
{:error, error} ->
raise "to_csv failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a binary representation of a delimited file.
## Options
* `:header` - Should the column names be written as the first line of the file? (default: `true`)
* `:delimiter` - A single character used to separate fields within a record. (default: `","`)
## Examples
iex> df = Explorer.Datasets.fossil_fuels() |> Explorer.DataFrame.head(2)
iex> Explorer.DataFrame.dump_csv(df)
{:ok, "year,country,total,solid_fuel,liquid_fuel,gas_fuel,cement,gas_flaring,per_capita,bunker_fuels\\n2010,AFGHANISTAN,2308,627,1601,74,5,0,0.08,9\\n2010,ALBANIA,1254,117,953,7,177,0,0.43,7\\n"}
"""
@doc type: :io
@spec dump_csv(df :: DataFrame.t(), opts :: Keyword.t()) ::
{:ok, String.t()} | {:error, Exception.t()}
def dump_csv(df, opts \\ []) do
opts = Keyword.validate!(opts, header: true, delimiter: ",")
Shared.apply_impl(df, :dump_csv, [opts[:header], opts[:delimiter]])
end
@doc """
Similar to `dump_csv/2`, but raises in case of error.
"""
@doc type: :io
@spec dump_csv!(df :: DataFrame.t(), opts :: Keyword.t()) :: String.t()
def dump_csv!(df, opts \\ []) do
case dump_csv(df, opts) do
{:ok, csv} -> csv
{:error, error} -> raise "dump_csv failed: #{inspect(error)}"
end
end
@doc """
Read a file of JSON objects or lists separated by new lines
## Options
* `:batch_size` - Sets the batch size for reading rows.
This value may have significant impact in performance,
so adjust it for your needs (default: `1000`).
* `:infer_schema_length` - Maximum number of rows read for schema inference.
Setting this to nil will do a full table scan and will be slow (default: `1000`).
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
"""
@doc type: :io
@spec from_ndjson(filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def from_ndjson(filename, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
config: nil,
batch_size: 1000,
infer_schema_length: @default_infer_schema_length
)
backend = backend_from_options!(backend_opts)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
backend.from_ndjson(
entry,
opts[:infer_schema_length],
opts[:batch_size]
)
end
end
@doc """
Similar to `from_ndjson/2`, but raises in case of error.
"""
@doc type: :io
@spec from_ndjson!(filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
DataFrame.t()
def from_ndjson!(filename, opts \\ []) do
case from_ndjson(filename, opts) do
{:ok, df} ->
df
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "from_ndjson failed: #{inspect(e.message)}"
{:error, error} ->
raise "from_ndjson failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a ndjson file.
Groups are ignored if the dataframe is using any.
NDJSON are files that contains JSON files separated by new lines.
They are often used as structured logs.
## Options
* `:config` - An optional struct, keyword list or map, normally associated with remote
file systems. See [IO section](#module-io-operations) for more details. (default: `nil`)
"""
@doc type: :io
@spec to_ndjson(df :: DataFrame.t(), filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
:ok | {:error, Exception.t()}
def to_ndjson(df, filename, opts \\ []) do
opts = Keyword.validate!(opts, config: nil)
with {:ok, entry} <- normalise_entry(filename, opts[:config]) do
Shared.apply_impl(df, :to_ndjson, [entry])
end
end
@doc """
Similar to `to_ndjson/3`, but raises in case of error.
"""
@doc type: :io
@spec to_ndjson!(df :: DataFrame.t(), filename :: String.t() | fs_entry(), opts :: Keyword.t()) ::
:ok
def to_ndjson!(df, filename, opts \\ []) do
case to_ndjson(df, filename, opts) do
:ok ->
:ok
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "to_ndjson failed: #{inspect(e.message)}"
{:error, error} ->
raise "to_ndjson failed: #{inspect(error)}"
end
end
@doc """
Writes a dataframe to a binary representation of a NDJSON file.
Groups are ignored if the dataframe is using any.
## Examples
iex> df = Explorer.DataFrame.new(col_a: [1, 2], col_b: [5.1, 5.2])
iex> Explorer.DataFrame.dump_ndjson(df)
{:ok, ~s({"col_a":1,"col_b":5.1}\\n{"col_a":2,"col_b":5.2}\\n)}
"""
@doc type: :io
@spec dump_ndjson(df :: DataFrame.t()) :: {:ok, binary()} | {:error, Exception.t()}
def dump_ndjson(df) do
Shared.apply_impl(df, :dump_ndjson, [])
end
@doc """
Similar to `dump_ndjson!/2`, but raises in case of error.
"""
@doc type: :io
@spec dump_ndjson!(df :: DataFrame.t()) :: binary()
def dump_ndjson!(df) do
case dump_ndjson(df) do
{:ok, ndjson} -> ndjson
{:error, error} -> raise "dump_ndjson failed: #{inspect(error)}"
end
end
@doc """
Reads a representation of a NDJSON file into a dataframe.
## Options
* `:batch_size` - Sets the batch size for reading rows.
This value may have significant impact in performance,
so adjust it for your needs (default: `1000`).
* `:infer_schema_length` - Maximum number of rows read for schema inference.
Setting this to nil will do a full table scan and will be slow (default: `1000`).
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:lazy` - force the results into the lazy version of the current backend.
"""
@doc type: :io
@spec load_ndjson(contents :: String.t(), opts :: Keyword.t()) ::
{:ok, DataFrame.t()} | {:error, Exception.t()}
def load_ndjson(contents, opts \\ []) do
{backend_opts, opts} = Keyword.split(opts, [:backend, :lazy])
opts =
Keyword.validate!(opts,
batch_size: 1000,
infer_schema_length: @default_infer_schema_length
)
backend = backend_from_options!(backend_opts)
backend.load_ndjson(
contents,
opts[:infer_schema_length],
opts[:batch_size]
)
end
@doc """
Similar to `load_ndjson/2`, but raises in case of error.
## Examples
iex> contents = ~s({"col_a":1,"col_b":5.1}\\n{"col_a":2,"col_b":5.2}\\n)
iex> Explorer.DataFrame.load_ndjson!(contents)
#Explorer.DataFrame<
Polars[2 x 2]
col_a s64 [1, 2]
col_b f64 [5.1, 5.2]
>
"""
@doc type: :io
@spec load_ndjson!(contents :: String.t(), opts :: Keyword.t()) ::
DataFrame.t()
def load_ndjson!(contents, opts \\ []) do
case load_ndjson(contents, opts) do
{:ok, df} ->
df
{:error, %module{} = e} when module in [ArgumentError, RuntimeError] ->
raise module, "load_ndjson failed: #{inspect(e.message)}"
{:error, error} ->
raise "load_ndjson failed: #{inspect(error)}"
end
end
## Conversion
@doc """
Returns true if the data frame is a lazy one.
"""
@doc type: :conversion
@spec lazy?(df :: DataFrame.t()) :: boolean
def lazy?(%DataFrame{data: %struct{}}), do: struct.lazy() == struct
@doc """
Converts the dataframe to the lazy version of the current backend.
Operations on a lazy dataframe are not executed immediately, instead
they are batched together, and performed when `collect/1` is invoked.
This allows for query optimizations, parallelism, leading to better
performance.
If already lazy, this is a noop. Converting a grouped dataframe should
return a lazy dataframe with groups.
"""
@doc type: :conversion
@spec lazy(df :: DataFrame.t()) :: DataFrame.t()
def lazy(df), do: Shared.apply_impl(df, :lazy)
@deprecated "Use lazy/1 instead"
@doc false
def to_lazy(df), do: Shared.apply_impl(df, :lazy)
@doc """
Collects the lazy data frame into an eager one, computing the query.
It is the opposite of `lazy/1`. If already eager, this is a noop.
Collecting a grouped dataframe should return a grouped dataframe.
"""
@doc type: :conversion
@spec collect(df :: DataFrame.t()) :: DataFrame.t()
def collect(df), do: Shared.apply_impl(df, :collect)
@doc """
Creates a new dataframe.
It accepts any of:
* a map or keyword list of string/atom keys and series as values
* a map or keyword list of string/atom keys and tensors as values
* any data structure adhering to the `Table.Reader` protocol
## Options
* `:backend` - The Explorer backend to use. Defaults to the value returned by `Explorer.Backend.get/0`.
* `:dtypes` - A list/map of `{column_name, dtype}` pairs. (default: `[]`)
* `:lazy` - force the results into the lazy version of the current backend.
## Examples
### From series
Series can be given either as keyword lists or maps
where the keys are the name and the values are series:
iex> Explorer.DataFrame.new(%{
...> floats: Explorer.Series.from_list([1.0, 2.0]),
...> ints: Explorer.Series.from_list([1, nil])
...> })
#Explorer.DataFrame<
Polars[2 x 2]
floats f64 [1.0, 2.0]
ints s64 [1, nil]
>
### From tensors
To create dataframe from tensors, you can pass a matrix as argument.
Each matrix column becomes a dataframe column with names x1, x2, x3,
etc:
iex> Explorer.DataFrame.new(Nx.tensor([
...> [1, 2, 3],
...> [4, 5, 6]
...> ]))
#Explorer.DataFrame<
Polars[2 x 3]
x1 s64 [1, 4]
x2 s64 [2, 5]
x3 s64 [3, 6]
>
Explorer expects tensors to have certain types, so you may need to cast
the data accordingly. See `Explorer.Series.from_tensor/2` for more info.
You can also pass a keyword list or maps of vectors (rank 1 tensors):
iex> Explorer.DataFrame.new(%{
...> floats: Nx.tensor([1.0, 2.0], type: :f64),
...> ints: Nx.tensor([3, 4])
...> })
#Explorer.DataFrame<
Polars[2 x 2]
floats f64 [1.0, 2.0]
ints s64 [3, 4]
>
Use dtypes to force a particular representation:
iex> Explorer.DataFrame.new([
...> floats: Nx.tensor([1.0, 2.0], type: :f64),
...> times: Nx.tensor([3_000, 4_000])
...> ], dtypes: [times: :time])
#Explorer.DataFrame<
Polars[2 x 2]
floats f64 [1.0, 2.0]
times time [00:00:00.000003, 00:00:00.000004]
>
### From tabular
Tabular data can be either columnar or row-based.
Let's start with column data:
iex> Explorer.DataFrame.new(%{floats: [1.0, 2.0], ints: [1, nil]})
#Explorer.DataFrame<
Polars[2 x 2]
floats f64 [1.0, 2.0]
ints s64 [1, nil]
>
iex> Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, nil])
#Explorer.DataFrame<
Polars[2 x 2]
floats f64 [1.0, 2.0]
ints s64 [1, nil]
>
iex> Explorer.DataFrame.new([floats: [1.0, 2.0], ints: [1, nil], binaries: [<<239, 191, 19>>, nil]], dtypes: [{:binaries, :binary}])
#Explorer.DataFrame<
Polars[2 x 3]
floats f64 [1.0, 2.0]
ints s64 [1, nil]
binaries binary [<<239, 191, 19>>, nil]
>
iex> Explorer.DataFrame.new(%{floats: [1.0, 2.0], ints: [1, "wrong"]})
** (ArgumentError) cannot create series "ints": the value "wrong" does not match the inferred dtype {:s, 64}
From row data:
iex> rows = [%{id: 1, name: "José"}, %{id: 2, name: "Christopher"}, %{id: 3, name: "Cristine"}]
iex> Explorer.DataFrame.new(rows)
#Explorer.DataFrame<
Polars[3 x 2]
id s64 [1, 2, 3]
name string ["José", "Christopher", "Cristine"]
>
iex> rows = [[id: 1, name: "José"], [id: 2, name: "Christopher"], [id: 3, name: "Cristine"]]
iex> Explorer.DataFrame.new(rows)
#Explorer.DataFrame<
Polars[3 x 2]
id s64 [1, 2, 3]
name string ["José", "Christopher", "Cristine"]
>
"""
@doc type: :conversion
@spec new(
Table.Reader.t() | series_pairs,
opts :: Keyword.t()
) :: DataFrame.t()
when series_pairs: %{column_name() => Series.t()} | [{column_name(), Series.t()}]
def new(data, opts \\ []) do
opts = Keyword.validate!(opts, lazy: false, backend: nil, dtypes: [])
backend = backend_from_options!(opts)
dtypes = check_dtypes!(opts[:dtypes])
case data do
%DataFrame{data: %^backend{}} ->
data
data ->
case classify_data(data) do
{:series, data} ->
pairs =
for {name, series} <- data do
if not is_struct(series, Series) do
raise ArgumentError,
"expected a list or map of only series, got: #{inspect(series)}"
end
{to_column_name(name), series}
end
backend.from_series(pairs)
{:tensor, data} ->
s_backend = df_backend_to_s_backend(backend)
pairs =
for {name, tensor} <- data do
if not is_struct(tensor, Nx.Tensor) do
raise ArgumentError,
"expected a list or map of only tensors, got: #{inspect(tensor)}"
end
name = to_column_name(name)
dtype = dtypes[name]
opts = [backend: s_backend]
opts = if dtype, do: [dtype: dtype] ++ opts, else: opts
{name, Series.from_tensor(tensor, opts)}
end
backend.from_series(pairs)
{:tabular, data} ->
backend.from_tabular(data, dtypes)
end
end
end
defp classify_data([{_, %struct{}} | _] = data) when struct == Series, do: {:series, data}
defp classify_data([{_, %struct{}} | _] = data) when struct == Nx.Tensor, do: {:tensor, data}
defp classify_data(%struct{} = data) do
if struct == Nx.Tensor do
case data.shape do
{_, cols} ->
{:tensor, Enum.map(1..cols, fn i -> {"x#{i}", data[[.., i - 1]]} end)}
_ ->
raise ArgumentError,
"Explorer.DataFrame.new/2 only accepts tensor of rank 2, got: #{inspect(data)}"
end
else
{:tabular, data}
end
end
defp classify_data(data) when is_map(data) do
case :maps.next(:maps.iterator(data)) do
{_key, %struct{}, _} when struct == Series -> {:series, data}
{_key, %struct{}, _} when struct == Nx.Tensor -> {:tensor, data}
_ -> {:tabular, data}
end
end
defp classify_data(data), do: {:tabular, data}
@doc """
Converts a dataframe to a list of columns with lists as values.
See `to_series/2` if you want a list of columns with series as values.
Note that this function does not take into account groups.
> #### Warning {: .warning}
>
> This is an expensive operation since it converts series to lists and doing
> so will copy the whole dataframe. Prefer to use the operations in this and
> the `Explorer.Series` module rather than the ones in `Enum` whenever possible,
> as Explorer is optimized for large series.
## Options
* `:atom_keys` - Configure if the resultant map should have atom keys. (default: `false`)
## Examples
iex> df = Explorer.DataFrame.new(ints: [1, nil], floats: [1.0, 2.0])
iex> Explorer.DataFrame.to_columns(df)
%{"floats" => [1.0, 2.0], "ints" => [1, nil]}
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, nil])
iex> Explorer.DataFrame.to_columns(df, atom_keys: true)
%{floats: [1.0, 2.0], ints: [1, nil]}
"""
@doc type: :conversion
@spec to_columns(df :: DataFrame.t(), Keyword.t()) :: map()
def to_columns(df, opts \\ []) do
opts = Keyword.validate!(opts, atom_keys: false)
atom_keys = opts[:atom_keys]
for name <- df.names, into: %{} do
series = Shared.apply_impl(df, :pull, [name])
key = if atom_keys, do: String.to_atom(name), else: name
{key, Series.to_list(series)}
end
end
@doc """
Converts a dataframe to a list of columns with series as values.
See `to_columns/2` if you want a list of columns with lists as values.
Note that this function does not take into account groups.
## Options
* `:atom_keys` - Configure if the resultant map should have atom keys. (default: `false`)
## Examples
iex> df = Explorer.DataFrame.new(ints: [1, nil], floats: [1.0, 2.0])
iex> map = Explorer.DataFrame.to_series(df)
iex> Explorer.Series.to_list(map["floats"])
[1.0, 2.0]
iex> Explorer.Series.to_list(map["ints"])
[1, nil]
"""
@doc type: :conversion
@spec to_series(df :: DataFrame.t(), Keyword.t()) :: map()
def to_series(df, opts \\ []) do
opts = Keyword.validate!(opts, atom_keys: false)
atom_keys = opts[:atom_keys]
for name <- df.names, into: %{} do
key = if atom_keys, do: String.to_atom(name), else: name
{key, Shared.apply_impl(df, :pull, [name])}
end
end
@doc """
Converts a dataframe to a list of maps (rows).
> #### Warning {: .warning}
>
> This is an expensive operation since data is stored in a columnar format.
> You must avoid converting a dataframe to rows, as that will transform and
> copy the whole dataframe in memory. Prefer to use the operations in this
> module rather than the ones in `Enum` whenever possible, as this module is
> optimized for large series.
## Options
* `:atom_keys` - Configure if the resultant maps should have atom keys. (default: `false`)
## Examples
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, nil])
iex> Explorer.DataFrame.to_rows(df)
[%{"floats" => 1.0, "ints" => 1}, %{"floats" => 2.0 ,"ints" => nil}]
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, nil])
iex> Explorer.DataFrame.to_rows(df, atom_keys: true)
[%{floats: 1.0, ints: 1}, %{floats: 2.0, ints: nil}]
"""
@doc type: :conversion
@spec to_rows(df :: DataFrame.t(), Keyword.t()) :: [map()]
def to_rows(df, opts \\ []) do
opts = Keyword.validate!(opts, atom_keys: false)
Shared.apply_impl(df, :to_rows, [opts[:atom_keys]])
end
@doc """
Converts a dataframe to a stream of maps (rows).
> #### Warning {: .warning}
>
> This is an expensive operation since data is stored in a columnar format.
> Prefer to use the operations in this module rather than the ones in `Enum`
> whenever possible, as this module is optimized for large series.
