Need some help with a map function.
I want to take a DataType::Date and store the corresponding weekday as a string column.
I have it working starting with string -> date-type -> string (case #1).
What I am looking for is date-type -> string (case#2).
Here is the working code for the first case ... any suggestions on how to get this to work for my second case?
My challenge with this stems from my lack of proper understanding of how map is supposed to work in this instance.
use chrono::{Date, Datelike, NaiveDate, Utc};
use polars::prelude::*;
fn main() {
let days = df!("column_1" => &["Tuesday"],
"column_2" => &["1900-01-02"]);
let options = StrpTimeOptions {
date_dtype: DataType::Date,
fmt: Some("%Y-%m-%d".into()),
strict: false,
exact: true,
};
// convert column_2-string into dtype(date) and put into new column "date"
let days = days
.unwrap()
.lazy()
.with_column(col("column_2").alias("date").str().strptime(options));
let o = GetOutput::from_type(DataType::Utf8);
fn str_to_weekday(str_val: Series) -> Result<Series> {
let x = str_val
.utf8()
.unwrap()
.into_iter()
// your actual custom function would be in this map
.map(|opt_date: Option<&str>| {
opt_date.map(|date: &str| {
// for DEBUG purpose only:
println! {"Date-String: {:?}", date};
NaiveDate::parse_from_str(date, "%Y-%m-%d")
.unwrap()
.format("%A")
.to_string()
})
})
.collect::<Utf8Chunked>();
Ok(x.into_series())
}
// column_2 to weekday-string ... into new column "weekday"
let days = days
.with_column(col("column_2").alias("weekday").apply(str_to_weekday, o))
.collect()
.unwrap()
.lazy();
println!("{:?}", days.clone().collect());
}
Got it to work :)
With a simplified approach ...
use polars::prelude::*;
fn main() {
let days = df!("column_1" => &["Tuesday"],
"column_2" => &["1900-01-02"]);
let options = StrpTimeOptions {
date_dtype: DataType::Date,
fmt: Some("%Y-%m-%d".into()),
strict: false,
exact: true,
};
// convert column_2-string into dtype(date) and put into new column "date"
let days = days
.unwrap()
.lazy()
.with_column(col("column_2").alias("date").str().strptime(options));
println!("{:?}", days.clone().collect());
let o = GetOutput::from_type(DataType::Utf8);
let days = days.with_column(
col("date")
.alias("weekday")
.map(|x| Ok(x.strftime("%A").unwrap()), o),
);
println!("{:?}", days.collect());
}
Related
I have a row of a polars dataframe created using iterators reading a parquet file from this method: Iterate over rows polars rust
I have constructed a HashMap that represents an individual row and I would like to now convert that row into JSON.
This is what my code looks like so far:
use polars::prelude::*;
use std::iter::zip;
use std::{fs::File, collections::HashMap};
fn main() -> anyhow::Result<()> {
let file = File::open("0.parquet").unwrap();
let mut df = ParquetReader::new(file).finish()?;
dbg!(df.schema());
let fields = df.fields();
let columns: Vec<&String> = fields.iter().map(|x| x.name()).collect();
df.as_single_chunk_par();
let mut iters = df.iter().map(|s| s.iter()).collect::<Vec<_>>();
for _ in 0..df.height() {
let mut row = HashMap::new();
for (column, iter) in zip(&columns, &mut iters) {
let value = iter.next().expect("should have as many iterations as rows");
row.insert(column, value);
}
dbg!(&row);
let json = serde_json::to_string(&row).unwrap();
dbg!(json);
break;
}
Ok(())
}
And I have the following feature flags enabled: ["parquet", "serde", "dtype-u8", "dtype-i8", "dtype-date", "dtype-datetime"].
I am running into the following error at the serde_json::to_string(&row).unwrap() line:
thread 'main' panicked at 'called `Result::unwrap()` on an `Err` value: Error("the enum variant AnyValue::Datetime cannot be serialized", line: 0, column: 0)', src/main.rs:47:48
I am also unable to implement my own serialized for AnyValue::DateTime because of only traits defined in the current crate can be implemented for types defined outside of the crate.
What's the best way to serialize this row into JSON?
