ExPolars.DataFrame (ExPolars v0.2.1-dev) View Source
Link to this section Summary
Functions
Drop duplicate rows from this DataFrame.
Note that this fails if there is a column of type List
in the DataFrame.
Check if DataFrame is equal to other.
Explode DataFrame
to long format by exploding a column with Lists.
Use multiple aggregations on columns
Count the unique values per group.
Get a mask of all duplicated rows in this DataFrame
Get a mask of all unique rows in this DataFrame
Unpivot DataFrame to long format.
Do a pivot operation based on the group key, a pivot column and an aggregation function on the values column.
Shift the values by a given period and fill the parts that will be empty due to this operation
with Nones
.
Link to this section Types
Specs
s() :: ExPolars.Series
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t() :: ExPolars.DataFrame
Link to this section Functions
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Drop duplicate rows from this DataFrame.
Note that this fails if there is a column of type List
in the DataFrame.
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Check if DataFrame is equal to other.
Parameters
df: DataFrame other: DataFrame to compare with. null_equal: Consider null values as equal.
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Explode DataFrame
to long format by exploding a column with Lists.
Parameters
columns
Column of LargeList type
Returns
DataFrame
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groupby_agg( t() | {:ok, t()}, [String.t()], %{required(String.t()) => [String.t()]} | [{String.t(), [String.t()]}] ) :: {:ok, t()} | {:error, term()}
Use multiple aggregations on columns
Parameters
column_to_agg
map column to aggregation functions
Examples:
[{"foo", ["sum", "n_unique", "min"]},
{"bar": ["max"]}]
{"foo": ["sum", "n_unique", "min"],
"bar": "max"}
Returns
Result of groupby split apply operations.
Specs
groupby_quantile(t() | {:ok, t()}, [String.t()], [String.t()], float()) :: {:ok, t()} | {:error, term()}
Count the unique values per group.
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Get a mask of all duplicated rows in this DataFrame
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Get a mask of all unique rows in this DataFrame
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Unpivot DataFrame to long format.
Parameters
id_vars
Columns to use as identifier variables
value_vars
Values to use as identifier variables
Returns
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pivot(t() | {:ok, t()}, [String.t()], String.t(), String.t(), String.t()) :: {:ok, t()} | {:error, term()}
Do a pivot operation based on the group key, a pivot column and an aggregation function on the values column.
Parameters
pivot_column
Column to pivot.
values_column
Column that will be aggregated
Specs
read_csv(filename, infer_schema_length \\ 100, batch_size \\ 64, has_header \\ true, ignore_errors \\ false, stop_after_n_rows \\ nil, skip_rows \\ 0, projection \\ nil, sep \\ ",", rechunk \\ true, columns \\ nil, encoding \\ "utf8")
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Shift the values by a given period and fill the parts that will be empty due to this operation
with Nones
.
Parameters
periods
Number of places to shift (may be negative).