## Options
* `:atom_keys` - Configure if the resultant maps should have atom keys. (default: `false`)
* `:chunk_size` - Number of rows passed to `to_rows/2` while streaming over the data. (default: `1000`)
## Examples
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, nil])
iex> Explorer.DataFrame.to_rows_stream(df) |> Enum.map(& &1)
[%{"floats" => 1.0, "ints" => 1}, %{"floats" => 2.0 ,"ints" => nil}]
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, nil])
iex> Explorer.DataFrame.to_rows_stream(df, atom_keys: true) |> Enum.map(& &1)
[%{floats: 1.0, ints: 1}, %{floats: 2.0, ints: nil}]
"""
@doc type: :conversion
@spec to_rows_stream(df :: DataFrame.t(), Keyword.t()) :: Enumerable.t()
def to_rows_stream(df, opts \\ []) do
opts = Keyword.validate!(opts, atom_keys: false, chunk_size: 1_000)
Shared.apply_impl(df, :to_rows_stream, [opts[:atom_keys], opts[:chunk_size]])
end
# Introspection
@doc """
Gets the names of the dataframe columns.
## Examples
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, 2])
iex> Explorer.DataFrame.names(df)
["floats", "ints"]
"""
@doc type: :introspection
@spec names(df :: DataFrame.t()) :: [String.t()]
def names(df), do: df.names
@doc """
Gets the dtypes of the dataframe columns.
## Examples
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0], ints: [1, 2])
iex> Explorer.DataFrame.dtypes(df)
%{"floats" => {:f, 64}, "ints" => {:s, 64}}
"""
@doc type: :introspection
@spec dtypes(df :: DataFrame.t()) :: %{String.t() => atom()}
def dtypes(df), do: df.dtypes
@doc """
Gets the shape of the dataframe as a `{height, width}` tuple.
This function works the same way for grouped dataframes, considering the entire
dataframe in the counting of rows.
## Examples
iex> df = Explorer.DataFrame.new(floats: [1.0, 2.0, 3.0], ints: [1, 2, 3])
iex> Explorer.DataFrame.shape(df)
{3, 2}
"""
@doc type: :introspection
@spec shape(df :: DataFrame.t()) :: {integer(), integer()}
def shape(df), do: {n_rows(df), n_columns(df)}
@doc """
Returns the number of rows in the dataframe.
This function works the same way for grouped dataframes, considering the entire
dataframe in the counting of rows.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.n_rows(df)
1094
"""
@doc type: :introspection
@spec n_rows(df :: DataFrame.t()) :: integer()
def n_rows(df), do: Shared.apply_impl(df, :n_rows)
@doc """
Returns the number of columns in the dataframe.
This function works the same way for grouped dataframes.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.n_columns(df)
10
"""
@doc type: :introspection
@spec n_columns(df :: DataFrame.t()) :: integer()
def n_columns(df), do: map_size(df.dtypes)
@doc """
Returns the groups of a dataframe.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> df = Explorer.DataFrame.group_by(df, "country")
iex> Explorer.DataFrame.groups(df)
["country"]
iex> df = Explorer.Datasets.iris()
iex> Explorer.DataFrame.groups(df)
[]
"""
@doc type: :introspection
@spec groups(df :: DataFrame.t()) :: list(String.t())
def groups(%DataFrame{groups: groups}), do: groups
# Single table verbs
@doc """
Returns the first *n* rows of the dataframe.
By default it returns the first 5 rows.
If the dataframe is using groups, then the first *n* rows of each group is
returned.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.head(df)
#Explorer.DataFrame<
Polars[5 x 10]
year s64 [2010, 2010, 2010, 2010, 2010]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA"]
total s64 [2308, 1254, 32500, 141, 7924]
solid_fuel s64 [627, 117, 332, 0, 0]
liquid_fuel s64 [1601, 953, 12381, 141, 3649]
gas_fuel s64 [74, 7, 14565, 0, 374]
cement s64 [5, 177, 2598, 0, 204]
gas_flaring s64 [0, 0, 2623, 0, 3697]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37]
bunker_fuels s64 [9, 7, 663, 0, 321]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.head(df, 2)
#Explorer.DataFrame<
Polars[2 x 10]
year s64 [2010, 2010]
country string ["AFGHANISTAN", "ALBANIA"]
total s64 [2308, 1254]
solid_fuel s64 [627, 117]
liquid_fuel s64 [1601, 953]
gas_fuel s64 [74, 7]
cement s64 [5, 177]
gas_flaring s64 [0, 0]
per_capita f64 [0.08, 0.43]
bunker_fuels s64 [9, 7]
>
## Grouped examples
Using grouped dataframes makes `head/2` return *n* rows from each group.
Here is an example using the Iris dataset, and returning two rows from each group:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.head(grouped, 2)
#Explorer.DataFrame<
Polars[6 x 5]
Groups: ["species"]
sepal_length f64 [5.1, 4.9, 7.0, 6.4, 6.3, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.2, 3.3, ...]
petal_length f64 [1.4, 1.4, 4.7, 4.5, 6.0, ...]
petal_width f64 [0.2, 0.2, 1.4, 1.5, 2.5, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", "Iris-virginica", ...]
>
"""
@doc type: :rows
@spec head(df :: DataFrame.t(), nrows :: integer()) :: DataFrame.t()
def head(df, nrows \\ @default_sample_nrows), do: Shared.apply_impl(df, :head, [nrows])
@doc """
Returns the last *n* rows of the dataframe.
By default it returns the last 5 rows.
If the dataframe is using groups, then the last *n* rows of each group is
returned.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.tail(df)
#Explorer.DataFrame<
Polars[5 x 10]
year s64 [2014, 2014, 2014, 2014, 2014]
country string ["VIET NAM", "WALLIS AND FUTUNA ISLANDS", "YEMEN", "ZAMBIA", "ZIMBABWE"]
total s64 [45517, 6, 6190, 1228, 3278]
solid_fuel s64 [19246, 0, 137, 132, 2097]
liquid_fuel s64 [12694, 6, 5090, 797, 1005]
gas_fuel s64 [5349, 0, 581, 0, 0]
cement s64 [8229, 0, 381, 299, 177]
gas_flaring s64 [0, 0, 0, 0, 0]
per_capita f64 [0.49, 0.44, 0.24, 0.08, 0.22]
bunker_fuels s64 [761, 1, 153, 33, 9]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.tail(df, 2)
#Explorer.DataFrame<
Polars[2 x 10]
year s64 [2014, 2014]
country string ["ZAMBIA", "ZIMBABWE"]
total s64 [1228, 3278]
solid_fuel s64 [132, 2097]
liquid_fuel s64 [797, 1005]
gas_fuel s64 [0, 0]
cement s64 [299, 177]
gas_flaring s64 [0, 0]
per_capita f64 [0.08, 0.22]
bunker_fuels s64 [33, 9]
>
## Grouped examples
Using grouped dataframes makes `tail/2` return **n rows** from each group.
Here is an example using the Iris dataset, and returning two rows from each group:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.tail(grouped, 2)
#Explorer.DataFrame<
Polars[6 x 5]
Groups: ["species"]
sepal_length f64 [5.3, 5.0, 5.1, 5.7, 6.2, ...]
sepal_width f64 [3.7, 3.3, 2.5, 2.8, 3.4, ...]
petal_length f64 [1.5, 1.4, 3.0, 4.1, 5.4, ...]
petal_width f64 [0.2, 0.2, 1.1, 1.3, 2.3, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", "Iris-virginica", ...]
>
"""
@doc type: :rows
@spec tail(df :: DataFrame.t(), nrows :: integer()) :: DataFrame.t()
def tail(df, nrows \\ @default_sample_nrows), do: Shared.apply_impl(df, :tail, [nrows])
@doc """
Selects a subset of columns by name.
It's important to notice that groups are kept:
you can't select off grouping columns.
## Examples
You can select a single column:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.select(df, "a")
#Explorer.DataFrame<
Polars[3 x 1]
a string ["a", "b", "c"]
>
Or a list of names:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.select(df, ["a"])
#Explorer.DataFrame<
Polars[3 x 1]
a string ["a", "b", "c"]
>
You can also use a range or a list of integers:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3], c: [4, 5, 6])
iex> Explorer.DataFrame.select(df, [0, 1])
#Explorer.DataFrame<
Polars[3 x 2]
a string ["a", "b", "c"]
b s64 [1, 2, 3]
>
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3], c: [4, 5, 6])
iex> Explorer.DataFrame.select(df, 0..1)
#Explorer.DataFrame<
Polars[3 x 2]
a string ["a", "b", "c"]
b s64 [1, 2, 3]
>
Or you can use a callback function that takes the dataframe's names as its first argument:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.select(df, &String.starts_with?(&1, "b"))
#Explorer.DataFrame<
Polars[3 x 1]
b s64 [1, 2, 3]
>
Or, if you prefer, a regex:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.select(df, ~r/^b$/)
#Explorer.DataFrame<
Polars[3 x 1]
b s64 [1, 2, 3]
>
Or a callback function that takes names and types:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.select(df, fn _name, type -> type == {:s, 64} end)
#Explorer.DataFrame<
Polars[3 x 1]
b s64 [1, 2, 3]
>
## Grouped examples
Columns that are also groups cannot be removed,
you need to ungroup before removing these columns.
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.select(grouped, ["sepal_width"])
#Explorer.DataFrame<
Polars[150 x 2]
Groups: ["species"]
sepal_width f64 [3.5, 3.0, 3.2, 3.1, 3.6, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
@spec select(df :: DataFrame.t(), column | columns) :: DataFrame.t()
def select(df, columns_or_column)
def select(df, column) when is_column(column) do
select(df, [column])
end
def select(df, columns) do
columns = to_existing_columns(df, columns)
columns_to_keep = Enum.uniq(columns ++ df.groups)
out_df = %{df | names: columns_to_keep, dtypes: Map.take(df.dtypes, columns_to_keep)}
Shared.apply_impl(df, :select, [out_df])
end
@doc """
Discards a subset of columns by name.
It's important to notice that groups are kept:
you can't discard grouping columns.
## Examples
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.discard(df, ["b"])
#Explorer.DataFrame<
Polars[3 x 1]
a string ["a", "b", "c"]
>
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3], c: [4, 5, 6])
iex> Explorer.DataFrame.discard(df, ["a", "b"])
#Explorer.DataFrame<
Polars[3 x 1]
c s64 [4, 5, 6]
>
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
## Grouped examples
You cannot discard grouped columns. You need to ungroup before removing them:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.discard(grouped, ["species"])
#Explorer.DataFrame<
Polars[150 x 5]
Groups: ["species"]
sepal_length f64 [5.1, 4.9, 4.7, 4.6, 5.0, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.1, 3.6, ...]
petal_length f64 [1.4, 1.4, 1.3, 1.5, 1.4, ...]
petal_width f64 [0.2, 0.2, 0.2, 0.2, 0.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
@spec discard(df :: DataFrame.t(), column | columns) :: DataFrame.t()
def discard(df, columns_or_column)
def discard(df, column) when is_column(column) do
discard(df, [column])
end
def discard(df, columns) do
columns = to_existing_columns(df, columns, false) -- df.groups
columns_to_keep = df.names -- columns
out_df = %{df | names: columns_to_keep, dtypes: Map.take(df.dtypes, columns_to_keep)}
Shared.apply_impl(df, :select, [out_df])
end
@doc """
Picks rows based on a list or series of values.
## Examples
This function must only be used when you need to select rows based
on external values that are not available to the dataframe. For example,
you can pass a list:
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.mask(df, [false, true, false])
#Explorer.DataFrame<
Polars[1 x 2]
col1 string ["b"]
col2 s64 [2]
>
You must avoid using masks when the masks themselves are computed from
other columns. For example, DO NOT do this:
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.mask(df, Explorer.Series.greater(df["col2"], 1))
#Explorer.DataFrame<
Polars[2 x 2]
col1 string ["b", "c"]
col2 s64 [2, 3]
>
Instead, do this:
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter_with(df, fn df -> Explorer.Series.greater(df["col2"], 1) end)
#Explorer.DataFrame<
Polars[2 x 2]
col1 string ["b", "c"]
col2 s64 [2, 3]
>
The `filter_with/2` version is much more efficient because it doesn't need
to create intermediate series representations to apply the mask.
"""
@doc type: :single
@spec mask(df :: DataFrame.t(), mask :: Series.t() | [boolean()]) :: DataFrame.t()
def mask(df, %Series{} = mask) do
s_len = Series.size(mask)
df_len = n_rows(df)
case s_len == df_len do
false ->
raise(
ArgumentError,
"size of the mask (#{s_len}) must match number of rows in the dataframe (#{df_len})"
)
true ->
Shared.apply_impl(df, :mask, [mask])
end
end
def mask(df, mask) when is_list(mask), do: mask |> Series.from_list() |> then(&mask(df, &1))
@doc """
Picks rows based on `Explorer.Query`.
The query is compiled and runs efficiently against the dataframe.
The query must return a boolean expression or a list of boolean expressions.
When a list is returned, they are joined as `and` expressions.
> #### Notice {: .notice}
>
> This is a macro. You must `require Explorer.DataFrame` before using it.
Besides element-wise series operations, you can also use window functions
and aggregations inside comparisons. In such cases, grouped dataframes
may have different results than ungrouped ones, because the filtering
is computed withing groups. See examples below.
See `filter_with/2` for a callback version of this function without
`Explorer.Query`.
## Examples
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter(df, col2 > 2)
#Explorer.DataFrame<
Polars[1 x 2]
col1 string ["c"]
col2 s64 [3]
>
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter(df, col1 == "b")
#Explorer.DataFrame<
Polars[1 x 2]
col1 string ["b"]
col2 s64 [2]
>
iex> df = Explorer.DataFrame.new(col1: [5, 4, 3], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter(df, [col1 > 3, col2 < 3])
#Explorer.DataFrame<
Polars[2 x 2]
col1 s64 [5, 4]
col2 s64 [1, 2]
>
Returning a non-boolean expression errors:
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter(df, cumulative_max(col2))
** (ArgumentError) expecting the function to return a boolean LazySeries, but instead it returned a LazySeries of type {:s, 64}
Which can be addressed by converting it to boolean:
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter(df, cumulative_max(col2) == 1)
#Explorer.DataFrame<
Polars[1 x 2]
col1 string ["a"]
col2 s64 [1]
>
## Grouped examples
In a grouped dataframe, the aggregation is calculated within each group.
In the following example we select the flowers of the Iris dataset that have the "petal length"
above the average of each species group.
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.filter(grouped, petal_length > mean(petal_length))
#Explorer.DataFrame<
Polars[79 x 5]
Groups: ["species"]
sepal_length f64 [4.6, 5.4, 5.0, 4.9, 5.4, ...]
sepal_width f64 [3.1, 3.9, 3.4, 3.1, 3.7, ...]
petal_length f64 [1.5, 1.7, 1.5, 1.5, 1.5, ...]
petal_width f64 [0.2, 0.4, 0.2, 0.1, 0.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
defmacro filter(df, query) do
quote do
require Explorer.Query
Explorer.DataFrame.filter_with(unquote(df), Explorer.Query.query(unquote(query)))
end
end
@doc """
Picks rows based on a callback function.
The callback receives a lazy dataframe. A lazy dataframe does
not hold any values, instead it stores all operations in order to
execute all filtering performantly.
This is a callback version of `filter/2`.
## Examples
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter_with(df, &Explorer.Series.greater(&1["col2"], 2))
#Explorer.DataFrame<
Polars[1 x 2]
col1 string ["c"]
col2 s64 [3]
>
iex> df = Explorer.DataFrame.new(col1: ["a", "b", "c"], col2: [1, 2, 3])
iex> Explorer.DataFrame.filter_with(df, fn df -> Explorer.Series.equal(df["col1"], "b") end)
#Explorer.DataFrame<
Polars[1 x 2]
col1 string ["b"]
col2 s64 [2]
>
## Grouped examples
In a grouped dataframe, the aggregation is calculated within each group.
In the following example we select the flowers of the Iris dataset that have the "petal length"
above the average of each species group.
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.filter_with(grouped, &Explorer.Series.greater(&1["petal_length"], Explorer.Series.mean(&1["petal_length"])))
#Explorer.DataFrame<
Polars[79 x 5]
Groups: ["species"]
sepal_length f64 [4.6, 5.4, 5.0, 4.9, 5.4, ...]
sepal_width f64 [3.1, 3.9, 3.4, 3.1, 3.7, ...]
petal_length f64 [1.5, 1.7, 1.5, 1.5, 1.5, ...]
petal_width f64 [0.2, 0.4, 0.2, 0.1, 0.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
@spec filter_with(
df :: DataFrame.t(),
callback :: (Explorer.Backend.LazyFrame.t() -> Series.lazy_t())
) :: DataFrame.t()
def filter_with(df, fun) when is_function(fun, 1) do
ldf = Explorer.Backend.LazyFrame.new(df)
case fun.(ldf) do
%Series{dtype: :boolean, data: %LazySeries{} = data} ->
Shared.apply_impl(df, :filter_with, [df, data])
%Series{dtype: dtype, data: %LazySeries{}} ->
raise ArgumentError,
"expecting the function to return a boolean LazySeries, " <>
"but instead it returned a LazySeries of type " <>
inspect(dtype)
[%Series{dtype: :boolean, data: %LazySeries{}} | _rest] = lazy_series ->
first_non_boolean =
Enum.find(lazy_series, &(!match?(%Series{dtype: :boolean, data: %LazySeries{}}, &1)))
if is_nil(first_non_boolean) do
series = Enum.reduce(lazy_series, &Explorer.Backend.LazySeries.binary_and(&2, &1))
Shared.apply_impl(df, :filter_with, [df, series.data])
else
filter_without_boolean_series_error(first_non_boolean)
end
other ->
filter_without_boolean_series_error(other)
end
end
defp filter_without_boolean_series_error(term) do
raise ArgumentError,
"expecting the function to return a single or a list of boolean LazySeries, " <>
"but instead it contains:\n#{inspect(term)}"
end
@doc """
Creates or modifies columns based on `Explorer.Query`.
The query is compiled and runs efficiently against the dataframe.
New variables overwrite existing variables of the same name.
Column names are coerced from atoms to strings.
> #### Notice {: .notice}
>
> This is a macro. You must `require Explorer.DataFrame` before using it.