I was able to resolve this error by using a match statement over value to change it from a Datetime to an Int64.
let value = match value {
AnyValue::Datetime(value, TimeUnit::Milliseconds, _) => AnyValue::Int64(value),
x => x
};
row.insert(column, value);
Root cause is there is no enum variant for Datetime in the impl Serialize block: https://docs.rs/polars-core/0.24.0/src/polars_core/datatypes/mod.rs.html#298
Although this code now works, it outputs data that looks like:
{'myintcolumn': {'Int64': 22342342343},
'mylistoclumn': {'List': {'datatype': 'Int32', 'name': '', 'values': []}},
'mystrcolumn': {'Utf8': 'lorem ipsum lorem ipsum'}
So you likely to be customizing the serialization here regardless of the data type.
Update: If you want to get the JSON without all of the inner nesting, I had to do a gnarly match statement:
use polars::prelude::*;
use std::iter::zip;
use std::{fs::File, collections::HashMap};
use serde_json::json;
fn main() -> anyhow::Result<()> {
let file = File::open("0.parquet").unwrap();
let mut df = ParquetReader::new(file).finish()?;
dbg!(df.schema());
let fields = df.fields();
let columns: Vec<&String> = fields.iter().map(|x| x.name()).collect();
df.as_single_chunk_par();
let mut iters = df.iter().map(|s| s.iter()).collect::<Vec<_>>();
for _ in 0..df.height() {
let mut row = HashMap::new();
for (column, iter) in zip(&columns, &mut iters) {
let value = iter.next().expect("should have as many iterations as rows");
let value = match value {
AnyValue::Null => json!(Option::<String>::None),
AnyValue::Int64(val) => json!(val),
AnyValue::Int32(val) => json!(val),
AnyValue::Int8(val) => json!(val),
AnyValue::Float32(val) => json!(val),
AnyValue::Float64(val) => json!(val),
AnyValue::Utf8(val) => json!(val),
AnyValue::List(val) => {
match val.dtype() {
DataType::Int32 => ({let vec: Vec<Option<_>> = val.i32().unwrap().into_iter().collect(); json!(vec)}),
DataType::Float32 => ({let vec: Vec<Option<_>> = val.f32().unwrap().into_iter().collect(); json!(vec)}),
DataType::Utf8 => ({let vec: Vec<Option<_>> = val.utf8().unwrap().into_iter().collect(); json!(vec)}),
DataType::UInt8 => ({let vec: Vec<Option<_>> = val.u8().unwrap().into_iter().collect(); json!(vec)}),
x => panic!("unable to parse list column: {} with value: {} and type: {:?}", column, x, x.inner_dtype())
}
},
AnyValue::Datetime(val, TimeUnit::Milliseconds, _) => json!(val),
x => panic!("unable to parse column: {} with value: {}", column, x)
};
row.insert(*column as &str, value);
}
let json = serde_json::to_string(&row).unwrap();
dbg!(json);
break;
}
Ok(())
}
I guess this is a conceptual oxymoron "peeking ahead in a LazyFrame-column" ... maybe one of you can enlighten me how to best do it.
I want to put the result of this for each date into a new column:
Ok( (next_weekday_number - current_weekday_number) == 1 )
Here is the sample code to help me find an answer:
// PLEASE be aware to add the needed feature flags in your toml file
use polars::export::arrow::temporal_conversions::date32_to_date;
use polars::prelude::*;
fn main() -> Result<()> {
let days = df!(
"date_string" => &["1900-01-01", "1900-01-02", "1900-01-03", "1900-01-04", "1900-01-05",
"1900-01-06", "1900-01-07", "1900-01-09", "1900-01-10"])?;
let options = StrpTimeOptions {
date_dtype: DataType::Date, // the result column-datatype
fmt: Some("%Y-%m-%d".into()), // the source format of the date-string
strict: false,
exact: true,
};
// convert date_string into dtype(date) and put into new column "date_type"
// we convert the days DataFrame to a LazyFrame ...
