eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I am working with 2 datasets. One describes some time windows by their start and stop times. The second one contains a big list of events with their corresponding timestamps. I want to combine this into a single dataframe that contains the start and stop time of each window, together with how many events happened during this time window. I have managed to "solve" my problem with: import polars as pl actions = { "id": ["a", "a", "a", "a", "b", "b", "a", "a"], "action": ["start", "stop", "start", "stop", "start", "stop", "start", "stop"], "time": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], } events = { "name": ["x", "x", "x", "y", "y", "z", "w", "w", "w"], "time": [0.0, 0.1, 0.5, 1.1, 2.5, 3.0, 4.5, 4.9, 5.5], } actions_df = ( pl.DataFrame(actions) .group_by("id") .agg( start=pl.col("time").filter(pl.col("action") == "start"), stop=pl.col("time").filter(pl.col("action") == "stop"), ) .explode(["start", "stop"]) ) df = ( actions_df.join(pl.DataFrame(events), how="cross") .filter((pl.col("time") >= pl.col("start")) & (pl.col("time") <= pl.col("stop"))) .group_by(["id", "start", "stop", "name"]) .agg(count=pl.count("name")) .pivot("name", index=["id", "start", "stop"], values="count") .fill_null(0) ) result_df = ( actions_df.join(df, on=["id", "start", "stop"], how="left") .fill_null(0) .sort("start") ) print(result_df) """ ┌─────┬───────┬──────┬─────┬─────┬─────┬─────┐ │ id ┆ start ┆ stop ┆ w ┆ y ┆ x ┆ z │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ f64 ┆ u32 ┆ u32 ┆ u32 ┆ u32 │ ╞═════╪═══════╪══════╪═════╪═════╪═════╪═════╡ │ a ┆ 0.0 ┆ 1.0 ┆ 0 ┆ 0 ┆ 3 ┆ 0 │ │ a ┆ 2.0 ┆ 3.0 ┆ 0 ┆ 1 ┆ 0 ┆ 1 │ │ b ┆ 4.0 ┆ 5.0 ┆ 2 ┆ 0 ┆ 0 ┆ 0 │ │ a ┆ 6.0 ┆ 7.0 ┆ 0 ┆ 0 ┆ 0 ┆ 0 │ └─────┴───────┴──────┴─────┴─────┴─────┴─────┘ """  My issue is that this approach "explodes" in RAM and my process gets killed. I guess that the join(... how="cross") makes my dataframe huge, just to then ignore most of it again. Can I get some help/hints on a better way to solve this? To give some orders of magnitude, my "actions" datasets have on the order of 100-500 time windows (~1 MB), and my "events" datasets have on the order of ~10 million (~200 MB). And I am getting my process killed with 16 GB of RAM. EDIT In real data, my intervals can be overlapping. Thanks to @RomanPekar for bringing this up.

Answer: The mentioned non-equi joins PR has been merged as part of Polars 1.7.0 It is called .join_where() and is just an inner join for now. (actions_df .join_where(events_df, pl.col.start <= pl.col.time, pl.col.stop >= pl.col.time ) .pivot( on = "name", values = "time", aggregate_function = pl.len() ) )  shape: (3, 7) ┌─────┬───────┬──────┬──────┬──────┬──────┬──────┐ │ id ┆ start ┆ stop ┆ x ┆ y ┆ z ┆ w │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ f64 ┆ u32 ┆ u32 ┆ u32 ┆ u32 │ ╞═════╪═══════╪══════╪══════╪══════╪══════╪══════╡ │ a ┆ 0.0 ┆ 1.0 ┆ 3 ┆ null ┆ null ┆ null │ │ a ┆ 2.0 ┆ 3.0 ┆ null ┆ 1 ┆ 1 ┆ null │ │ b ┆ 4.0 ┆ 5.0 ┆ null ┆ null ┆ null ┆ 2 │ └─────┴───────┴──────┴──────┴──────┴──────┴──────┘  There is an issue for additional join types:  https://github.com/pola-rs/polars/issues/18669
Question: I am using LangChain 0.2.34 together with Python 3.12.5 to build a RAG architecture and Pydantic 2.8.2 for validation. It appears that some LangChain classes are not compatible with Pydantic although I explicitly allow arbitrary types. Or am I missing something? Here is a code sample and the respective error. from typing import List from langchain_core.documents.base import Document from pydantic import BaseModel, ConfigDict class ResponseBody(BaseModel): message: List[Document] model_config = ConfigDict(arbitrary_types_allowed=True) docs = [Document(page_content="This is a document")] res = ResponseBody(message=docs)  Error: TypeError: BaseModel.validate() takes 2 positional arguments but 3 were given 