Besides element-wise series operations, you can also use window functions
and aggregations inside mutations. In such cases, grouped dataframes
may have different results than ungrouped ones, because the mutation
is computed withing groups. See examples below.
See `mutate_with/2` for a callback version of this function without
`Explorer.Query`. If your mutation cannot be expressed with queries,
you may compute the values using the `Explorer.Series` API directly
and then add it to the dataframe using `put/3`.
## Examples
Mutations are useful to add or modify columns in your dataframe:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate(df, c: b + 1)
#Explorer.DataFrame<
Polars[3 x 3]
a string ["a", "b", "c"]
b s64 [1, 2, 3]
c s64 [2, 3, 4]
>
It's also possible to overwrite existing columns:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate(df, a: b * 2)
#Explorer.DataFrame<
Polars[3 x 2]
a s64 [2, 4, 6]
b s64 [1, 2, 3]
>
Scalar values are repeated to fill the series:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate(df, a: 4)
#Explorer.DataFrame<
Polars[3 x 2]
a s64 [4, 4, 4]
b s64 [1, 2, 3]
>
It's also possible to use functions from the Series module, like `Explorer.Series.window_sum/3`:
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> Explorer.DataFrame.mutate(df, b: window_sum(a, 2))
#Explorer.DataFrame<
Polars[3 x 2]
a s64 [1, 2, 3]
b s64 [1, 3, 5]
>
Alternatively, all of the above works with a map instead of a keyword list:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate(df, %{"c" => cast(b, {:f, 64})})
#Explorer.DataFrame<
Polars[3 x 3]
a string ["a", "b", "c"]
b s64 [1, 2, 3]
c f64 [1.0, 2.0, 3.0]
>
## Grouped examples
Mutations in grouped dataframes takes the context of the group.
This enables some aggregations to be made considering each group. It's almost like `summarise/2`,
but repeating the results for each member in the group.
For example, if we want to count how many elements of a given group, we can add a new
column with that aggregation:
iex> df = Explorer.DataFrame.new(id: ["a", "a", "b"], b: [1, 2, 3])
iex> grouped = Explorer.DataFrame.group_by(df, :id)
iex> Explorer.DataFrame.mutate(grouped, count: count(b))
#Explorer.DataFrame<
Polars[3 x 3]
Groups: ["id"]
id string ["a", "a", "b"]
b s64 [1, 2, 3]
count u32 [2, 2, 1]
>
In case we want to get the average size of the petal length from the Iris dataset, we can:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.mutate(grouped, petal_length_avg: mean(petal_length))
#Explorer.DataFrame<
Polars[150 x 6]
Groups: ["species"]
sepal_length f64 [5.1, 4.9, 4.7, 4.6, 5.0, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.1, 3.6, ...]
petal_length f64 [1.4, 1.4, 1.3, 1.5, 1.4, ...]
petal_width f64 [0.2, 0.2, 0.2, 0.2, 0.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
petal_length_avg f64 [1.464, 1.464, 1.464, 1.464, 1.464, ...]
>
"""
@doc type: :single
defmacro mutate(df, mutations) do
quote do
require Explorer.Query
Explorer.DataFrame.mutate_with(unquote(df), Explorer.Query.query(unquote(mutations)))
end
end
@doc """
Creates or modifies columns using a callback function.
The callback receives a lazy dataframe. A lazy dataframe doesn't
hold any values, instead it stores all operations in order to
execute all mutations performantly.
This is a callback version of `mutate/2`. If your mutation
cannot be expressed with lazy dataframes, you may compute the
values using the `Explorer.Series` API directly and then add
it to the dataframe using `put/3`.
## Examples
Here is an example of a new column that sums the value of two other columns:
iex> df = Explorer.DataFrame.new(a: [4, 5, 6], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate_with(df, &[c: Explorer.Series.add(&1["a"], &1["b"])])
#Explorer.DataFrame<
Polars[3 x 3]
a s64 [4, 5, 6]
b s64 [1, 2, 3]
c s64 [5, 7, 9]
>
You can overwrite existing columns as well:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "c"], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate_with(df, &[b: Explorer.Series.pow(&1["b"], 2)])
#Explorer.DataFrame<
Polars[3 x 2]
a string ["a", "b", "c"]
b f64 [1.0, 4.0, 9.0]
>
It's possible to "reuse" a variable for different computations:
iex> df = Explorer.DataFrame.new(a: [4, 5, 6], b: [1, 2, 3])
iex> Explorer.DataFrame.mutate_with(df, fn ldf ->
iex> c = Explorer.Series.add(ldf["a"], ldf["b"])
iex> [c: c, d: Explorer.Series.window_sum(c, 2)]
iex> end)
#Explorer.DataFrame<
Polars[3 x 4]
a s64 [4, 5, 6]
b s64 [1, 2, 3]
c s64 [5, 7, 9]
d s64 [5, 12, 16]
>
## Grouped examples
Mutations in grouped dataframes takes the context of the group.
For example, if we want to count how many elements of a given group,
we can add a new column with that aggregation:
iex> df = Explorer.DataFrame.new(id: ["a", "a", "b"], b: [1, 2, 3])
iex> grouped = Explorer.DataFrame.group_by(df, :id)
iex> Explorer.DataFrame.mutate_with(grouped, &[count: Explorer.Series.count(&1["b"])])
#Explorer.DataFrame<
Polars[3 x 3]
Groups: ["id"]
id string ["a", "a", "b"]
b s64 [1, 2, 3]
count u32 [2, 2, 1]
>
"""
@doc type: :single
@spec mutate_with(
df :: DataFrame.t(),
callback :: (Explorer.Backend.LazyFrame.t() -> column_pairs(Series.lazy_t()))
) :: DataFrame.t()
def mutate_with(%DataFrame{} = df, fun) when is_function(fun) do
ldf = Explorer.Backend.LazyFrame.new(df)
result = fun.(ldf)
column_pairs = to_column_pairs(df, result, &query_to_series!/1)
new_dtypes =
for {column_name, series} <- column_pairs, into: %{} do
{column_name, series.dtype}
end
mut_names = Enum.map(column_pairs, &elem(&1, 0))
new_names = Enum.uniq(df.names ++ mut_names)
df_out = %{df | names: new_names, dtypes: Map.merge(df.dtypes, new_dtypes)}
column_pairs = for {name, %Series{data: lazy_series}} <- column_pairs, do: {name, lazy_series}
Shared.apply_impl(df, :mutate_with, [df_out, column_pairs])
end
defp query_to_series!(%Series{} = series), do: series
defp query_to_series!(list) when is_list(list) do
raise ArgumentError,
"expecting a lazy series or scalar value, but instead got a list. " <>
"consider using `Explorer.Series.from_list/2` to create a `Series`, " <>
"and then `Explorer.DataFrame.put/3` to add the series to your dataframe."
end
defp query_to_series!(scalar) do
lazy_s = query_to_lazy_series!(scalar)
Explorer.Backend.Series.new(lazy_s, lazy_s.dtype)
end
defp query_to_lazy_series!(scalar) do
case scalar do
%Series{data: %LazySeries{}} = series ->
series.data
nil ->
LazySeries.new(:lazy, [nil], :null)
number when is_number(number) ->
dtype = if is_integer(number), do: {:s, 64}, else: {:f, 64}
LazySeries.new(:lazy, [number], dtype)
string when is_binary(string) ->
LazySeries.new(:lazy, [string], :string)
boolean when is_boolean(boolean) ->
LazySeries.new(:lazy, [boolean], :boolean)
date = %Date{} ->
LazySeries.new(:lazy, [date], :date)
datetime = %NaiveDateTime{} ->
LazySeries.new(:lazy, [datetime], {:datetime, :nanosecond})
duration = %Explorer.Duration{precision: precision} ->
LazySeries.new(:lazy, [duration], {:duration, precision})
map = %{} when not is_struct(map) ->
{series_list, dtype_list} =
Enum.reduce(map, {[], []}, fn {name, series}, {sl, dl} ->
lazy_series = query_to_lazy_series!(series)
name = if is_atom(name), do: Atom.to_string(name), else: name
{[{name, lazy_series} | sl], [{name, lazy_series.dtype} | dl]}
end)
map = Enum.into(series_list, %{})
dtype_list = Enum.sort(dtype_list)
LazySeries.new(:lazy, [map], {:struct, dtype_list})
other ->
raise ArgumentError,
"expecting a lazy series or scalar value, but instead got #{inspect(other)}"
end
end
@doc """
Creates or modifies a single column.
This is a simplified way to add or modify one column,
accepting a series, tensor, or a list.
If you are computing a series, it is preferrable to use
`mutate/2` or `mutate_with/2` to compute the series and
modify it in a single step, as it is more powerful and
it handles both expressions and scalar values accordingly.
If you are passing tensors or lists, they will be automatically
converted to a series. By default, the new series will have the
same dtype as the existing series, unless the `:dtype` option
is given. If there is no existing series, one is inferred from
the tensor/list.
## Examples
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> Explorer.DataFrame.put(df, :b, Explorer.Series.transform(df[:a], fn n -> n * 2 end))
#Explorer.DataFrame<
Polars[3 x 2]
a s64 [1, 2, 3]
b s64 [2, 4, 6]
>
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> Explorer.DataFrame.put(df, :b, Explorer.Series.from_list([4, 5, 6]))
#Explorer.DataFrame<
Polars[3 x 2]
a s64 [1, 2, 3]
b s64 [4, 5, 6]
>
## Grouped examples
If the dataframe is grouped, `put/3` is going to ignore the groups.
So the series must be of the same size of the entire dataframe.
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> grouped = Explorer.DataFrame.group_by(df, "a")
iex> series = Explorer.Series.from_list([9, 8, 7])
iex> Explorer.DataFrame.put(grouped, :b, series)
#Explorer.DataFrame<
Polars[3 x 2]
Groups: ["a"]
a s64 [1, 2, 3]
b s64 [9, 8, 7]
>
## Tensor examples
You can also put tensors into the dataframe:
iex> df = Explorer.DataFrame.new([])
iex> Explorer.DataFrame.put(df, :a, Nx.tensor([1, 2, 3]))
#Explorer.DataFrame<
Polars[3 x 1]
a s64 [1, 2, 3]
>
You can specify which dtype the tensor represents.
For example, a tensor of s64 represents integers
by default, but it may also represent timestamps
in microseconds from the Unix epoch:
iex> df = Explorer.DataFrame.new([])
iex> Explorer.DataFrame.put(df, :a, Nx.tensor([1, 2, 3]), dtype: {:datetime, :microsecond})
#Explorer.DataFrame<
Polars[3 x 1]
a datetime[μs] [1970-01-01 00:00:00.000001, 1970-01-01 00:00:00.000002, 1970-01-01 00:00:00.000003]
>
If there is already a column where we want to place the tensor,
the column dtype will be automatically used, this means that
updating dataframes in place while preserving their types is
straight-forward:
iex> df = Explorer.DataFrame.new(a: [~N[1970-01-01 00:00:00]])
iex> Explorer.DataFrame.put(df, :a, Nx.tensor(529550625987654))
#Explorer.DataFrame<
Polars[1 x 1]
a datetime[μs] [1986-10-13 01:23:45.987654]
>
This is particularly useful for categorical columns:
iex> cat = Explorer.Series.from_list(["foo", "bar", "baz"], dtype: :category)
iex> df = Explorer.DataFrame.new(a: cat)
iex> Explorer.DataFrame.put(df, :a, Nx.tensor([2, 1, 0]))
#Explorer.DataFrame<
Polars[3 x 1]
a category ["baz", "bar", "foo"]
>
On the other hand, if you try to put a floating tensor on
an integer column, an error will be raised unless a dtype
or `dtype: :infer` is given:
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> Explorer.DataFrame.put(df, :a, Nx.tensor(1.0, type: :f64))
** (ArgumentError) dtype {:s, 64} expects a tensor of type {:s, 64} but got type {:f, 64}
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> Explorer.DataFrame.put(df, :a, Nx.tensor(1.0, type: :f64), dtype: {:f, 64})
#Explorer.DataFrame<
Polars[3 x 1]
a f64 [1.0, 1.0, 1.0]
>
iex> df = Explorer.DataFrame.new(a: [1, 2, 3])
iex> Explorer.DataFrame.put(df, :a, Nx.tensor(1.0, type: :f64), dtype: :infer)
#Explorer.DataFrame<
Polars[3 x 1]
a f64 [1.0, 1.0, 1.0]
>
## List examples
Similar to tensors, we can also put lists in the dataframe:
iex> df = Explorer.DataFrame.new([])
iex> Explorer.DataFrame.put(df, :a, [1, 2, 3])
#Explorer.DataFrame<
Polars[3 x 1]
a s64 [1, 2, 3]
>
The same considerations as above apply.
"""
@doc type: :single
@spec put(DataFrame.t(), column_name(), Series.t() | Nx.Tensor.t() | list()) :: DataFrame.t()
def put(df, column_name, series_or_tensor_or_list, opts \\ [])
def put(%DataFrame{} = df, column_name, %Series{} = series, opts)
when is_column_name(column_name) do
Keyword.validate!(opts, [])
name = to_column_name(column_name)
new_names = append_unless_present(df.names, name)
out_df = %{df | names: new_names, dtypes: Map.put(df.dtypes, name, series.dtype)}
Shared.apply_impl(df, :put, [out_df, name, series])
end
def put(%DataFrame{} = df, column_name, tensor, opts)
when is_column_name(column_name) and is_struct(tensor, Nx.Tensor) do
put(df, column_name, :from_tensor, tensor, opts)
end
def put(%DataFrame{} = df, column_name, list, opts)
when is_column_name(column_name) and is_list(list) do
put(df, column_name, :from_list, list, opts)
end
defp put(%DataFrame{} = df, column_name, fun, arg, opts) when is_column_name(column_name) do
opts = Keyword.validate!(opts, [:dtype])
name = to_column_name(column_name)
dtype = opts[:dtype]
if dtype == nil and is_map_key(df.dtypes, name) do
put(df, name, Series.replace(pull(df, name), arg), [])
else
backend = df_backend_to_s_backend(df.data.__struct__)
opts =
if dtype == nil or dtype == :infer do
[backend: backend]
else
[dtype: Shared.normalise_dtype!(dtype), backend: backend]
end
put(df, name, apply(Series, fun, [arg, opts]), [])
end
end
defp df_backend_to_s_backend(backend) do
backend_df_string = Atom.to_string(backend)
backend_string = binary_part(backend_df_string, 0, byte_size(backend_df_string) - 10)
String.to_atom(backend_string)
end
defp append_unless_present([name | tail], name), do: [name | tail]
defp append_unless_present([head | tail], name), do: [head | append_unless_present(tail, name)]
defp append_unless_present([], name), do: [name]
@doc """
Sorts rows by columns using `Explorer.Query`.
> #### Notice {: .notice}
>
> This is a macro. You must `require Explorer.DataFrame` before using it.
See `sort_with/2` for a callback version of this function without
`Explorer.Query`.
## Options
* `:nils` - `:first` or `:last`.
By default it is `:last` if direction is `:asc`, and `:first` otherwise.
* `:parallel` - boolean.
Whether to parallelize the sorting.
By default it is `true`.
Parallel sort isn't available on certain lazy operations.
In those situations this option is ignored.
* `:stable` - boolean.
Determines if the sorting is stable (ties are guaranteed to maintain their order) or not.
Unstable sorting may be more performant.
By default it is `false`.
## Examples
A single column name will sort ascending by that column:
iex> df = Explorer.DataFrame.new(a: ["b", "c", "a"], b: [1, 2, 3])
iex> Explorer.DataFrame.sort_by(df, a)
#Explorer.DataFrame<
Polars[3 x 2]
a string ["a", "b", "c"]
b s64 [3, 1, 2]
>
You can also sort descending:
iex> df = Explorer.DataFrame.new(a: ["b", "c", "a"], b: [1, 2, 3])
iex> Explorer.DataFrame.sort_by(df, desc: a)
#Explorer.DataFrame<
Polars[3 x 2]
a string ["c", "b", "a"]
b s64 [2, 1, 3]
>
You can specify how `nil`s are sorted:
iex> df = Explorer.DataFrame.new(a: ["b", "c", nil, "a"])
iex> Explorer.DataFrame.sort_by(df, [desc: a], nils: :first)
#Explorer.DataFrame<
Polars[4 x 1]
a string [nil, "c", "b", "a"]
>
Sorting by more than one column sorts them in the order they are entered:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.sort_by(df, asc: total, desc: country)
#Explorer.DataFrame<
Polars[1094 x 10]
year s64 [2010, 2010, 2011, 2011, 2012, ...]
country string ["NIUE", "TUVALU", "TUVALU", "NIUE", "NIUE", ...]
total s64 [1, 2, 2, 2, 2, ...]
solid_fuel s64 [0, 0, 0, 0, 0, ...]
liquid_fuel s64 [1, 2, 2, 2, 2, ...]
gas_fuel s64 [0, 0, 0, 0, 0, ...]
cement s64 [0, 0, 0, 0, 0, ...]
gas_flaring s64 [0, 0, 0, 0, 0, ...]
per_capita f64 [0.52, 0.0, 0.0, 1.04, 1.04, ...]
bunker_fuels s64 [0, 0, 0, 0, 0, ...]
>
## Grouped examples
When used in a grouped dataframe, sort_by is going to sort each group individually and
then return the entire dataframe with the existing groups. If one of the sort_by columns
is also a group, the sorting for that column is not going to work. It is necessary to
first summarise the desired column and then sort_by it.
Here is an example using the Iris dataset. We group by species and then we try to sort
the dataframe by species and petal length, but only "petal length" is taken into account
because "species" is a group.
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.sort_by(grouped, desc: species, asc: sepal_width)
#Explorer.DataFrame<
Polars[150 x 5]
Groups: ["species"]
sepal_length f64 [4.5, 4.4, 4.9, 4.8, 4.3, ...]
sepal_width f64 [2.3, 2.9, 3.0, 3.0, 3.0, ...]
petal_length f64 [1.3, 1.4, 1.4, 1.4, 1.1, ...]
petal_width f64 [0.3, 0.2, 0.2, 0.1, 0.1, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
defmacro sort_by(df, query, opts \\ []) do
quote do
require Explorer.Query
Explorer.DataFrame.sort_with(
unquote(df),
Explorer.Query.query(unquote(query)),
unquote(opts)
)
end
end
@deprecated "Use sort_by/3 instead"
@doc false
defmacro arrange(df, query, opts \\ []) do
quote do
Explorer.DataFrame.sort_by(unquote(df), unquote(query), unquote(opts))
end
end
@doc """
Sorts rows by columns using a callback function.