// because in my real-world example I am getting a LazyFrame
let mut new_days = days.lazy().with_column(
col("date_string")
.alias("date_type")
.str()
.strptime(options),
);
// This is what I wanted to do ... but I get a string result .. need u32
// let o = GetOutput::from_type(DataType::Date);
// new_days = new_days.with_column(
// col("date_type")
// .alias("weekday_number")
// .map(|x| Ok(x.strftime("%w").unwrap()), o.clone()),
// );
// This is the convoluted workaround
let o = GetOutput::from_type(DataType::Date);
new_days = new_days.with_column(col("date_type").alias("weekday_number").map(
|x| {
Ok(x.date()
.unwrap()
.clone()
.into_iter()
.map(|opt_name: Option<i32>| {
opt_name.map(|datum: i32| {
// println!("{:?}", datum);
date32_to_date(datum)
.format("%w")
.to_string()
.parse::<u32>()
.unwrap()
})
})
.collect::<UInt32Chunked>()
.into_series())
},
o,
));
// Here is where my challenge is ..
// I need to get the weekday_number of the following day to determine a condition
// my pseudo code:
// new_days = new_days.with_column(
// col("weekday_number")
// .alias("cold_day")
// .map(|x| Ok( (next_weekday_number - current_weekday_number) == 1 ), o.clone()),
// );
println!("{:?}", new_days.clone().collect());
Ok(())
}
Ok, I could not find a way to do everything with a LazyFrame, thus I converted the LazyFrame to an eager DataFrame and was able to process two columns at the same time.
So its working for now. Maybe someone can help me realize a solution just with a LazyFrame.
Here is the working code:
use polars::export::arrow::temporal_conversions::date32_to_date;
use polars::prelude::*;
fn main() -> Result<()> {
let days = df!(
"date_string" => &["1900-01-01", "1900-01-02", "1900-01-03", "1900-01-04", "1900-01-05",
"1900-01-06", "1900-01-07", "1900-01-09", "1900-01-10"])?;
let options = StrpTimeOptions {
date_dtype: DataType::Date, // the result column-datatype
fmt: Some("%Y-%m-%d".into()), // the source format of the date-string
strict: false,
exact: true,
};
// convert date_string into dtype(date) and put into new column "date_type"
// we convert the days DataFrame to a LazyFrame ...
// because in my real-world example I am getting a LazyFrame
let mut new_days_lf = days.lazy().with_column(
col("date_string")
.alias("date_type")
.str()
.strptime(options),
);
// Getting the weekday as a number:
// This is what I wanted to do ... but I get a string result .. need u32
// let o = GetOutput::from_type(DataType::Date);
// new_days_lf = new_days_lf.with_column(
// col("date_type")
// .alias("weekday_number")
// .map(|x| Ok(x.strftime("%w").unwrap()), o.clone()),
// );
// This is the convoluted workaround for getting the weekday as a number
let o = GetOutput::from_type(DataType::Date);
new_days_lf = new_days_lf.with_column(col("date_type").alias("weekday_number").map(
|x| {
Ok(x.date()
.unwrap()
.clone()
.into_iter()
.map(|opt_name: Option<i32>| {
opt_name.map(|datum: i32| {
// println!("{:?}", datum);
date32_to_date(datum)
.format("%w")
.to_string()
.parse::<u32>()
.unwrap()
})
})
.collect::<UInt32Chunked>()
.into_series())
},
o,
));
// The "peek" ==> add a shifted column
new_days_lf = new_days_lf.with_column(
col("weekday_number")
.shift_and_fill(-1, 9999)
.alias("next_weekday_number"),
);
// now we convert the LazyFrame into a normal DataFrame for further processing:
let mut new_days_df = new_days_lf.collect()?;
// convert the column to a series
// to get a column by name we need to collect the LazyFrame into a normal DataFrame
let col1 = new_days_df.column("weekday_number")?;
// convert the column to a series
let col2 = new_days_df.column("next_weekday_number")?;
// now I can use series-arithmetics
let diff = col2 - col1;
// create a bool column based on "element == 2"
// add bool column to DataFrame
new_days_df.replace_or_add("weekday diff eq(2)", diff.equal(2)?.into_series())?;
println!("{:?}", new_days_df);
Ok(())
}
I want to change all string values in a LazyFrame-Column.
e.g. from "alles ok" ==> to "ALLES OK"
I see that a series has a function to do it:
polars.internals.series.StringNameSpace.to_uppercase
Q: What is the proper way to apply a string (or Date) manipulation on just one column in a LazyFrame?
Q: Do I need to extract the column I want to work on as a series and re-integrate it?
I can do math on elements of a column and put the result in a new column e.g.:
df.with_column((col("b") ** 2).alias("b_squared")).collect()
but strings?