Answer: Langchain is using the functionality in pydantic v1. Define your model with v1 syntax: from pydantic.v1 import BaseModel, ConfigDict ...  You can read about their migration plan + pydantic compatibility here: How to use LangChain with different Pydantic versions
Question: Is it possible **, to pass a value to a process via the startup url/path. I have a process model with a note field. I want to start a new process flow and pass the note to the url e.g. http://server.com/my_process/start/?note=mynote

Answer: Since Viewflow is a thin workflow layer built on top of Django, URL parameter processing works just like in Django. Parameters are available in the request.GET object, and you can use them in a custom view according to your needs. For example, to pre-initialize a user form with a value from the URL, you can create a custom subclass of CreateProcessView: from viewflow.workflow.flow.views import CreateProcessView class CustomCreateProcessView(CreateProcessView): """ Custom view to initialize a process with data from the request URL. """ def get_initial(self): initial = super().get_initial() initial['text'] = self.request.GET.get('note', '') return initial  With this approach, when you navigate to a URL like http://server.com/my_process/start/?note=mynote, the note parameter will be extracted and used to initialize the text field in the form. For more details, refer to the Django documentation on class-based views and URL handling.
Question: Using FastHTML, I would like to create a navigation bar that updates a div's content based on the item clicked. I wrote the following piece of code, but nothing happens when I click the buttons: the #page-content div doesn't update, and I don't see the logs of the GET methods being triggered. from fasthtml.common import * app = FastHTML() @app.route("/", methods=["get"]) def index(): return Html( Body( Header(navigation_bar(["page1", "page2"])), Div(page1(), id="page-content"), ), ) def navigation_bar(navigation_items: list[str]): return Nav( Ul( *[ Li(Button(item, hx_get=f"/{item}", hx_target="#page-content")) for item in navigation_items ] ), ) @app.route("/page1", methods=["get"]) def page1(): return Div("You are on page 1.") @app.route("/page2", methods=["get"]) def page2(): return Div("You are on page 2.") serve()  Here is the generated HTML: <html> <head></head> <body> <header> <nav> <ul> <li> <button hx-get="/page1" hx-target="#page-content">page-1</button> </li> <li> <button hx-get="/page2" hx-target="#page-content">page-2</button> </li> </ul> </nav> </header> <div id="page-content"><div>You are on page 1.</div></div> </body> </html>  I think the HTML is generated correctly, as the tags' attributes related to HTMX look like what I could see on online examples. Where could the issue be?

Answer: The issue came from the fact that I was wrapping my Body component into an Html component manually. Removing the Html component, FastHTML takes care of the wrapping automatically and also includes the required scripts. Here is my updated piece of code: from fasthtml.common import * app = FastHTML() @app.route("/", methods=["get"]) def index(): return Body( Header(navigation_bar(["page1", "page2"])), Div(page1(), id="page-content"), ) def navigation_bar(navigation_items: list[str]): return Nav( Ul( *[ Li(Button(item, hx_get=f"/{item}", hx_target="#page-content")) for item in navigation_items ] ), ) @app.route("/page1", methods=["get"]) def page1(): return Div("You are on page 1.") @app.route("/page2", methods=["get"]) def page2(): return Div("You are on page 2.") serve()
Question: I'm working with Pydantic for data validation in a Python project and I'm encountering an issue with specifying optional fields in my BaseModel. from pydantic import BaseModel class MyModel(BaseModel): author_id: int | None # Case 1: throws error author_id: Optional[int] # Case 2: throws error author_id: int = None # Case 3: works  Now, while requesting an endpoint that accepts the above model as its JSON body, I am not providing the field author_id in the request. When I use author_id: int | None, I get an error saying that a required field is missing. However, if I change it to author_id: Optional[int], I encounter the same error. But when I use author_id: int = None or author_id: Optional[int] = None, the model works as expected without errors. (Working if = is present) Do you have any recommendations on how to properly define optional fields in Pydantic models? Is there a specific version of Pydantic (or another library) that supports the int | None syntax correctly?  python==3.11 pydantic==2.8.1 fastapi==0.111.1 