The callback receives a lazy dataframe which stores
operations instead of values for efficient sorting.
This is a callback version of `sort_by/2`.
## Options
* `:nils` - `:first` or `:last`.
By default it is `:last` if direction is `:asc`, and `:first` otherwise.
* `:parallel` - boolean.
Whether to parallelize the sorting.
By default it is `true`.
Parallel sort isn't available on certain lazy operations.
In those situations this option is ignored.
* `:stable` - boolean.
Determines if the sorting is stable (ties are guaranteed to maintain their order) or not.
Unstable sorting may be more performant.
By default it is `false`.
## Examples
A single column name will sort ascending by that column:
iex> df = Explorer.DataFrame.new(a: ["b", "c", "a"], b: [1, 2, 3])
iex> Explorer.DataFrame.sort_with(df, &(&1["a"]))
#Explorer.DataFrame<
Polars[3 x 2]
a string ["a", "b", "c"]
b s64 [3, 1, 2]
>
You can also sort descending:
iex> df = Explorer.DataFrame.new(a: ["b", "c", "a"], b: [1, 2, 3])
iex> Explorer.DataFrame.sort_with(df, &[desc: &1["a"]])
#Explorer.DataFrame<
Polars[3 x 2]
a string ["c", "b", "a"]
b s64 [2, 1, 3]
>
You can specify how `nil`s are sorted:
iex> df = Explorer.DataFrame.new(a: ["b", "c", nil, "a"])
iex> Explorer.DataFrame.sort_with(df, &[desc: &1["a"]], nils: :first)
#Explorer.DataFrame<
Polars[4 x 1]
a string [nil, "c", "b", "a"]
>
Sorting by more than one column sorts them in the order they are entered:
iex> df = Explorer.DataFrame.new(a: [3, 1, 3], b: [2, 1, 3])
iex> Explorer.DataFrame.sort_with(df, &[desc: &1["a"], asc: &1["b"]])
#Explorer.DataFrame<
Polars[3 x 2]
a s64 [3, 3, 1]
b s64 [2, 3, 1]
>
## Grouped examples
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.sort_with(grouped, &[desc: &1["species"], asc: &1["sepal_width"]])
#Explorer.DataFrame<
Polars[150 x 5]
Groups: ["species"]
sepal_length f64 [4.5, 4.4, 4.9, 4.8, 4.3, ...]
sepal_width f64 [2.3, 2.9, 3.0, 3.0, 3.0, ...]
petal_length f64 [1.3, 1.4, 1.4, 1.4, 1.1, ...]
petal_width f64 [0.3, 0.2, 0.2, 0.1, 0.1, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
@spec sort_with(
df :: DataFrame.t(),
(Explorer.Backend.LazyFrame.t() ->
Series.lazy_t() | [Series.lazy_t()] | [{:asc | :desc, Series.lazy_t()}]),
opts :: [nils: :first | :last, stable: boolean()]
) :: DataFrame.t()
def sort_with(%DataFrame{} = df, fun, opts \\ []) when is_function(fun, 1) do
[_descending? | opts] = Shared.validate_sort_options!(opts)
ldf = Explorer.Backend.LazyFrame.new(df)
result = fun.(ldf)
dir_and_lazy_series_pairs =
result
|> List.wrap()
|> Enum.map(fn
{dir, %Series{data: %LazySeries{} = lazy_series}} when dir in [:asc, :desc] ->
{dir, lazy_series}
{wrong_dir, %Series{data: %LazySeries{}}} ->
raise "expecting a valid direction, which is :asc or :desc, got: #{inspect(wrong_dir)}"
{_, other} ->
raise "expecting a lazy series, got: #{inspect(other)}"
%Series{data: %LazySeries{} = lazy_series} ->
{:asc, lazy_series}
other ->
raise "not a valid lazy series or sort_by instruction: #{inspect(other)}"
end)
Shared.apply_impl(df, :sort_with, [df, dir_and_lazy_series_pairs] ++ opts)
end
@deprecated "Use sort_with/3 instead"
@doc false
def arrange_with(df, fun, opts \\ []), do: sort_with(df, fun, opts)
@doc """
Takes distinct rows by a selection of columns.
Distinct is not affected by groups, although groups are kept in the
columns selection if `keep_all` option is false (the default).
## Options
* `:keep_all` - If set to `true`, keep all columns. Default is `false`.
## Examples
By default will return unique values of the requested columns:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.distinct(df, ["year", "country"])
#Explorer.DataFrame<
Polars[1094 x 2]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
>
If `keep_all` is set to `true`, then the first value of each column not in the requested
columns will be returned:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.distinct(df, ["year", "country"], keep_all: true)
#Explorer.DataFrame<
Polars[1094 x 10]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
A callback on the dataframe's names can be passed instead of a list (like `select/2`):
iex> df = Explorer.DataFrame.new(x1: [1, 3, 3], x2: ["a", "c", "c"], y1: [1, 2, 3])
iex> Explorer.DataFrame.distinct(df, &String.starts_with?(&1, "x"))
#Explorer.DataFrame<
Polars[2 x 2]
x1 s64 [1, 3]
x2 string ["a", "c"]
>
If the dataframe has groups, then the columns of each group will be added to the distinct columns:
iex> df = Explorer.DataFrame.new(x1: [1, 3, 3], x2: ["a", "c", "c"], y1: [1, 2, 3])
iex> df = Explorer.DataFrame.group_by(df, "x1")
iex> Explorer.DataFrame.distinct(df, ["x2"])
#Explorer.DataFrame<
Polars[2 x 2]
Groups: ["x1"]
x1 s64 [1, 3]
x2 string ["a", "c"]
>
"""
@doc type: :single
@spec distinct(df :: DataFrame.t(), columns :: columns(), opts :: Keyword.t()) :: DataFrame.t()
def distinct(df, columns \\ .., opts \\ [])
def distinct(df, columns, opts) do
opts = Keyword.validate!(opts, keep_all: false)
columns = to_existing_columns(df, columns)
if columns != [] do
out_df =
if opts[:keep_all] do
df
else
groups = df.groups
keep = if groups == [], do: columns, else: Enum.uniq(groups ++ columns)
%{df | names: keep, dtypes: Map.take(df.dtypes, keep)}
end
Shared.apply_impl(df, :distinct, [out_df, columns])
else
df
end
end
@doc """
Drop nil values.
Optionally accepts a subset of columns.
## Examples
To drop nils on all columns:
iex> df = Explorer.DataFrame.new(a: [1, 2, nil], b: [1, nil, 3])
iex> Explorer.DataFrame.drop_nil(df)
#Explorer.DataFrame<
Polars[1 x 2]
a s64 [1]
b s64 [1]
>
To drop nils on a single column:
iex> df = Explorer.DataFrame.new(a: [1, 2, nil], b: [1, nil, 3])
iex> Explorer.DataFrame.drop_nil(df, :a)
#Explorer.DataFrame<
Polars[2 x 2]
a s64 [1, 2]
b s64 [1, nil]
>
To drop some columns:
iex> df = Explorer.DataFrame.new(a: [1, 2, nil], b: [1, nil, 3], c: [nil, 5, 6])
iex> Explorer.DataFrame.drop_nil(df, [:a, :c])
#Explorer.DataFrame<
Polars[1 x 3]
a s64 [2]
b s64 [nil]
c s64 [5]
>
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
"""
@doc type: :single
@spec drop_nil(df :: DataFrame.t(), column() | columns()) ::
DataFrame.t()
def drop_nil(df, columns_or_column \\ ..)
def drop_nil(df, column) when is_column(column), do: drop_nil(df, [column])
def drop_nil(df, []), do: df
def drop_nil(df, columns) do
columns = to_existing_columns(df, columns)
Shared.apply_impl(df, :drop_nil, [columns])
end
@doc """
Relocates columns.
Change column order within a DataFrame. The `before` and `after` options are mutually exclusive.
Providing no options will relocate the columns to beginning of the DataFrame.
## Options
* `:before` - Specifies to relocate before the given column.
It can be an index or a column name.
* `:after` - Specifies to relocate after the given column.
It can be an index or a column name.
## Examples
Relocate a single column
iex> df = Explorer.DataFrame.new(a: ["a", "b", "a"], b: [1, 3, 1], c: [nil, 5, 6])
iex> Explorer.DataFrame.relocate(df, "a", after: "c")
#Explorer.DataFrame<
Polars[3 x 3]
b s64 [1, 3, 1]
c s64 [nil, 5, 6]
a string ["a", "b", "a"]
>
Relocate (and reorder) multiple columns to the beginning
iex> df = Explorer.DataFrame.new(a: [1, 2], b: [5.1, 5.2], c: [4, 5], d: ["yes", "no"])
iex> Explorer.DataFrame.relocate(df, ["d", 1], before: 0)
#Explorer.DataFrame<
Polars[2 x 4]
d string ["yes", "no"]
b f64 [5.1, 5.2]
a s64 [1, 2]
c s64 [4, 5]
>
Relocate before another column
iex> df = Explorer.DataFrame.new(a: [1, 2], b: [5.1, 5.2], c: [4, 5], d: ["yes", "no"])
iex> Explorer.DataFrame.relocate(df, ["a", "c"], before: "b")
#Explorer.DataFrame<
Polars[2 x 4]
a s64 [1, 2]
c s64 [4, 5]
b f64 [5.1, 5.2]
d string ["yes", "no"]
>
"""
@doc type: :single
@spec relocate(
df :: DataFrame.t(),
columns :: [column()] | column(),
opts :: Keyword.t()
) :: DataFrame.t()
def relocate(df, columns_or_column, opts)
def relocate(df, column, opts) when is_column(column),
do: relocate(df, [column], opts)
def relocate(df, columns, opts) do
opts = Keyword.validate!(opts, before: nil, after: nil)
columns = to_existing_columns(df, columns) |> Enum.uniq()
columns =
case {opts[:before], opts[:after]} do
{before_col, nil} ->
{:before, before_col}
{nil, after_col} ->
{:after, after_col}
{before_col, after_col} ->
raise(
ArgumentError,
"only one location must be given. Got both " <>
"before: #{inspect(before_col)} and after: #{inspect(after_col)}"
)
end
|> relocate_columns(df, columns)
select(df, columns)
end
defp relocate_columns({direction, target_column}, df, columns_to_relocate) do
[target_column] = to_existing_columns(df, [target_column])
offset =
case direction do
:before -> 0
:after -> 1
end
target_index = Enum.find_index(df.names, fn col -> col == target_column end) + offset
df.names
|> Enum.split(target_index)
|> Kernel.then(fn {before_cols, after_cols} ->
Enum.reject(before_cols, &(&1 in columns_to_relocate)) ++
columns_to_relocate ++ Enum.reject(after_cols, &(&1 in columns_to_relocate))
end)
end
@doc """
Renames columns.
Renaming a column that is also a group is going to rename the group as well.
To apply a function to a subset of columns, see `rename_with/3`.
## Examples
You can pass in a list of new names:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "a"], b: [1, 3, 1])
iex> Explorer.DataFrame.rename(df, ["c", "d"])
#Explorer.DataFrame<
Polars[3 x 2]
c string ["a", "b", "a"]
d s64 [1, 3, 1]
>
Or you can rename individual columns using keyword args:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "a"], b: [1, 3, 1])
iex> Explorer.DataFrame.rename(df, a: "first")
#Explorer.DataFrame<
Polars[3 x 2]
first string ["a", "b", "a"]
b s64 [1, 3, 1]
>
Or you can rename individual columns using a map:
iex> df = Explorer.DataFrame.new(a: ["a", "b", "a"], b: [1, 3, 1])
iex> Explorer.DataFrame.rename(df, %{"a" => "first"})
#Explorer.DataFrame<
Polars[3 x 2]
first string ["a", "b", "a"]
b s64 [1, 3, 1]
>
"""
@doc type: :single
@spec rename(
df :: DataFrame.t(),
names :: column_names() | column_pairs(column_name())
) ::
DataFrame.t()
def rename(df, [name | _] = names) when is_column_name(name) do
check_new_names_length!(df, names)
rename(df, Enum.zip(df.names, names))
end
def rename(df, names) when is_column_pairs(names) do
case to_column_pairs(df, names, &to_column_name(&1)) do
[] ->
df
pairs ->
pairs_map = Map.new(pairs)
if Enum.count(pairs) != map_size(pairs_map),
do: raise(ArgumentError, "duplicate source column for rename")
old_dtypes = df.dtypes
for {name, _} <- pairs do
Shared.maybe_raise_column_not_found(df, name)
end
new_dtypes =
for name <- df.names do
{Map.get(pairs_map, name, name), Map.fetch!(old_dtypes, name)}
end
new_names = Enum.map(new_dtypes, &elem(&1, 0))
new_groups =
for group <- df.groups do
Map.get(pairs_map, group, group)
end
out_df = %{df | names: new_names, dtypes: Map.new(new_dtypes), groups: new_groups}
Shared.apply_impl(df, :rename, [out_df, pairs])
end
end
defp check_new_names_length!(df, names) do
width = n_columns(df)
n_new_names = length(names)
if width != n_new_names,
do:
raise(
ArgumentError,
"list of new names must match the number of columns in the dataframe; found " <>
"#{n_new_names} new name(s), but the supplied dataframe has #{width} column(s)"
)
end
@doc """
Renames columns with a function.
Renaming a column that is also a group is going to rename the group as well.
## Examples
If no columns are specified, it will apply the function to all column names:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.rename_with(df, &String.upcase/1)
#Explorer.DataFrame<
Polars[1094 x 10]
YEAR s64 [2010, 2010, 2010, 2010, 2010, ...]
COUNTRY string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
TOTAL s64 [2308, 1254, 32500, 141, 7924, ...]
SOLID_FUEL s64 [627, 117, 332, 0, 0, ...]
LIQUID_FUEL s64 [1601, 953, 12381, 141, 3649, ...]
GAS_FUEL s64 [74, 7, 14565, 0, 374, ...]
CEMENT s64 [5, 177, 2598, 0, 204, ...]
GAS_FLARING s64 [0, 0, 2623, 0, 3697, ...]
PER_CAPITA f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
BUNKER_FUELS s64 [9, 7, 663, 0, 321, ...]
>
A callback can be used to filter the column names that will be renamed, similarly to `select/2`:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.rename_with(df, &String.ends_with?(&1, "_fuel"), &String.trim_trailing(&1, "_fuel"))
#Explorer.DataFrame<
Polars[1094 x 10]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid s64 [627, 117, 332, 0, 0, ...]
liquid s64 [1601, 953, 12381, 141, 3649, ...]
gas s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Or you can just pass in the list of column names you'd like to apply the function to:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.rename_with(df, ["total", "cement"], &String.upcase/1)
#Explorer.DataFrame<
Polars[1094 x 10]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
TOTAL s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
CEMENT s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
"""
@doc type: :single
@spec rename_with(df :: DataFrame.t(), columns :: columns(), callback :: function()) ::
DataFrame.t()
def rename_with(df, columns \\ .., callback)
def rename_with(df, .., callback) when is_function(callback) do
df.names
|> Enum.map(callback)
|> then(&rename(df, &1))
end
def rename_with(df, columns, callback) when is_function(callback) do
columns = to_existing_columns(df, columns)
renames = for name <- df.names, name in columns, do: {name, callback.(name)}
rename(df, renames)
end
@doc """
Turns a set of columns to dummy variables.
In case the dataframe is using groups, all groups will be removed.
## Examples
To mark a single column as dummy:
iex> df = Explorer.DataFrame.new(col_x: ["a", "b", "a", "c"], col_y: ["b", "a", "b", "d"])
iex> Explorer.DataFrame.dummies(df, "col_x")
#Explorer.DataFrame<
Polars[4 x 3]
col_x_a u8 [1, 0, 1, 0]
col_x_b u8 [0, 1, 0, 0]
col_x_c u8 [0, 0, 0, 1]
>
Or multiple columns:
iex> df = Explorer.DataFrame.new(col_x: ["a", "b", "a", "c"], col_y: ["b", "a", "b", "d"])
iex> Explorer.DataFrame.dummies(df, ["col_x", "col_y"])
#Explorer.DataFrame<
Polars[4 x 6]
col_x_a u8 [1, 0, 1, 0]
col_x_b u8 [0, 1, 0, 0]
col_x_c u8 [0, 0, 0, 1]
col_y_b u8 [1, 0, 1, 0]
col_y_a u8 [0, 1, 0, 0]
col_y_d u8 [0, 0, 0, 1]
>
Or all string columns:
iex> df = Explorer.DataFrame.new(num: [1, 2, 3, 4], col_y: ["b", "a", "b", "d"])
iex> Explorer.DataFrame.dummies(df, fn _name, type -> type == :string end)
#Explorer.DataFrame<
Polars[4 x 3]
col_y_b u8 [1, 0, 1, 0]
col_y_a u8 [0, 1, 0, 0]
col_y_d u8 [0, 0, 0, 1]
>
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
"""
@doc type: :single
@spec dummies(df :: DataFrame.t(), column() | columns()) ::
DataFrame.t()
def dummies(df, columns_or_column)
def dummies(df, column) when is_column(column), do: dummies(df, [column])
def dummies(df, columns) do
columns = to_existing_columns(df, columns)
out_columns =
for column <- columns,
value <- Series.to_list(Series.distinct(df[column])),
do: column <> "_#{value}"
out_dtypes = for new_column <- out_columns, into: %{}, do: {new_column, {:u, 8}}
out_df = %{df | groups: [], names: out_columns, dtypes: out_dtypes}
Shared.apply_impl(df, :dummies, [out_df, columns])
end
@doc """
Extracts a single column as a series.
This is equivalent to `df[field]` for retrieving a single field.