Ok, after some digging I was able to take a string-column of a LazyFrame and convert it to dtype(datetime).
I also found a code snippet to apply a "len" function to the first column and add the result into a new column:
use polars::prelude::*;
fn main() {
let df: Result<DataFrame> = df!("column_1" => &["Tuesday"],
"column_2" => &["1900-01-02"]);
let options = StrpTimeOptions {
date_dtype: DataType::Datetime(TimeUnit::Milliseconds, None),
fmt: Some("%Y-%m-%d".into()),
strict: false,
exact: true,
};
// in-place convert string into dtype(datetime)
let days = df
.unwrap()
.lazy()
.with_column(col("column_2").str().strptime(options));
// ### courtesy of Alex Moore-Niemi:
let o = GetOutput::from_type(DataType::UInt32);
fn str_to_len(str_val: Series) -> Result<Series> {
let x = str_val
.utf8()
.unwrap()
.into_iter()
// your actual custom function would be in this map
.map(|opt_name: Option<&str>| opt_name.map(|name: &str| name.len() as u32))
.collect::<UInt32Chunked>();
Ok(x.into_series())
}
// ###
// add new column with length of string in column_1
let days = days
.with_column(col("column_1").alias("new_column").apply(str_to_len, o))
.collect()
.unwrap();
let o = GetOutput::from_type(DataType::Utf8);
fn str_to_uppercase(str_val: Series) -> Result<Series> {
let x = str_val
.utf8()
.unwrap()
.into_iter()
// your actual custom function would be in this map
.map(|opt_name: Option<&str>| opt_name.map(|name: &str| name.to_uppercase()))
.collect::<Utf8Chunked>();
Ok(x.into_series())
}
// column_1 to UPPERCASE ... in-place
let days = days
.lazy()
.with_column(col("column_1").apply(str_to_uppercase, o))
.collect()
.unwrap();
println!("{}", days);
}
I would like to take a vector of characters and duplicate the first letter and the last one.
The only way I managed to do that is with this ugly code:
fn repeat_ends(s: &Vec<char>) -> Vec<char> {
let mut result: Vec<char> = Vec::new();
let first = s.first().unwrap();
let last = s.last().unwrap();
result.push(*first);
result.append(&mut s.clone());
result.push(*last);
result
}
fn main() {
let test: Vec<char> = String::from("Hello world !").chars().collect();
println!("{:?}", repeat_ends(&test)); // "HHello world !!"
}
What would be a better way to do it?
I am not sure if it is "better" but one way is using slice patterns:
fn repeat_ends(s: &Vec<char>) -> Vec<char> {
match s[..] {
[first, .. , last ] => {
let mut out = Vec::with_capacity(s.len() + 2);
out.push(first);
out.extend(s);
out.push(last);
out
},
_ => panic!("whatever"), // or s.clone()
}
}
If it can be mutable:
fn repeat_ends(s: &mut Vec<char>) {
if let [first, .. , last ] = s[..] {
s.insert(0, first);
s.push(last);
}
}
If it's ok to mutate the original vector, this does the job:
fn repeat_ends(s: &mut Vec<char>) {
let first = *s.first().unwrap();
s.insert(0, first);
let last = *s.last().unwrap();
s.push(last);
}
fn main() {
let mut test: Vec<char> = String::from("Hello world !").chars().collect();
repeat_ends(&mut test);
println!("{}", test.into_iter().collect::<String>()); // "HHello world !!"
}
Vec::insert:
Inserts an element at position index within the vector, shifting all elements after it to the right.
This means the function repeat_ends would be O(n) with n being the number of characters in the vector. I'm not sure if there is a more efficient method if you need to use a vector, but I'd be curious to hear it if there is.