Answer: The reason you encounter an error is that there is no default value. The annotations don't actually do anything by themselves. All of the options bellow work, and they are pretty much equivalent under the hood. The third option is now recommended for projects only using Python 3.10+ from pydantic import BaseModel class MyModel(BaseModel): author_id: Optional[int] = None # the original way author_id: Union[int, None] = None # another option author_id: int | None = None # more modern option (Python 3.10+)
Question: I have a pl.DataFrame with a column comprising lists with integers. I need to assert that each consecutive integer is showing up two times in a row at a maximum. For instance, a list containing [1,1,0,-1,1] would be OK, as the number 1 is showing up max two times in a row (the first two elements, followed by a zero). This list should lead to a failed assertion: [1,1,1,0,-1] The number 1 shows up three times in a row. Here's a toy example, where row2 should lead to a failed assertion. import polars as pl row1 = [0, 1, -1, -1, 1, 1, -1, 0] row2 = [1, -1, -1, -1, 0, 0, 1, -1] df = pl.DataFrame({"list": [row1, row2]}) print(f"row1: {row1}") print(f"row2: {row2}") print(df) row1: [0, 1, -1, -1, 1, 1, -1, 0] row2: [1, -1, -1, -1, 0, 0, 1, -1] shape: (2, 1) ┌───────────────┐ │ list │ │ --- │ │ list[i64] │ ╞═══════════════╡ │ [0, 1, … 0] │ │ [1, -1, … -1] │ └───────────────┘ 

Answer: The following could be used.  Perform run-length encoding of the list using pl.Expr.rle. This produces a list of structs. Each struct contains a (unique) list value and the corresponding run length.  Check whether the maximum run length in the list is at most 2.  Ensure the result is of type bool by selecting the first (and only) element in the resulting list (using pl.Expr.list.first).   df.with_columns( ok=pl.col("list").list.eval( pl.element().rle().struct.field("len").max() <= 2 ).list.first() )  shape: (2, 2) ┌──────────────────────────────┬───────┐ │ list ┆ ok │ │ --- ┆ --- │ │ list[i64] ┆ bool │ ╞══════════════════════════════╪═══════╡ │ [0, 1, -1, -1, 1, 1, -1, 0] ┆ true │ │ [1, -1, -1, -1, 0, 0, 1, -1] ┆ false │ └──────────────────────────────┴───────┘  Doing all of this in a pl.Expr.list.eval can be avoided by exploding the list. Then, a window function (pl.Expr.over) is needed to ensure the maximum is computed separately for each list. max_run_length = pl.col("list").explode().rle().struct.field("len").max().over(pl.int_range(pl.len())) df.with_columns(passed=max_run_length <= 2)  The result will be same.
Question: I need to compute the Herfindahl–Hirschman index ("HHI", the sum of squared market shares) but leaving out the firm represented in the row. Here's an example: df = (pl.DataFrame({ 'year':(2023, 2023, 2023, 2024, 2024, 2024), 'firm':('A', 'B', 'C', 'A', 'B', 'C'), 'volume':(20, 50, 3, 25, 13, 5) }) .with_columns( sum = pl.col('volume').sum().over('year'), leaveout_sum = (pl.col('volume').sum().over('year'))-(pl.col('volume')) ) .with_columns( share = (pl.col('volume')/pl.col('sum'))*100 ) .with_columns( hhi = (pl.col('share')**2).sum().over('year').round() ))  Which gives: ┌──────┬──────┬────────┬─────┬──────────────┬───────────┬────────┐ │ year ┆ firm ┆ volume ┆ sum ┆ leaveout_sum ┆ share ┆ hhi │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ str ┆ i64 ┆ i64 ┆ i64 ┆ f64 ┆ f64 │ ╞══════╪══════╪════════╪═════╪══════════════╪═══════════╪════════╡ │ 2023 ┆ A ┆ 20 ┆ 73 ┆ 53 ┆ 27.39726 ┆ 5459.0 │ │ 2023 ┆ B ┆ 50 ┆ 73 ┆ 23 ┆ 68.493151 ┆ 5459.0 │ │ 2023 ┆ C ┆ 3 ┆ 73 ┆ 70 ┆ 4.109589 ┆ 5459.0 │ │ 2024 ┆ A ┆ 25 ┆ 43 ┆ 18 ┆ 58.139535 ┆ 4429.0 │ │ 2024 ┆ B ┆ 13 ┆ 43 ┆ 30 ┆ 30.232558 ┆ 4429.0 │ │ 2024 ┆ C ┆ 5 ┆ 43 ┆ 38 ┆ 11.627907 ┆ 4429.0 │ └──────┴──────┴────────┴─────┴──────────────┴───────────┴────────┘  The hhi column there is the normal HHI index, including all firms in the market, and I can compute the "leaveout" volume summation to get the sum of volumes of the other firms in that year. For example, the leaveout-HHI for firm A in 2023 would be the square of 3/53 plus the square of 50/53 (i.e., the squares of the market shares of firms B and C assuming firm A didn't exist). How do I tell polars to do this? Is there a way to filter the window function, perhaps? My real dataset includes almost 800 firms over 204 months for 500 separate markets, so doing this manually is out of the question.