The returned series will have its `:name` field set to the column name.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.pull(df, "total")
#Explorer.Series<
Polars[1094]
s64 [2308, 1254, 32500, 141, 7924, 41, 143, 51246, 1150, 684, 106589, 18408, 8366, 451, 7981, 16345, 403, 17192, 30222, 147, 1388, 166, 133, 5802, 1278, 114468, 47, 2237, 12030, 535, 58, 1367, 145806, 152, 152, 72, 141, 19703, 2393248, 20773, 44, 540, 19, 2064, 1900, 5501, 10465, 2102, 30428, 18122, ...]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.pull(df, 2)
#Explorer.Series<
Polars[1094]
s64 [2308, 1254, 32500, 141, 7924, 41, 143, 51246, 1150, 684, 106589, 18408, 8366, 451, 7981, 16345, 403, 17192, 30222, 147, 1388, 166, 133, 5802, 1278, 114468, 47, 2237, 12030, 535, 58, 1367, 145806, 152, 152, 72, 141, 19703, 2393248, 20773, 44, 540, 19, 2064, 1900, 5501, 10465, 2102, 30428, 18122, ...]
>
"""
@doc type: :single
@spec pull(df :: DataFrame.t(), column :: column()) :: Series.t()
def pull(df, column) when is_column(column) do
[column] = to_existing_columns(df, [column])
pull_existing(df, column)
end
defp pull_existing(df, column) do
series = Shared.apply_impl(df, :pull, [column])
%{series | name: column}
end
@doc """
Subset a continuous set of rows.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.slice(df, 1, 2)
#Explorer.DataFrame<
Polars[2 x 10]
year s64 [2010, 2010]
country string ["ALBANIA", "ALGERIA"]
total s64 [1254, 32500]
solid_fuel s64 [117, 332]
liquid_fuel s64 [953, 12381]
gas_fuel s64 [7, 14565]
cement s64 [177, 2598]
gas_flaring s64 [0, 2623]
per_capita f64 [0.43, 0.9]
bunker_fuels s64 [7, 663]
>
Negative offsets count from the end of the series:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.slice(df, -10, 2)
#Explorer.DataFrame<
Polars[2 x 10]
year s64 [2014, 2014]
country string ["UNITED STATES OF AMERICA", "URUGUAY"]
total s64 [1432855, 1840]
solid_fuel s64 [450047, 2]
liquid_fuel s64 [576531, 1700]
gas_fuel s64 [390719, 25]
cement s64 [11314, 112]
gas_flaring s64 [4244, 0]
per_capita f64 [4.43, 0.54]
bunker_fuels s64 [30722, 251]
>
If the length would run past the end of the dataframe, the result may be shorter than the length:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.slice(df, -10, 20)
#Explorer.DataFrame<
Polars[10 x 10]
year s64 [2014, 2014, 2014, 2014, 2014, ...]
country string ["UNITED STATES OF AMERICA", "URUGUAY", "UZBEKISTAN", "VANUATU", "VENEZUELA", ...]
total s64 [1432855, 1840, 28692, 42, 50510, ...]
solid_fuel s64 [450047, 2, 1677, 0, 204, ...]
liquid_fuel s64 [576531, 1700, 2086, 42, 28445, ...]
gas_fuel s64 [390719, 25, 23929, 0, 12731, ...]
cement s64 [11314, 112, 1000, 0, 1088, ...]
gas_flaring s64 [4244, 0, 0, 0, 8042, ...]
per_capita f64 [4.43, 0.54, 0.97, 0.16, 1.65, ...]
bunker_fuels s64 [30722, 251, 0, 10, 1256, ...]
>
## Grouped examples
We want to take the first 3 rows of each group. We need the offset 0 and the length 3:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.slice(grouped, 0, 3)
#Explorer.DataFrame<
Polars[9 x 5]
Groups: ["species"]
sepal_length f64 [5.1, 4.9, 4.7, 7.0, 6.4, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.2, 3.2, ...]
petal_length f64 [1.4, 1.4, 1.3, 4.7, 4.5, ...]
petal_width f64 [0.2, 0.2, 0.2, 1.4, 1.5, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", ...]
>
We can also pass a negative offset:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.slice(grouped, -6, 3)
#Explorer.DataFrame<
Polars[9 x 5]
Groups: ["species"]
sepal_length f64 [5.1, 4.8, 5.1, 5.6, 5.7, ...]
sepal_width f64 [3.8, 3.0, 3.8, 2.7, 3.0, ...]
petal_length f64 [1.9, 1.4, 1.6, 4.2, 4.2, ...]
petal_width f64 [0.4, 0.3, 0.2, 1.3, 1.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", ...]
>
"""
@doc type: :rows
def slice(df, offset, length) when is_integer(offset) and is_integer(length) and length >= 0,
do: Shared.apply_impl(df, :slice, [offset, length])
@doc """
Slices rows at the given indices as a new dataframe.
The indices may be a list or series of indices, or a range.
A list of indices does not support negative numbers.
Ranges may be negative on either end, which are then
normalized. Note ranges in Elixir are inclusive.
Slice works differently when a dataframe is grouped.
It is going to consider the indices of each group
instead of the entire dataframe. See the examples below.
If your intention is to grab a portion of each group,
prefer to use `sample/3` instead.
## Examples
iex> df = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> Explorer.DataFrame.slice(df, [0, 2])
#Explorer.DataFrame<
Polars[2 x 2]
a s64 [1, 3]
b string ["a", "c"]
>
With a series
iex> df = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> Explorer.DataFrame.slice(df, Explorer.Series.from_list([0, 2]))
#Explorer.DataFrame<
Polars[2 x 2]
a s64 [1, 3]
b string ["a", "c"]
>
With a range:
iex> df = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> Explorer.DataFrame.slice(df, 1..2)
#Explorer.DataFrame<
Polars[2 x 2]
a s64 [2, 3]
b string ["b", "c"]
>
With a range with negative first and last:
iex> df = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> Explorer.DataFrame.slice(df, -2..-1)
#Explorer.DataFrame<
Polars[2 x 2]
a s64 [2, 3]
b string ["b", "c"]
>
## Grouped examples
We are going to once again use the Iris dataset.
In this example we want to take elements at indexes
0 and 2:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.slice(grouped, [0, 2])
#Explorer.DataFrame<
Polars[6 x 5]
Groups: ["species"]
sepal_length f64 [5.1, 4.7, 7.0, 6.9, 6.3, ...]
sepal_width f64 [3.5, 3.2, 3.2, 3.1, 3.3, ...]
petal_length f64 [1.4, 1.3, 4.7, 4.9, 6.0, ...]
petal_width f64 [0.2, 0.2, 1.4, 1.5, 2.5, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", "Iris-virginica", ...]
>
Now we want to take the first 3 rows of each group.
This is going to work with the range `0..2`:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.slice(grouped, 0..2)
#Explorer.DataFrame<
Polars[9 x 5]
Groups: ["species"]
sepal_length f64 [5.1, 4.9, 4.7, 7.0, 6.4, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.2, 3.2, ...]
petal_length f64 [1.4, 1.4, 1.3, 4.7, 4.5, ...]
petal_width f64 [0.2, 0.2, 0.2, 1.4, 1.5, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", ...]
>
"""
@doc type: :rows
def slice(%DataFrame{groups: []} = df, row_indices) when is_list(row_indices) do
n_rows = n_rows(df)
Enum.each(row_indices, fn idx ->
if idx > n_rows or idx < -n_rows,
do:
raise(
ArgumentError,
"requested row index (#{idx}) out of bounds (-#{n_rows}:#{n_rows})"
)
end)
Shared.apply_impl(df, :slice, [row_indices])
end
def slice(%DataFrame{} = df, %Series{dtype: int_dtype} = indices)
when int_dtype in @integer_types do
Shared.apply_impl(df, :slice, [indices])
end
def slice(%DataFrame{groups: []} = df, first..last//1) do
first = if first < 0, do: first + n_rows(df), else: first
last = if last < 0, do: last + n_rows(df), else: last
size = last - first + 1
if first >= 0 and size >= 0 do
Shared.apply_impl(df, :slice, [first, size])
else
Shared.apply_impl(df, :slice, [[]])
end
end
def slice(%DataFrame{groups: []} = df, %Range{} = range) do
slice(df, Enum.slice(0..(n_rows(df) - 1)//1, range))
end
def slice(%DataFrame{groups: [_ | _]} = df, row_indices) when is_list(row_indices),
do: Shared.apply_impl(df, :slice, [row_indices])
def slice(%DataFrame{groups: [_ | _]} = df, %Range{} = range),
do: Shared.apply_impl(df, :slice, [range])
@doc """
Sample rows from a dataframe.
If given an integer as the second argument, it will return N samples. If given a float, it will
return that proportion of the series.
Can sample with or without replacement.
For grouped dataframes, sample will take into account the rows of each group, meaning that
if you try to get N samples and you have G groups, you will get N * G rows. See the examples
below.
## Options
* `:replace` - If set to `true`, each sample will be independent and therefore
values may repeat. Required to be `true` for `n` greater then the number of rows
in the dataframe or `frac` > 1.0. (default: `false`)
* `:seed` - An integer to be used as a random seed. If nil, a random value between 0
and 2^64 - 1 will be used. (default: `nil`)
* `:shuffle` - If set to `true`, the resultant dataframe is going to be shuffle
if the sample is equal to the size of the dataframe. (default: `false`)
## Examples
You can sample N rows:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.sample(df, 3, seed: 100)
#Explorer.DataFrame<
Polars[3 x 10]
year s64 [2012, 2013, 2014]
country string ["SYRIAN ARAB REPUBLIC", "EGYPT", "AFGHANISTAN"]
total s64 [12198, 58198, 2675]
solid_fuel s64 [1, 224, 1194]
liquid_fuel s64 [7909, 26501, 1393]
gas_fuel s64 [3265, 24672, 74]
cement s64 [816, 6800, 14]
gas_flaring s64 [208, 0, 0]
per_capita f64 [0.61, 0.66, 0.08]
bunker_fuels s64 [437, 694, 9]
>
Or you can sample a proportion of rows:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.sample(df, 0.03, seed: 100)
#Explorer.DataFrame<
Polars[32 x 10]
year s64 [2013, 2012, 2014, 2011, 2011, ...]
country string ["BRITISH VIRGIN ISLANDS", "TAJIKISTAN", "AFGHANISTAN", "ICELAND", "SINGAPORE", ...]
total s64 [48, 800, 2675, 513, 12332, ...]
solid_fuel s64 [0, 192, 1194, 94, 7, ...]
liquid_fuel s64 [48, 501, 1393, 400, 7774, ...]
gas_fuel s64 [0, 74, 74, 0, 4551, ...]
cement s64 [0, 34, 14, 19, 0, ...]
gas_flaring s64 [0, 0, 0, 0, 0, ...]
per_capita f64 [1.64, 0.1, 0.08, 1.6, 2.38, ...]
bunker_fuels s64 [0, 28, 9, 168, 41786, ...]
>
## Grouped examples
In the following example we have the Iris dataset grouped by species, and we want
to take a sample of two plants from each group. Since we have three species, the
resultant dataframe is going to have six rows (2 * 3).
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.sample(grouped, 2, seed: 100)
#Explorer.DataFrame<
Polars[6 x 5]
Groups: ["species"]
sepal_length f64 [4.8, 5.0, 5.5, 6.5, 7.4, ...]
sepal_width f64 [3.1, 3.6, 2.4, 2.8, 2.8, ...]
petal_length f64 [1.6, 1.4, 3.8, 4.6, 6.1, ...]
petal_width f64 [0.2, 0.2, 1.1, 1.5, 1.9, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-versicolor", "Iris-versicolor", "Iris-virginica", ...]
>
The behaviour is similar when you want to take a fraction of the rows from each group. The main
difference is that each group can have more or less rows, depending on its size.
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.sample(grouped, 0.1, seed: 100)
#Explorer.DataFrame<
Polars[15 x 5]
Groups: ["species"]
sepal_length f64 [5.2, 5.0, 5.2, 5.0, 5.0, ...]
sepal_width f64 [3.4, 3.6, 3.5, 3.0, 3.4, ...]
petal_length f64 [1.4, 1.4, 1.5, 1.6, 1.6, ...]
petal_width f64 [0.2, 0.2, 0.2, 0.2, 0.4, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :rows
@spec sample(df :: DataFrame.t(), n_or_frac :: number(), opts :: Keyword.t()) :: DataFrame.t()
def sample(df, n_or_frac, opts \\ [])
def sample(df, n_or_frac, opts) when is_number(n_or_frac) do
opts = Keyword.validate!(opts, replace: false, shuffle: false, seed: nil)
if groups(df) == [] do
n_rows = n_rows(df)
n = if is_integer(n_or_frac), do: n_or_frac, else: round(n_or_frac * n_rows)
if n > n_rows && opts[:replace] == false do
raise ArgumentError,
"in order to sample more rows than are in the dataframe (#{n_rows}), sampling " <>
"`replace` must be true"
end
end
Shared.apply_impl(df, :sample, [n_or_frac, opts[:replace], opts[:shuffle], opts[:seed]])
end
@doc """
Change the order of the rows of a dataframe randomly.
This function is going to ignore groups.
## Options
* `:seed` - An integer to be used as a random seed. If nil, a random value between 0
and 2^64 - 1 will be used. (default: `nil`)
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.shuffle(df, seed: 100)
#Explorer.DataFrame<
Polars[1094 x 10]
year s64 [2014, 2014, 2014, 2012, 2010, ...]
country string ["ISRAEL", "ARGENTINA", "NETHERLANDS", "YEMEN", "GRENADA", ...]
total s64 [17617, 55638, 45624, 5091, 71, ...]
solid_fuel s64 [6775, 1588, 9070, 129, 0, ...]
liquid_fuel s64 [6013, 25685, 18272, 4173, 71, ...]
gas_fuel s64 [3930, 26368, 18010, 414, 0, ...]
cement s64 [898, 1551, 272, 375, 0, ...]
gas_flaring s64 [0, 446, 0, 0, 0, ...]
per_capita f64 [2.22, 1.29, 2.7, 0.2, 0.68, ...]
bunker_fuels s64 [1011, 2079, 14210, 111, 4, ...]
>
"""
@doc type: :rows
@spec shuffle(df :: DataFrame.t(), opts :: Keyword.t()) :: DataFrame.t()
def shuffle(df, opts \\ [])
def shuffle(df, opts) do
opts = Keyword.validate!(opts, seed: nil)
sample(df, 1.0, seed: opts[:seed], shuffle: true)
end
@doc """
Transpose a DataFrame.
> #### Warning {: .warning}
>
> This is an expensive operation since data is stored in a columnar format.
## Options
* `:header` - When a string is passed, name of the header column is set to the value. When `header` is `true`, name is set to `column` (default: `false`)
* `:columns` - names for non header columns. Length of column_names should match the row count of data frame. (default: `nil`)
## Examples
iex> df = Explorer.DataFrame.new(a: ["d", nil], b: [1, 2], c: ["a", "b"])
iex> Explorer.DataFrame.transpose(df)
#Explorer.DataFrame<
Polars[3 x 2]
column_0 string ["d", "1", "a"]
column_1 string [nil, "2", "b"]
>
iex> df = Explorer.DataFrame.new(a: ["d", nil], b: [1, 2], c: ["a", "b"])
iex> Explorer.DataFrame.transpose(df, columns: ["x", "y"])
#Explorer.DataFrame<
Polars[3 x 2]
x string ["d", "1", "a"]
y string [nil, "2", "b"]
>
iex> df = Explorer.DataFrame.new(a: ["d", nil], b: [1, 2], c: ["a", "b"])
iex> Explorer.DataFrame.transpose(df, header: "name")
#Explorer.DataFrame<
Polars[3 x 3]
name string ["a", "b", "c"]
column_0 string ["d", "1", "a"]
column_1 string [nil, "2", "b"]
>
"""
@doc type: :single
@spec transpose(df :: DataFrame.t(), opts :: Keyword.t()) :: DataFrame.t()
def transpose(df, opts \\ []) do
opts = Keyword.validate!(opts, header: false, columns: nil)
header =
case opts[:header] do
false -> nil
true -> "column"
header -> to_column_name(header)
end
columns = opts[:columns] && Enum.map(opts[:columns], &to_column_name/1)
df_length = n_rows(df)
names =
cond do
columns == nil ->
for i <- 0..(df_length - 1), do: "column_#{i}"
length(columns) == df_length ->
columns
true ->
raise ArgumentError,
"invalid :columns option, length of column names (#{length(columns)}) must match the row count (#{df_length})"
end
names = if header, do: [header | names], else: names
out_df = %{
df
| names: names,
dtypes: Enum.map(names, fn n -> {n, :string} end) |> Enum.into(%{})
}
args = [out_df, header, columns]
Shared.apply_impl(df, :transpose, args)
end
@doc """
Pivot data from wide to long.
`pivot_longer/3` "lengthens" data, increasing the number of rows and
decreasing the number of columns. The inverse transformation is `pivot_wider/4`.
The second argument, `columns_to_pivot`, can be either list of column names to pivot.
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
The selected columns must always have the same data type.
In case the dataframe is using groups, the groups that are also in the list of columns
to pivot will be removed from the resultant dataframe. See the examples below.
## Options
* `:select` - Columns that are not in the list of pivot and should be kept in the dataframe.
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
Defaults to all columns except the ones to pivot.
* `:discard` - Columns that are not in the list of pivot and should be dropped from the dataframe.
Ranges, regexes, and functions are also accepted in column names, as in `select/2`.
This list of columns is going to be subtracted from the list of `select`.
Defaults to an empty list.
* `:names_to` - A string specifying the name of the column to create from the data stored
in the column names of the dataframe. Defaults to `"variable"`.
* `:values_to` - A string specifying the name of the column to create from the data stored
in series element values. Defaults to `"value"`.