How would one translate the following Python, in which several files are read and their contents are used as values to a dictionary (with filename as key), to Rust?
countries = {region: open("{}.txt".format(region)).read() for region in ["canada", "usa", "mexico"]}
My attempt is shown below, but I was wondering if a one-line, idiomatic solution is possible.
use std::{
fs::File,
io::{prelude::*, BufReader},
path::Path,
collections::HashMap,
};
macro_rules! map(
{ $($key:expr => $value:expr),+ } => {
{
let mut m = HashMap::new();
$(
m.insert($key, $value);
)+
m
}
};
);
fn lines_from_file<P>(filename: P) -> Vec<String>
where
P: AsRef<Path>,
{
let file = File::open(filename).expect("no such file");
let buf = BufReader::new(file);
buf.lines()
.map(|l| l.expect("Could not parse line"))
.collect()
}
fn main() {
let _countries = map!{ "canada" => lines_from_file("canada.txt"),
"usa" => lines_from_file("usa.txt"),
"mexico" => lines_from_file("mexico.txt") };
}
Rust's iterators have map/filter/collect methods which are enough to do anything Python's comprehensions can. You can create a HashMap with collect on an iterator of pairs, but collect can return various types of collections, so you may have to specify the type you want.
For example,
use std::collections::HashMap;
fn main() {
println!(
"{:?}",
(1..5).map(|i| (i + i, i * i)).collect::<HashMap<_, _>>()
);
}
Is roughly equivalent to the Python
print({i+i: i*i for i in range(1, 5)})
But translated very literally, it's actually closer to
from builtins import dict
def main():
print("{!r}".format(dict(map(lambda i: (i+i, i*i), range(1, 5)))))
if __name__ == "__main__":
main()
not that you would ever say it that way in Python.
Python's comprehensions are just sugar for a for loop and accumulator. Rust has macros--you can make any sugar you want.
Take this simple Python example,
print({i+i: i*i for i in range(1, 5)})
You could easily re-write this as a loop and accumulator:
map = {}
for i in range(1, 5):
map[i+i] = i*i
print(map)
You could do it basically the same way in Rust.
use std::collections::HashMap;
fn main() {
let mut hm = HashMap::new();
for i in 1..5 {
hm.insert(i + i, i * i);
}
println!("{:?}", hm);
}
You can use a macro to do the rewriting to this form for you.
use std::collections::HashMap;
macro_rules! hashcomp {
($name:ident = $k:expr => $v:expr; for $i:ident in $itr:expr) => {
let mut $name = HashMap::new();
for $i in $itr {
$name.insert($k, $v);
}
};
}
When you use it, the resulting code is much more compact. And this choice of separator tokens makes it resemble the Python.
fn main() {
hashcomp!(hm = i+i => i*i; for i in 1..5);
println!("{:?}", hm);
}
This is just a basic example that can handle a single loop. Python's comprehensions also can have filters and additional loops, but a more advanced macro could probably do that too.
Without using your own macros I think the closest to
countries = {region: open("{}.txt".format(region)).read() for region in ["canada", "usa", "mexico"]}
in Rust would be
let countries: HashMap<_, _> = ["canada", "usa", "mexico"].iter().map(|&c| {(c,read_to_string(c.to_owned() + ".txt").expect("Error reading file"),)}).collect();
but running a formatter, will make it more readable:
let countries: HashMap<_, _> = ["canada", "usa", "mexico"]
.iter()
.map(|&c| {
(
c,
read_to_string(c.to_owned() + ".txt").expect("Error reading file"),
)
})
.collect();
A few notes:
To map a vector, you need to transform it into an iterator, thus iter().map(...).
To transform an iterator back into a tangible data structure, e.g. a HashMap (dict), use .collect(). This is the advantage and pain of Rust, it is very strict with types, no unexpected conversions.
A complete test program:
use std::collections::HashMap;
use std::fs::{read_to_string, File};
use std::io::Write;
fn create_files() -> std::io::Result<()> {
let regios = [
("canada", "Ottawa"),
("usa", "Washington"),
("mexico", "Mexico city"),
];
for (country, capital) in regios {
let mut file = File::create(country.to_owned() + ".txt")?;
file.write_fmt(format_args!("The capital of {} is {}", country, capital))?;
}
Ok(())
}
fn create_hashmap() -> HashMap<&'static str, String> {
let countries = ["canada", "usa", "mexico"]
.iter()
.map(|&c| {
(
c,
read_to_string(c.to_owned() + ".txt").expect("Error reading file"),
)
})
.collect();
countries
}
fn main() -> std::io::Result<()> {
println!("Hello, world!");
create_files().expect("Failed to create files");
let countries = create_hashmap();
{
println!("{:#?}", countries);
}
std::io::Result::Ok(())
}
Not that specifying the type of countries is not needed here, because the return type of create_hashmap() is defined.