## Examples
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.pivot_longer(df, &String.ends_with?(&1, "fuel"))
#Explorer.DataFrame<
Polars[3282 x 9]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
variable string ["solid_fuel", "solid_fuel", "solid_fuel", "solid_fuel", "solid_fuel", ...]
value s64 [627, 117, 332, 0, 0, ...]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.pivot_longer(df, &String.ends_with?(&1, "fuel"), select: ["year", "country"])
#Explorer.DataFrame<
Polars[3282 x 4]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
variable string ["solid_fuel", "solid_fuel", "solid_fuel", "solid_fuel", "solid_fuel", ...]
value s64 [627, 117, 332, 0, 0, ...]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.pivot_longer(df, ["total"], select: ["year", "country"], discard: ["country"])
#Explorer.DataFrame<
Polars[1094 x 3]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
variable string ["total", "total", "total", "total", "total", ...]
value s64 [2308, 1254, 32500, 141, 7924, ...]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.pivot_longer(df, ["total"], select: [], names_to: "my_var", values_to: "my_value")
#Explorer.DataFrame<
Polars[1094 x 2]
my_var string ["total", "total", "total", "total", "total", ...]
my_value s64 [2308, 1254, 32500, 141, 7924, ...]
>
## Grouped examples
In the following example we want to take the Iris dataset and increase the number of rows by
pivoting the "sepal_length" column. This dataset is grouped by "species", so the resultant
dataframe is going to keep the "species" group:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.pivot_longer(grouped, ["sepal_length"])
#Explorer.DataFrame<
Polars[150 x 6]
Groups: ["species"]
sepal_width f64 [3.5, 3.0, 3.2, 3.1, 3.6, ...]
petal_length f64 [1.4, 1.4, 1.3, 1.5, 1.4, ...]
petal_width f64 [0.2, 0.2, 0.2, 0.2, 0.2, ...]
species string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
variable string ["sepal_length", "sepal_length", "sepal_length", "sepal_length", "sepal_length", ...]
value f64 [5.1, 4.9, 4.7, 4.6, 5.0, ...]
>
Now we want to do something different: we want to pivot the "species" column that is also a group.
This is going to remove the group in the resultant dataframe:
iex> df = Explorer.Datasets.iris()
iex> grouped = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.pivot_longer(grouped, ["species"])
#Explorer.DataFrame<
Polars[150 x 6]
sepal_length f64 [5.1, 4.9, 4.7, 4.6, 5.0, ...]
sepal_width f64 [3.5, 3.0, 3.2, 3.1, 3.6, ...]
petal_length f64 [1.4, 1.4, 1.3, 1.5, 1.4, ...]
petal_width f64 [0.2, 0.2, 0.2, 0.2, 0.2, ...]
variable string ["species", "species", "species", "species", "species", ...]
value string ["Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", "Iris-setosa", ...]
>
"""
@doc type: :single
@spec pivot_longer(
df :: DataFrame.t(),
columns_to_pivot :: columns(),
opts :: Keyword.t()
) :: DataFrame.t()
def pivot_longer(df, columns_to_pivot, opts \\ [])
def pivot_longer(df, columns_to_pivot, opts) do
opts =
Keyword.validate!(opts,
select: ..,
discard: [],
names_to: "variable",
values_to: "value"
)
names_to = to_column_name(opts[:names_to])
values_to = to_column_name(opts[:values_to])
columns_to_pivot = to_existing_columns(df, columns_to_pivot)
dtypes = df.dtypes
columns_to_keep =
case opts[:select] do
keep when is_list(keep) ->
Enum.each(keep, fn column ->
if column in columns_to_pivot do
raise ArgumentError,
"selected columns must not include columns to pivot, but found #{inspect(column)} in both"
end
end)
to_existing_columns(df, keep)
keep ->
to_existing_columns(df, keep)
end
columns_to_keep =
(columns_to_keep -- columns_to_pivot) -- to_existing_columns(df, opts[:discard])
values_dtype =
dtypes
|> Map.take(columns_to_pivot)
|> Map.values()
|> Enum.uniq()
|> case do
[dtype] ->
dtype
[_ | _] = dtypes ->
raise ArgumentError,
"columns to pivot must include columns with the same dtype, but found multiple dtypes: #{Shared.inspect_dtypes(dtypes)}"
end
new_dtypes =
dtypes
|> Map.take(columns_to_keep)
|> Map.put(names_to, :string)
|> Map.put(values_to, values_dtype)
out_df = %{
df
| names: columns_to_keep ++ [names_to, values_to],
dtypes: new_dtypes,
groups: df.groups -- columns_to_pivot
}
args = [out_df, columns_to_pivot, columns_to_keep, names_to, values_to]
Shared.apply_impl(df, :pivot_longer, args)
end
@doc """
Pivot data from long to wide.
`pivot_wider/4` "widens" data, increasing the number of columns and decreasing the number of rows.
The inverse transformation is `pivot_longer/3`.
In case the dataframe is using groups, the groups that are also in the list of columns
to pivot will be removed from the resultant dataframe. See the examples below.
## Options
* `:id_columns` - A set of columns that uniquely identifies each observation.
Defaults to all columns in data except for the columns specified in `names_from` and `values_from`,
and columns that are of the `{:f, 64}` dtype.
Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated
with existing variables. May accept a filter callback, a list or a range of column names.
If an empty list is passed, or a range that results in a empty list of column names, it raises an error.
ID columns cannot be of the float type and any columns of this dtype is discarded.
If you need to use float columns as IDs, you must carefully consider rounding
or truncating the column and converting it to integer, as long as doing so
preserves the properties of the column.
* `:names_prefix` - String added to the start of every variable name.
This is particularly useful if `names_from` is a numeric vector and you want to create syntactic variable names.
## Examples
Suppose we have a basketball court and multiple teams that want to train in that court. They need
to share a schedule with the hours each team is going to use it. Here is a dataframe representing
that schedule:
iex> Explorer.DataFrame.new(
iex> weekday: ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
iex> team: ["A", "B", "C", "A", "B", "C", "A", "B", "C", "A"],
iex> hour: [10, 9, 10, 10, 11, 15, 14, 16, 14, 16]
iex> )
This dataframe is going to look like this - using `print/2`:
+----------------------------------------------+
| Explorer DataFrame: [rows: 10, columns: 3] |
+---------------+--------------+---------------+
| weekday | team | hour |
| <string> | <string> | <integer> |
+===============+==============+===============+
| Monday | A | 10 |
+---------------+--------------+---------------+
| Tuesday | B | 9 |
+---------------+--------------+---------------+
| Wednesday | C | 10 |
+---------------+--------------+---------------+
| Thursday | A | 10 |
+---------------+--------------+---------------+
| Friday | B | 11 |
+---------------+--------------+---------------+
| Monday | C | 15 |
+---------------+--------------+---------------+
| Tuesday | A | 14 |
+---------------+--------------+---------------+
| Wednesday | B | 16 |
+---------------+--------------+---------------+
| Thursday | C | 14 |
+---------------+--------------+---------------+
| Friday | A | 16 |
+---------------+--------------+---------------+
You can see that the "weekday" repeats, and it's not clear how free the agenda is.
We can solve that by pivoting the "weekday" column in multiple columns, making each weekday
a new column in the resultant dataframe.
iex> df = Explorer.DataFrame.new(
iex> weekday: ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
iex> team: ["A", "B", "C", "A", "B", "C", "A", "B", "C", "A"],
iex> hour: [10, 9, 10, 10, 11, 15, 14, 16, 14, 16]
iex> )
iex> Explorer.DataFrame.pivot_wider(df, "weekday", "hour")
#Explorer.DataFrame<
Polars[3 x 6]
team string ["A", "B", "C"]
Monday s64 [10, nil, 15]
Tuesday s64 [14, 9, nil]
Wednesday s64 [nil, 16, 10]
Thursday s64 [10, nil, 14]
Friday s64 [16, 11, nil]
>
Now if we print that same dataframe with `print/2`, we get a better picture of the schedule:
+----------------------------------------------------------------------+
| Explorer DataFrame: [rows: 3, columns: 6] |
+----------+-----------+-----------+-----------+-----------+-----------+
| team | Monday | Tuesday | Wednesday | Thursday | Friday |
| <string> | <integer> | <integer> | <integer> | <integer> | <integer> |
+==========+===========+===========+===========+===========+===========+
| A | 10 | 14 | | 10 | 16 |
+----------+-----------+-----------+-----------+-----------+-----------+
| B | | 9 | 16 | | 11 |
+----------+-----------+-----------+-----------+-----------+-----------+
| C | 15 | | 10 | 14 | |
+----------+-----------+-----------+-----------+-----------+-----------+
Pivot wider can create unpredictable column names, and sometimes they can conflict with ID columns.
In that scenario, we add a number as suffix to duplicated column names. Here is an example:
iex> df = Explorer.DataFrame.new(
iex> product_id: [1, 1, 1, 1, 2, 2, 2, 2],
iex> property: ["product_id", "width_cm", "height_cm", "length_cm", "product_id", "width_cm", "height_cm", "length_cm"],
iex> property_value: [1, 42, 40, 64, 2, 35, 20, 40]
iex> )
iex> Explorer.DataFrame.pivot_wider(df, "property", "property_value")
#Explorer.DataFrame<
Polars[2 x 5]
product_id s64 [1, 2]
product_id_1 s64 [1, 2]
width_cm s64 [42, 35]
height_cm s64 [40, 20]
length_cm s64 [64, 40]
>
But if the option `:names_prefix` is used, that suffix is not added:
iex> df = Explorer.DataFrame.new(
iex> product_id: [1, 1, 1, 1, 2, 2, 2, 2],
iex> property: ["product_id", "width_cm", "height_cm", "length_cm", "product_id", "width_cm", "height_cm", "length_cm"],
iex> property_value: [1, 42, 40, 64, 2, 35, 20, 40]
iex> )
iex> Explorer.DataFrame.pivot_wider(df, "property", "property_value", names_prefix: "col_")
#Explorer.DataFrame<
Polars[2 x 5]
product_id s64 [1, 2]
col_product_id s64 [1, 2]
col_width_cm s64 [42, 35]
col_height_cm s64 [40, 20]
col_length_cm s64 [64, 40]
>
Multiple columns are accepted for the `values_from` parameter, but the behaviour is slightly
different for the naming of new columns in the resultant dataframe. The new columns are going
to be prefixed by the name of the original value column, followed by an underscore and the
original column name, followed by the name of the variable.
iex> df = Explorer.DataFrame.new(
iex> product_id: [1, 1, 1, 1, 2, 2, 2, 2],
iex> property: ["product_id", "width_cm", "height_cm", "length_cm", "product_id", "width_cm", "height_cm", "length_cm"],
iex> property_value: [1, 42, 40, 64, 2, 35, 20, 40],
iex> another_value: [1, 43, 41, 65, 2, 36, 21, 42]
iex> )
iex> Explorer.DataFrame.pivot_wider(df, "property", ["property_value", "another_value"])
#Explorer.DataFrame<
Polars[2 x 9]
product_id s64 [1, 2]
property_value_property_product_id s64 [1, 2]
property_value_property_width_cm s64 [42, 35]
property_value_property_height_cm s64 [40, 20]
property_value_property_length_cm s64 [64, 40]
another_value_property_product_id s64 [1, 2]
another_value_property_width_cm s64 [43, 36]
another_value_property_height_cm s64 [41, 21]
another_value_property_length_cm s64 [65, 42]
>
## Grouped examples
Now using the same idea, we can see that there is not much difference for grouped dataframes.
The only detail is that groups that are not ID columns are discarded.
iex> df = Explorer.DataFrame.new(
iex> weekday: ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
iex> team: ["A", "B", "C", "A", "B", "C", "A", "B", "C", "A"],
iex> hour: [10, 9, 10, 10, 11, 15, 14, 16, 14, 16]
iex> )
iex> grouped = Explorer.DataFrame.group_by(df, "team")
iex> Explorer.DataFrame.pivot_wider(grouped, "weekday", "hour")
#Explorer.DataFrame<
Polars[3 x 6]
Groups: ["team"]
team string ["A", "B", "C"]
Monday s64 [10, nil, 15]
Tuesday s64 [14, 9, nil]
Wednesday s64 [nil, 16, 10]
Thursday s64 [10, nil, 14]
Friday s64 [16, 11, nil]
>
In the following example the group "weekday" is going to be removed, because the column is going
to be pivoted in multiple columns:
iex> df = Explorer.DataFrame.new(
iex> weekday: ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
iex> team: ["A", "B", "C", "A", "B", "C", "A", "B", "C", "A"],
iex> hour: [10, 9, 10, 10, 11, 15, 14, 16, 14, 16]
iex> )
iex> grouped = Explorer.DataFrame.group_by(df, "weekday")
iex> Explorer.DataFrame.pivot_wider(grouped, "weekday", "hour")
#Explorer.DataFrame<
Polars[3 x 6]
team string ["A", "B", "C"]
Monday s64 [10, nil, 15]
Tuesday s64 [14, 9, nil]
Wednesday s64 [nil, 16, 10]
Thursday s64 [10, nil, 14]
Friday s64 [16, 11, nil]
>
"""
@doc type: :single
@spec pivot_wider(
df :: DataFrame.t(),
names_from :: column(),
values_from :: column() | columns(),
opts ::
Keyword.t()
) :: DataFrame.t()
def pivot_wider(df, names_from, values_from, opts \\ [])
def pivot_wider(df, names_from, values_from, opts) when is_column(values_from) do
pivot_wider(df, names_from, [values_from], opts)
end
def pivot_wider(df, names_from, values_from, opts) when is_list(values_from) do
opts = Keyword.validate!(opts, id_columns: .., names_prefix: "")
[names_from | values_from] = to_existing_columns(df, [names_from | values_from])
id_columns =
for column_name <- to_existing_columns(df, opts[:id_columns]) -- [names_from | values_from],
df.dtypes[column_name] != {:f, 64},
do: column_name
if id_columns == [] do
raise ArgumentError,
"id_columns must select at least one existing column, but #{inspect(opts[:id_columns])} selects none. " <>
"Note that float columns are discarded from the selection."
end
out_df =
Shared.apply_impl(df, :pivot_wider, [
id_columns,
names_from,
values_from,
opts[:names_prefix]
])
%{out_df | groups: Enum.filter(df.groups, &(&1 in id_columns))}
end
# Two table verbs
@valid_join_types [:inner, :left, :right, :outer, :cross]
@doc """
Join two tables.
## Join types
* `:inner` - Returns all rows from `left` where there are matching values in `right`, and all columns from `left` and `right`.
* `:left` - Returns all rows from `left` and all columns from `left` and `right`. Rows in `left` with no match in `right` will have `nil` values in the new columns.
* `:right` - Returns all rows from `right` and all columns from `left` and `right`. Rows in `right` with no match in `left` will have `nil` values in the new columns.
* `:outer` - Returns all rows and all columns from both `left` and `right`. Where there are not matching values, returns `nil` for the one missing.
* `:cross` - Also known as a cartesian join. Returns all combinations of `left` and `right`. Can be very computationally expensive.
## Options
* `:on` - The column(s) to join on. Defaults to overlapping columns. Does not apply to cross join.
* `:how` - One of the join types (as an atom) described above. Defaults to `:inner`.
## Examples
Inner join:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 2], c: ["d", "e", "f"])
iex> Explorer.DataFrame.join(left, right)
#Explorer.DataFrame<
Polars[3 x 3]
a s64 [1, 2, 2]
b string ["a", "b", "b"]
c string ["d", "e", "f"]
>
Left join:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 2], c: ["d", "e", "f"])
iex> Explorer.DataFrame.join(left, right, how: :left)
#Explorer.DataFrame<
Polars[4 x 3]
a s64 [1, 2, 2, 3]
b string ["a", "b", "b", "c"]
c string ["d", "e", "f", nil]
>
Right join:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 4], c: ["d", "e", "f"])
iex> Explorer.DataFrame.join(left, right, how: :right)
#Explorer.DataFrame<
Polars[3 x 3]
a s64 [1, 2, 4]
c string ["d", "e", "f"]
b string ["a", "b", nil]
>
Outer join:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 4], c: ["d", "e", "f"])
iex> Explorer.DataFrame.join(left, right, how: :outer)
#Explorer.DataFrame<
Polars[4 x 4]
a s64 [1, 2, nil, 3]
b string ["a", "b", nil, "c"]
a_right s64 [1, 2, 4, nil]
c string ["d", "e", "f", nil]
>
Cross join:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 4], c: ["d", "e", "f"])
iex> Explorer.DataFrame.join(left, right, how: :cross)
#Explorer.DataFrame<
Polars[9 x 4]
a s64 [1, 1, 1, 2, 2, ...]
b string ["a", "a", "a", "b", "b", ...]
a_right s64 [1, 2, 4, 1, 2, ...]
c string ["d", "e", "f", "d", "e", ...]
>
Inner join with different names:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(d: [1, 2, 2], c: ["d", "e", "f"])
iex> Explorer.DataFrame.join(left, right, on: [{"a", "d"}])
#Explorer.DataFrame<
Polars[3 x 3]
a s64 [1, 2, 2]
b string ["a", "b", "b"]
c string ["d", "e", "f"]
>
## Grouped examples
When doing a join operation with grouped dataframes, the joined dataframe
may keep the groups from only one side.
An inner join operation will keep the groups from the left-hand side dataframe:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 2], c: ["d", "e", "f"])
iex> grouped_left = Explorer.DataFrame.group_by(left, "b")
iex> grouped_right = Explorer.DataFrame.group_by(right, "c")
iex> Explorer.DataFrame.join(grouped_left, grouped_right)
#Explorer.DataFrame<
Polars[3 x 3]
Groups: ["b"]
a s64 [1, 2, 2]
b string ["a", "b", "b"]
c string ["d", "e", "f"]
>
A left join operation will keep the groups from the left-hand side dataframe:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 2], c: ["d", "e", "f"])
iex> grouped_left = Explorer.DataFrame.group_by(left, "b")
iex> grouped_right = Explorer.DataFrame.group_by(right, "c")
iex> Explorer.DataFrame.join(grouped_left, grouped_right, how: :left)
#Explorer.DataFrame<
Polars[4 x 3]
Groups: ["b"]
a s64 [1, 2, 2, 3]
b string ["a", "b", "b", "c"]
c string ["d", "e", "f", nil]
>
A right join operation will keep the groups from the right-hand side dataframe:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 4], c: ["d", "e", "f"])
iex> grouped_left = Explorer.DataFrame.group_by(left, "b")
iex> grouped_right = Explorer.DataFrame.group_by(right, "c")
iex> Explorer.DataFrame.join(grouped_left, grouped_right, how: :right)
#Explorer.DataFrame<
Polars[3 x 3]
Groups: ["c"]
a s64 [1, 2, 4]
c string ["d", "e", "f"]
b string ["a", "b", nil]
>
An outer join operation is going to keep the groups from the left-hand side dataframe:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 4], c: ["d", "e", "f"])
iex> grouped_left = Explorer.DataFrame.group_by(left, "b")
iex> grouped_right = Explorer.DataFrame.group_by(right, "c")
iex> Explorer.DataFrame.join(grouped_left, grouped_right, how: :outer)
#Explorer.DataFrame<
Polars[4 x 4]
Groups: ["b"]
a s64 [1, 2, nil, 3]
b string ["a", "b", nil, "c"]
a_right s64 [1, 2, 4, nil]
c string ["d", "e", "f", nil]
>
A cross join operation is going to keep the groups from the left-hand side dataframe:
iex> left = Explorer.DataFrame.new(a: [1, 2, 3], b: ["a", "b", "c"])
iex> right = Explorer.DataFrame.new(a: [1, 2, 4], c: ["d", "e", "f"])
iex> grouped_left = Explorer.DataFrame.group_by(left, "b")
iex> grouped_right = Explorer.DataFrame.group_by(right, "c")
iex> Explorer.DataFrame.join(grouped_left, grouped_right, how: :cross)
#Explorer.DataFrame<
Polars[9 x 4]
Groups: ["b"]
a s64 [1, 1, 1, 2, 2, ...]
b string ["a", "a", "a", "b", "b", ...]
a_right s64 [1, 2, 4, 1, 2, ...]
c string ["d", "e", "f", "d", "e", ...]
>
"""
@doc type: :multi
@spec join(left :: DataFrame.t(), right :: DataFrame.t(), opts :: Keyword.t()) :: DataFrame.t()
def join(%DataFrame{} = left, %DataFrame{} = right, opts \\ []) do
left_columns = left.names
right_columns = right.names
opts =
Keyword.validate!(opts,
on: find_overlapping_columns(left_columns, right_columns),
how: :inner
)
unless opts[:how] in @valid_join_types do
raise ArgumentError,
"join type is not valid: #{inspect(opts[:how])}. " <>
"Valid options are: #{Enum.map_join(@valid_join_types, ", ", &inspect/1)}"
end
{on, how} =
case {List.wrap(opts[:on]), opts[:how]} do
{[], :cross} ->
{[], :cross}
{[], _} ->
raise(ArgumentError, "could not find any overlapping columns")
{[_ | _] = on, how} ->
normalized_on =
Enum.map(on, fn
{l_name, r_name} ->
[l_column] = to_existing_columns(left, [l_name])
[r_column] = to_existing_columns(right, [r_name])
{l_column, r_column}
name ->
[l_column] = to_existing_columns(left, [name])
[r_column] = to_existing_columns(right, [name])
# This is an edge case for when an index is passed as column selection
if l_column != r_column do
raise ArgumentError,
"the column given to option `:on` is not the same for both dataframes"
end
{l_column, r_column}
end)
{normalized_on, how}
end
out_df = out_df_for_join(how, left, right, on)
Shared.apply_impl(left, :join, [right, out_df, on, how])
end
defp find_overlapping_columns(left_columns, right_columns) do
left_columns = MapSet.new(left_columns)
right_columns = MapSet.new(right_columns)
left_columns |> MapSet.intersection(right_columns) |> MapSet.to_list()
end
defp out_df_for_join(:right, left, right, on) do
{left_on, _right_on} = Enum.unzip(on)
pairs = dtypes_pairs_for_common_join(right, left, left_on, "_left")
{new_names, _} = Enum.unzip(pairs)
%{right | names: new_names, dtypes: Map.new(pairs)}
end
defp out_df_for_join(how, left, right, on) do
{_left_on, right_on} = Enum.unzip(on)
right_on = if how in [:cross, :outer], do: [], else: right_on
pairs = dtypes_pairs_for_common_join(left, right, right_on)
{new_names, _} = Enum.unzip(pairs)
%{left | names: new_names, dtypes: Map.new(pairs)}
end
defp dtypes_pairs_for_common_join(left, right, right_on, suffix \\ "_right") do
Enum.map(left.names, fn name -> {name, left.dtypes[name]} end) ++
Enum.map(right.names -- right_on, fn right_name ->
name =
if right_name in left.names do
right_name <> suffix
else
right_name
end
{name, right.dtypes[right_name]}
end)
end
@doc """
Combine two or more dataframes column-wise.
This function expects the dataframes to have the same number of rows,
otherwise rows may be silently discarded. Eager backends may check
whenever this happens and raise instead of silently fail. But this may not
be possible for lazy dataframes as the number of rows is not known upfront.
When working with grouped dataframes, be aware that only groups from the first
dataframe are kept in the resultant dataframe.
## Examples
iex> df1 = Explorer.DataFrame.new(x: [1, 2, 3], y: ["a", "b", "c"])
iex> df2 = Explorer.DataFrame.new(z: [4, 5, 6], a: ["d", "e", "f"])
iex> Explorer.DataFrame.concat_columns([df1, df2])
#Explorer.DataFrame<
Polars[3 x 4]
x s64 [1, 2, 3]
y string ["a", "b", "c"]
z s64 [4, 5, 6]
a string ["d", "e", "f"]
>
Conflicting names are suffixed with the index of the dataframe in the array:
iex> df1 = Explorer.DataFrame.new(x: [1, 2, 3], y: ["a", "b", "c"])
iex> df2 = Explorer.DataFrame.new(x: [4, 5, 6], a: ["d", "e", "f"])
iex> Explorer.DataFrame.concat_columns([df1, df2])
#Explorer.DataFrame<
Polars[3 x 4]
x s64 [1, 2, 3]
y string ["a", "b", "c"]
x_1 s64 [4, 5, 6]
a string ["d", "e", "f"]
>
"""
@doc type: :multi
@spec concat_columns([DataFrame.t()]) :: DataFrame.t()
def concat_columns([%DataFrame{} = head | tail] = dfs) do
{names, dtypes} =
Enum.reduce(Enum.with_index(tail, 1), {head.names, head.dtypes}, fn {df, idx},
{names, dtypes} ->
new_names_and_dtypes =
for name <- df.names do
if name in names do
{name <> "_#{idx}", df.dtypes[name]}
else
{name, df.dtypes[name]}
end
end
new_names = for {name, _} <- new_names_and_dtypes, do: name
{names ++ new_names, Map.merge(dtypes, Map.new(new_names_and_dtypes))}
end)
out_df = %{head | names: names, dtypes: dtypes}
Shared.apply_impl(dfs, :concat_columns, [out_df])
end
@doc """
Combine two dataframes column-wise.
When working with grouped dataframes, be aware that only groups from the left-hand side
dataframe are kept in the resultant dataframe.
`concat_columns(df1, df2)` is equivalent to `concat_columns([df1, df2])`.
"""
@doc type: :multi
@spec concat_columns(DataFrame.t(), DataFrame.t()) :: DataFrame.t()
def concat_columns(%DataFrame{} = df1, %DataFrame{} = df2), do: concat_columns([df1, df2])
def concat_columns(%DataFrame{} = df, [%DataFrame{} | _] = dfs), do: concat_columns([df | dfs])
@doc """
Combine two or more dataframes row-wise (stack).
Column names and dtypes must match. The only exception is for numeric
columns that can be mixed together, and casted automatically to the
"highest" dtype.
For example, if a column is represented by `{:u, 16}` and `{:u, 32}`,
it will be casted to the highest unsigned integer dtype between the two,
which is `{:u, 32}`.
If it is mixing signed and unsigned integers, it will be casted to
the highest signed integer possible.
And if floats and integers are mixed together, Explorer will cast
them to the float dtype `{:f, 64}`.
When working with grouped dataframes, be aware that only groups from the first
dataframe are kept in the resultant dataframe.
## Examples
iex> df1 = Explorer.DataFrame.new(x: [1, 2, 3], y: ["a", "b", "c"])
iex> df2 = Explorer.DataFrame.new(x: [4, 5, 6], y: ["d", "e", "f"])
iex> Explorer.DataFrame.concat_rows([df1, df2])
#Explorer.DataFrame<
Polars[6 x 2]
x s64 [1, 2, 3, 4, 5, ...]
y string ["a", "b", "c", "d", "e", ...]
>
iex> df1 = Explorer.DataFrame.new(x: [1, 2, 3], y: ["a", "b", "c"])
iex> df2 = Explorer.DataFrame.new(x: [4.2, 5.3, 6.4], y: ["d", "e", "f"])
iex> Explorer.DataFrame.concat_rows([df1, df2])
#Explorer.DataFrame<
Polars[6 x 2]
x f64 [1.0, 2.0, 3.0, 4.2, 5.3, ...]
y string ["a", "b", "c", "d", "e", ...]
>
"""
@doc type: :multi
@spec concat_rows([DataFrame.t()]) :: DataFrame.t()
def concat_rows([%DataFrame{} = head | _tail] = dfs) do
changed_types = compute_changed_types_concat_rows(dfs)
out_df = %{head | dtypes: Map.merge(head.dtypes, changed_types)}
dfs =
if changed_types == %{} do
dfs
else
for df <- dfs do
mutate_with(ungroup(df), fn ldf ->
for {column, target_type} <- changed_types,
do: {column, Series.cast(ldf[column], target_type)}
end)
end
end
Shared.apply_impl(dfs, :concat_rows, [out_df])
end
defp compute_changed_types_concat_rows([head | tail]) do
types = head.dtypes
Enum.reduce(tail, %{}, fn df, changed_types ->
if n_columns(df) != map_size(types) do
raise ArgumentError,
"dataframes must have the same columns"
end
Enum.reduce(df.dtypes, changed_types, fn {name, type}, changed_types ->
case types do
%{^name => current_type} ->
if dtype = Shared.merge_dtype(Map.get(changed_types, name, current_type), type) do
Map.put(changed_types, name, dtype)
else
raise ArgumentError,
"columns and dtypes must be identical for all dataframes"
end
%{} ->
raise ArgumentError, "dataframes must have the same columns"
end
end)
end)
end
@doc """
Combine two dataframes row-wise.
`concat_rows(df1, df2)` is equivalent to `concat_rows([df1, df2])`.
When working with grouped dataframes, be aware that only groups from the left-hand side
dataframe are kept in the resultant dataframe.
"""
@doc type: :multi
@spec concat_rows(DataFrame.t(), DataFrame.t()) :: DataFrame.t()
def concat_rows(%DataFrame{} = df1, %DataFrame{} = df2), do: concat_rows([df1, df2])
def concat_rows(%DataFrame{} = df, [%DataFrame{} | _] = dfs), do: concat_rows([df | dfs])
# Groups
@doc """
Group the dataframe by one or more variables.
When the dataframe has grouping variables, operations are performed per group.
`Explorer.DataFrame.ungroup/2` removes grouping.
## Examples
You can group by a single variable:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.group_by(df, "country")
#Explorer.DataFrame<
Polars[1094 x 10]
Groups: ["country"]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Or you can group by multiple columns in a given list:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.group_by(df, ["country", "year"])
#Explorer.DataFrame<
Polars[1094 x 10]
Groups: ["country", "year"]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Or by a range:
iex> df = Explorer.Datasets.fossil_fuels()
iex> Explorer.DataFrame.group_by(df, 0..1)
#Explorer.DataFrame<
Polars[1094 x 10]
Groups: ["year", "country"]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Regexes and functions are also accepted in column names, as in `select/2`.
"""
@doc type: :single
@spec group_by(df :: DataFrame.t(), groups_or_group :: column_names() | column_name()) ::
DataFrame.t()
def group_by(df, group) when is_column(group), do: group_by(df, [group])
def group_by(df, groups) do
groups = to_existing_columns(df, groups)
all_groups = Enum.uniq(df.groups ++ groups)
%{df | groups: all_groups}
end
@doc """
Removes grouping variables.
Accepts a list of group names. If groups is not specified, then all groups are
removed.
## Examples
Ungroups all by default:
iex> df = Explorer.Datasets.fossil_fuels()
iex> df = Explorer.DataFrame.group_by(df, ["country", "year"])
iex> Explorer.DataFrame.ungroup(df)
#Explorer.DataFrame<
Polars[1094 x 10]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Ungrouping a single column:
iex> df = Explorer.Datasets.fossil_fuels()
iex> df = Explorer.DataFrame.group_by(df, ["country", "year"])
iex> Explorer.DataFrame.ungroup(df, "country")
#Explorer.DataFrame<
Polars[1094 x 10]
Groups: ["year"]
year s64 [2010, 2010, 2010, 2010, 2010, ...]
country string ["AFGHANISTAN", "ALBANIA", "ALGERIA", "ANDORRA", "ANGOLA", ...]
total s64 [2308, 1254, 32500, 141, 7924, ...]
solid_fuel s64 [627, 117, 332, 0, 0, ...]
liquid_fuel s64 [1601, 953, 12381, 141, 3649, ...]
gas_fuel s64 [74, 7, 14565, 0, 374, ...]
cement s64 [5, 177, 2598, 0, 204, ...]
gas_flaring s64 [0, 0, 2623, 0, 3697, ...]
per_capita f64 [0.08, 0.43, 0.9, 1.68, 0.37, ...]
bunker_fuels s64 [9, 7, 663, 0, 321, ...]
>
Lists, ranges, regexes, and functions are also accepted in column names, as in `select/2`.
"""
@doc type: :single
@spec ungroup(df :: DataFrame.t(), groups_or_group :: column_names() | column_name() | :all) ::
DataFrame.t()
def ungroup(df, groups \\ ..)
def ungroup(df, ..), do: %{df | groups: []}
def ungroup(df, group) when is_column(group), do: ungroup(df, [group])
def ungroup(df, groups) do
current_groups = groups(df)
groups = to_existing_columns(df, groups)
Enum.each(groups, fn group ->
if group not in current_groups do
raise ArgumentError,
"could not find #{group} in current groups (#{current_groups})"
end
end)
%{df | groups: current_groups -- groups}
end
@doc """
Summarise each group to a single row using `Explorer.Query`.
To summarise, you must either perform an aggregation (defined in `Explorer.Series`)
on the desired columns, or select plain columns to retrieve all elements of a group.
The query is compiled and runs efficiently against the dataframe. This function
performs aggregations based on groups and it implicitly ungroups the resultant dataframe.
> #### Notice {: .notice}
>
> This is a macro. You must `require Explorer.DataFrame` before using it.
See `summarise_with/2` for a callback version of this function without
`Explorer.Query`.
## Examples
iex> df = Explorer.DataFrame.new(letters: ~w(a b c d e), is_vowel: [true, false, false, false, true])
iex> grouped_df = Explorer.DataFrame.group_by(df, :is_vowel)
iex> Explorer.DataFrame.summarise(grouped_df, letters: letters)
#Explorer.DataFrame<
Polars[2 x 2]
is_vowel boolean [true, false]
letters list[string] [["a", "e"], ["b", "c", "d"]]
>
iex> df = Explorer.Datasets.fossil_fuels()
iex> grouped_df = Explorer.DataFrame.group_by(df, "year")
iex> Explorer.DataFrame.summarise(grouped_df, total_max: max(total), total_min: min(total))
#Explorer.DataFrame<
Polars[5 x 3]
year s64 [2010, 2011, 2012, 2013, 2014]
total_max s64 [2393248, 2654360, 2734817, 2797384, 2806634]
total_min s64 [1, 2, 2, 2, 3]
>
Suppose you want to get the mean sepal width of each Iris species. You could do something
like this:
iex> df = Explorer.Datasets.iris()
iex> grouped_df = Explorer.DataFrame.group_by(df, "species")
iex> Explorer.DataFrame.summarise(grouped_df, mean_sepal_width: round(mean(sepal_width), 3))
#Explorer.DataFrame<
Polars[3 x 2]
species string ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
mean_sepal_width f64 [3.418, 2.77, 2.974]
>
In case aggregations for all the dataframe is what you want, you can use ungrouped
dataframes:
iex> df = Explorer.Datasets.iris()
iex> Explorer.DataFrame.summarise(df, mean_petal_length: mean(petal_length))
#Explorer.DataFrame<
Polars[1 x 1]
mean_petal_length f64 [3.758666666666666]
>
"""
@doc type: :single
defmacro summarise(df, query) do
quote do
require Explorer.Query
Explorer.DataFrame.summarise_with(unquote(df), Explorer.Query.query(unquote(query)))
end
end
@doc """
Summarise each group to a single row using a callback function.
In case no group is set, the entire dataframe will be considered.
The callback receives a lazy dataframe. A lazy dataframe does not
hold any values, instead it stores all operations in order to
execute all summarizations performantly.
This is a callback version of `summarise/2`.
## Examples
iex> alias Explorer.{DataFrame, Series}
iex> df = Explorer.Datasets.fossil_fuels() |> DataFrame.group_by("year")
iex> DataFrame.summarise_with(df, &[total_max: Series.max(&1["total"]), countries: Series.n_distinct(&1["country"])])
#Explorer.DataFrame<
Polars[5 x 3]
year s64 [2010, 2011, 2012, 2013, 2014]
total_max s64 [2393248, 2654360, 2734817, 2797384, 2806634]
countries u32 [217, 217, 220, 220, 220]
>
iex> alias Explorer.{DataFrame, Series}
iex> df = Explorer.Datasets.fossil_fuels()
iex> DataFrame.summarise_with(df, &[total_max: Series.max(&1["total"]), countries: Series.n_distinct(&1["country"])])
#Explorer.DataFrame<
Polars[1 x 2]
total_max s64 [2806634]
countries u32 [222]
>
"""
@doc type: :single
@spec summarise_with(
df :: DataFrame.t(),
callback :: (Explorer.Backend.LazyFrame.t() -> column_pairs(Series.lazy_t()))
) :: DataFrame.t()
def summarise_with(%DataFrame{} = df, fun) when is_function(fun, 1) do
ldf = Explorer.Backend.LazyFrame.new(df)
result = fun.(ldf)
result =
Enum.map(result, fn
{key, nil} ->
lazy_s = LazySeries.new(:lazy, [nil], :null)
{key, Explorer.Backend.Series.new(lazy_s, :null)}
pair ->
pair
end)
column_pairs =
to_column_pairs(df, result, fn value ->
case value do
%Series{data: %LazySeries{op: :lazy, args: [nil], dtype: :null}} ->
value
%Series{data: %LazySeries{aggregation: true}} ->
value
%Series{data: %LazySeries{op: :column}} ->
%{value | dtype: {:list, value.dtype}}
%Series{data: %LazySeries{}} = series ->
raise "expecting summarise with an aggregation operation or plain column, " <>
"but none of which were found in: #{inspect(series)}"
other ->
raise "expecting a lazy series, got: #{inspect(other)}"
end
end)
new_dtypes = names_with_dtypes_for_column_pairs(df, column_pairs)
new_names = for {name, _} <- new_dtypes, do: name
df_out = %{df | names: new_names, dtypes: Map.new(new_dtypes), groups: []}
column_pairs = for {name, %Series{data: lazy_series}} <- column_pairs, do: {name, lazy_series}
Shared.apply_impl(df, :summarise_with, [df_out, column_pairs])
end
defp names_with_dtypes_for_column_pairs(df, column_pairs) do
groups = for group <- df.groups, do: {group, df.dtypes[group]}
names_with_dtypes =
for {column_name, series} <- column_pairs do
{column_name, series.dtype}
end
groups ++ names_with_dtypes
end
@doc """
Explodes one or multiple list column into multiple rows.
When exploding multiple columns, the number of list elements in each row for
the exploded columns must be the same.
## Examples
iex> df = Explorer.DataFrame.new(a: [[1, 2], [3, 4]], b: [[5, 6], [7, 8]], c: ["a", "b"])
iex> Explorer.DataFrame.explode(df, :a)
#Explorer.DataFrame<
Polars[4 x 3]
a s64 [1, 2, 3, 4]
b list[s64] [[5, 6], [5, 6], [7, 8], [7, ...]]
c string ["a", "a", "b", "b"]
>
iex> Explorer.DataFrame.explode(df, [:a, :b])
#Explorer.DataFrame<
Polars[4 x 3]
a s64 [1, 2, 3, 4]
b s64 [5, 6, 7, 8]
c string ["a", "a", "b", "b"]
>
You can think of exploding multiple list columns as being the inverse of
aggregating the elements the exploded columns into lists:
iex> df = Explorer.DataFrame.new(a: [1, 2, 3, 4], b: [5, 6, 7, 8], c: ["a", "a", "b", "b"])
iex> df = df |> Explorer.DataFrame.group_by(:c) |> Explorer.DataFrame.summarise(a: a, b: b)
#Explorer.DataFrame<
Polars[2 x 3]
c string ["a", "b"]
a list[s64] [[1, 2], [3, 4]]
b list[s64] [[5, 6], [7, 8]]
>
iex> Explorer.DataFrame.explode(df, [:a, :b]) # we are back where we started
#Explorer.DataFrame<
Polars[4 x 3]
c string ["a", "a", "b", "b"]
a s64 [1, 2, 3, 4]
b s64 [5, 6, 7, 8]
>
If you want to perform the cartesian product of two list columns, you must
call `explode/2` once for each column:
iex> df = Explorer.DataFrame.new(a: [[1, 2], [3, 4]], b: [[5, 6], [7, 8]], c: ["a", "b"])
iex> df |> Explorer.DataFrame.explode(:a) |> Explorer.DataFrame.explode(:b)
#Explorer.DataFrame<
Polars[8 x 3]
a s64 [1, 1, 2, 2, 3, ...]
b s64 [5, 6, 5, 6, 7, ...]
c string ["a", "a", "a", "a", "b", ...]
>
"""
@doc type: :single
@spec explode(df :: DataFrame.t(), column :: column_name() | [column_name()]) :: DataFrame.t()
def explode(%DataFrame{} = df, column_or_columns) do
columns = to_existing_columns(df, List.wrap(column_or_columns))
dtypes = Enum.map(columns, &Map.fetch!(df.dtypes, &1))
unless Enum.all?(dtypes, &match?({:list, _}, &1)) do
raise ArgumentError,
"explode/2 expects list columns, but the given columns have the types: #{Shared.inspect_dtypes(dtypes)}"
end
out_dtypes =
Enum.reduce(columns, df.dtypes, fn column, dtypes ->
Map.update!(dtypes, column, fn {:list, inner_dtype} -> inner_dtype end)
end)
out_df = %{df | dtypes: out_dtypes}
Shared.apply_impl(df, :explode, [out_df, columns])
end
@doc """
Unnests one or multiple struct columns into individual columns.
The field names in a unnested column must not exist in the dataframe
or in any other unnested columns.
## Examples
iex> df = Explorer.DataFrame.new(before: [1, 2], struct: [%{x: 1, y: 2}, %{x: 3, y: 4}], after: [3, 4])
iex> Explorer.DataFrame.unnest(df, :struct)
#Explorer.DataFrame<
Polars[2 x 4]
before s64 [1, 2]
x s64 [1, 3]
y s64 [2, 4]
after s64 [3, 4]
>
iex> df = Explorer.DataFrame.new(struct1: [%{x: 1, y: 2}, %{x: 3, y: 4}], struct2: [%{z: 5}, %{z: 6}])
iex> Explorer.DataFrame.unnest(df, [:struct1, :struct2])
#Explorer.DataFrame<
Polars[2 x 3]
x s64 [1, 3]
y s64 [2, 4]
z s64 [5, 6]
>
"""
@doc type: :single
@spec unnest(df :: DataFrame.t(), column_or_columns :: column_name() | [column_name()]) ::
DataFrame.t()
def unnest(%DataFrame{} = df, column_or_columns) do
columns = to_existing_columns(df, List.wrap(column_or_columns))
dtypes = Enum.map(columns, &Map.fetch!(df.dtypes, &1))
unless Enum.all?(dtypes, &match?({:struct, _}, &1)) do
raise ArgumentError,
"unnest/2 expects struct columns, but the given columns have the types: #{Shared.inspect_dtypes(dtypes)}"
end
{new_dtypes, new_names} =
columns
|> Enum.zip(dtypes)
|> Enum.reduce({%{}, %{}}, fn {column, {:struct, inner_dtypes}}, {new_dtypes, new_names} ->
new_dtypes = Map.merge(new_dtypes, Map.new(inner_dtypes))
new_names = Map.put(new_names, column, Enum.map(inner_dtypes, &elem(&1, 0)))
{new_dtypes, new_names}
end)
out_dtypes =
df.dtypes
|> Map.drop(columns)
|> Map.merge(new_dtypes)
out_names =
Enum.flat_map(df.names, fn name ->
case Map.fetch(new_names, name) do
{:ok, names} -> names
:error -> [name]
end
end)
out_df = %{df | dtypes: out_dtypes, names: out_names}
Shared.apply_impl(df, :unnest, [out_df, columns])
end
@doc """
Prints the DataFrame in a tabular fashion.
## Examples
df = Explorer.Datasets.iris()
Explorer.DataFrame.print(df)
Explorer.DataFrame.print(df, limit: 1)
Explorer.DataFrame.print(df, limit: :infinity)
"""
@doc type: :introspection
@spec print(df :: DataFrame.t(), opts :: Keyword.t()) :: :ok
def print(df, opts \\ []) do
{rows, columns} =
if lazy?(df) do
{"???", n_columns(df)}
else
shape(df)
end
headers = df.names
df =
case opts[:limit] do
:infinity -> df
nrow when is_integer(nrow) and nrow >= 0 -> slice(df, 0, nrow)
_ -> slice(df, 0, @default_sample_nrows)
end
|> collect()
types = Enum.map(df.names, &"\n<#{Shared.dtype_to_string(df.dtypes[&1])}>")
values =
headers
|> Enum.map(fn n ->
s = df[n]
list = Series.to_list(s)
case s.dtype do
{:struct, _} -> Enum.map(list, &inspect/1)
_ -> list
end
end)
|> Enum.zip_with(& &1)
name_type = Enum.zip_with(headers, types, fn x, y -> x <> y end)
TableRex.Table.new()
|> TableRex.Table.put_title("Explorer DataFrame: [rows: #{rows}, columns: #{columns}]")
|> TableRex.Table.put_header(name_type)
|> TableRex.Table.put_header_meta(0..columns, align: :center)
|> TableRex.Table.add_rows(values)
|> TableRex.Table.render!(
header_separator_symbol: "=",
horizontal_style: :all
)
|> IO.puts()
end
@doc """
Describe numeric columns of a DataFrame.
Groups are ignored if the dataframe is using any.
## Options
* `:percentiles` - Floating point list with the percentiles to be calculated. (default: `[0.25, 0.5, 0.75]`)
## Examples
iex> df = Explorer.DataFrame.new(a: ["d", nil, "f"], b: [1, 2, 3], c: ["a", "b", "c"])
iex> Explorer.DataFrame.describe(df)
#Explorer.DataFrame<
Polars[9 x 4]
describe string ["count", "nil_count", "mean", "std", "min", ...]
a string ["2", "1", nil, nil, nil, ...]
b f64 [3.0, 0.0, 2.0, 1.0, 1.0, ...]
c string ["3", "0", nil, nil, nil, ...]
>
iex> df = Explorer.DataFrame.new(a: ["d", nil, "f"], b: [1, 2, 3], c: ["a", "b", "c"])
iex> Explorer.DataFrame.describe(df, percentiles: [0.3, 0.5, 0.8])
#Explorer.DataFrame<
Polars[9 x 4]
describe string ["count", "nil_count", "mean", "std", "min", ...]
a string ["2", "1", nil, nil, nil, ...]
b f64 [3.0, 0.0, 2.0, 1.0, 1.0, ...]
c string ["3", "0", nil, nil, nil, ...]
>
"""
@doc type: :single
@spec describe(df :: DataFrame.t(), Keyword.t()) :: DataFrame.t()
def describe(df, opts \\ []) do
opts = Keyword.validate!(opts, percentiles: nil)
if Enum.empty?(df.names) do
raise ArgumentError, "cannot describe a DataFrame without any columns"
end
stat_cols = df.names
percentiles = process_percentiles(opts[:percentiles])
numeric_types = Shared.numeric_types()
datetime_types = Shared.datetime_types()
duration_types = Shared.duration_types()
metrics_df =
summarise_with(df, fn x ->
Enum.flat_map(stat_cols, fn c ->
dt = x[c].dtype
numeric? = dt in numeric_types
min_max? = numeric? or dt in datetime_types or dt in duration_types
[
{"count:#{c}", Series.count(x[c])},
{"nil_count:#{c}", Series.nil_count(x[c])},
{"mean:#{c}", if(numeric?, do: Series.mean(x[c]))},
{"std:#{c}", if(numeric?, do: Series.standard_deviation(x[c]))},
{"min:#{c}", if(min_max?, do: Series.min(x[c]))}
] ++
for p <- percentiles do
{"#{trunc(p * 100)}%:#{c}", if(numeric?, do: Series.quantile(x[c], p))}
end ++
[{"max:#{c}", if(min_max?, do: Series.max(x[c]))}]
end)
end)
[metrics_row] = metrics_df |> collect() |> to_rows()
metrics =
["count", "nil_count", "mean", "std", "min"] ++
for(p <- percentiles, do: "#{trunc(p * 100)}%") ++ ["max"]
data =
metrics_df.names
|> Enum.chunk_every(length(metrics))
|> Enum.zip_with(stat_cols, fn metrics, col ->
if df.dtypes[col] in numeric_types do
{col, Enum.map(metrics, &metrics_row[&1])}
else
{col, Enum.map(metrics, &(metrics_row[&1] && "#{metrics_row[&1]}"))}
end
end)
new([{"describe", metrics} | data])
end
defp process_percentiles(nil), do: [0.25, 0.50, 0.75]
defp process_percentiles(percentiles) do
Enum.each(percentiles, fn p ->
if p < 0 or p > 1 do
raise ArgumentError, message: "percentiles must all be in the range [0, 1]"
end
end)
Enum.sort(percentiles)
end
@doc """
Counts the number of `nil` elements in each column.
## Examples
iex> df = Explorer.DataFrame.new(a: ["d", nil, "f"], b: [nil, 2, nil], c: ["a", "b", "c"])
iex> Explorer.DataFrame.nil_count(df)
#Explorer.DataFrame<
Polars[1 x 3]
a u32 [1]
b u32 [2]
c u32 [0]
>
"""
@doc type: :single
@spec nil_count(df :: DataFrame.t()) :: DataFrame.t()
def nil_count(df), do: Shared.apply_impl(df, :nil_count)
@doc """
Creates a new dataframe with unique rows and the frequencies of each.
## Examples
iex> df = Explorer.DataFrame.new(a: ["a", "a", "b"], b: [1, 1, nil])
iex> Explorer.DataFrame.frequencies(df, [:a, :b])
#Explorer.DataFrame<
Polars[2 x 3]
a string ["a", "b"]
b s64 [1, nil]
counts u32 [2, 1]
>
"""
@doc type: :single
@spec frequencies(df :: DataFrame.t(), columns :: column_names()) :: DataFrame.t()
def frequencies(%DataFrame{} = df, [col | _] = columns) do
df
|> group_by(columns)
|> summarise_with(&[counts: Series.count(&1[col])])
|> sort_with(&[desc: &1[:counts]])
end
def frequencies(_df, []), do: raise(ArgumentError, "columns cannot be empty")
@doc """
Calculates the pairwise correlation of numeric columns.
The returned dataframe is the correlation matrix.
## Supported dtypes
Only columns with the following dtypes are taken into account.
* floats: #{Shared.inspect_dtypes(Shared.float_types(), backsticks: true)}
* integers: #{Shared.inspect_dtypes(Shared.integer_types(), backsticks: true)}
The resultant columns are always `{:f, 64}`.
## Options
* `:columns` - the selection of columns to calculate. Defaults to all numeric columns.
* `:column_name` - the name of the column with column names. Defaults to "names".
* `:ddof` - the 'delta degrees of freedom' - the divisor used in the correlation
calculation. Defaults to 1.
* `:method` refers to the correlation method. The following methods are available:
- `:pearson` : Standard correlation coefficient. (default)
- `:spearman` : Spearman rank correlation.
## Examples
iex> df = Explorer.DataFrame.new(dogs: [1, 8, 3], cats: [4, 5, 2])
iex> Explorer.DataFrame.correlation(df)
#Explorer.DataFrame<
Polars[2 x 3]
names string ["dogs", "cats"]
dogs f64 [1.0000000000000002, 0.5447047794019219]
cats f64 [0.5447047794019219, 1.0]
>
"""
@doc type: :single
@spec correlation(df :: DataFrame.t(), opts :: Keyword.t()) :: df :: DataFrame.t()
def correlation(df, opts \\ []) do
opts =
Keyword.validate!(opts,
column_name: "names",
columns: names(df),
ddof: 1,
method: :pearson
)
out_df = pairwised_df(df, opts)
Shared.apply_impl(df, :correlation, [out_df, opts[:ddof], opts[:method]])
end
@doc """
Calculates the pairwise covariance of numeric columns.
The returned dataframe is the covariance matrix.
## Supported dtypes
Only columns with the following dtypes are taken into account.
* floats: #{Shared.inspect_dtypes(Shared.float_types(), backsticks: true)}
* integers: #{Shared.inspect_dtypes(Shared.integer_types(), backsticks: true)}
The resultant columns are always `{:f, 64}`.
## Options
* `:columns` - the selection of columns to calculate. Defaults to all numeric columns.
* `:column_name` - the name of the column with column names. Defaults to "names".
* `:ddof` - the 'delta degrees of freedom' - the divisor used in the covariance
calculation. Defaults to 1.
## Examples
iex> df = Explorer.DataFrame.new(dogs: [1, 0, 2, 1], cats: [2, 3, 0, 1])
iex> Explorer.DataFrame.covariance(df)
#Explorer.DataFrame<
Polars[2 x 3]
names string ["dogs", "cats"]
dogs f64 [0.6666666666666666, -1.0]
cats f64 [-1.0, 1.6666666666666667]
>
"""
@doc type: :single
@spec covariance(df :: DataFrame.t(), opts :: Keyword.t()) :: df :: DataFrame.t()
def covariance(df, opts \\ []) do
opts = Keyword.validate!(opts, column_name: "names", columns: names(df), ddof: 1)
out_df = pairwised_df(df, opts)
Shared.apply_impl(df, :covariance, [out_df, opts[:ddof]])
end
# Helpers
defp backend_from_options!(opts) do
backend = Explorer.Shared.backend_from_options!(opts) || Explorer.Backend.get()
if opts[:lazy] do
:"#{backend}.LazyFrame"
else
:"#{backend}.DataFrame"
end
end
defp numeric_column?(df, name) do
Series.dtype(df[name]) in Explorer.Shared.numeric_types()
end
defp pairwised_df(df, opts) do
column_name = to_column_name(opts[:column_name])
cols =
df
|> to_existing_columns(opts[:columns])
|> Enum.filter(fn name -> numeric_column?(df, name) end)
out_dtypes = for col <- cols, into: %{column_name => :string}, do: {col, {:f, 64}}
%{df | dtypes: out_dtypes, names: [column_name | cols]}
end
defimpl Inspect do
import Inspect.Algebra
def inspect(df, opts) do
force_unfit(
concat([
color("#Explorer.DataFrame<", :map, opts),
nest(
concat([line(), Shared.apply_impl(df, :inspect, [opts])]),
2
),
line(),
color(">", :map, opts)
])
)
end
end
end
defimpl Table.Reader, for: Explorer.DataFrame do
def init(df) do
df = Explorer.DataFrame.collect(df)
columns = Explorer.DataFrame.names(df)
data =
Enum.map(columns, fn column ->
df
|> Explorer.DataFrame.pull(column)
|> Explorer.Series.to_enum()
end)
{:columns, %{columns: columns, count: Explorer.DataFrame.n_rows(df)}, data}
end
end