Answer: You can take out the denominator from the sum of squares: .with_columns( leaveout_sum = (pl.col.volume.sum().over('year')) - pl.col.volume, leaveout_sum_of_sq = (pl.col.volume**2).sum().over('year') - pl.col.volume**2 ) .with_columns( leaveout_hhi = pl.col.leaveout_sum_of_sq / pl.col.leaveout_sum**2 ))  I left out the * 100 factor, the above correctly computes (3/53)^2 + (50/53)^2 for your example by doing (3^2 + 50^2) / (50 + 3)^2.
Question:  Given a string made up of "a", "e", "i", "o", or "u", find the longest subsequence of vowels in order. For example, is the string is "aeiaeiou", then the answer is 6, making the subsequence "aaeiou". Another example is: "aeiaaioooaauuaeiou" where the answer is 10. A complete subsequence means that you need to have all the vowels in order, a -> e -> i -> o -> u, but you can have repeats of vowels like "aaeeeeiiooouuu" is valid. If no such subsequence exists, return 0.  I came up with a DP approach of O(n^2) and I'm curious if there is a faster version for this problem. def find_longest_dp(vowels): n = len(vowels) dp = [[0, False] for _ in range(n)] prev_vowel = {"a": None, "e": "a", "i": "e", "o": "i", "u": "o"} for i in range(n): if vowels[i] == "a": dp[i] = [1, True] for j in range(i): if dp[j][1] and vowels[j] in {vowels[i], prev_vowel[vowels[i]]}: dp[i][0] = max(dp[i][0], dp[j][0] + 1) dp[i][1] = True return max((dp[i][0] for i in range(n) if vowels[i] == 'u'), default=0) 

Answer: O(n) by keeping track of the max length for each last-of-the-subsequence vowel (e.g., maxlen[3] tells the max length of an ordered subsequence ending with 'o'): def find_longest_dp2(vowels): maxlen = [0] * 5 for v in vowels: i = 'aeiou'.find(v) maxlen[i] = max(maxlen[:i+1]) + 1 return max(maxlen)  Or if all five vowels need to be in the subsequence (as the "complete" in your title suggests) and you want 0 if no such subsequence exists (as your code suggests): def find_longest_dp3(vowels): maxlen = [0] + [float('-inf')] * 5 for v in vowels: i = '.aeiou'.find(v) maxlen[i] = max(maxlen[i-1:i+1]) + 1 return max(maxlen[5], 0)  Attempt This Online! with testing.
Question: I would like to drop the row with all three columns have equal value. e.g. import pandas as pd data = [ ['A',2,2,2], ['B',2,2,3], ['C',3,3,3], ['D',4,2,2], ['E',5,5,2] ] df = pd.DataFrame(data,columns=['name','val1','val2','val3']) print(df)  In above example, row 0 and row 2 will be dropped since the value is equal.

Answer: You can easily drop rows where specified columns have equal values using the following steps: Step 1: Use df[['val1', 'val2', 'val3']].nunique(axis=1) to calculate the number of unique values in each row across these columns. Step2 : where the number of unique values >= then filter the rows Here is code snippet to guide you #filters val1, val2, val3 is >=1 df_filterd = df[df[['val1', 'val2', 'val3']].nunique(axis=1) > 1] print(df_filterd)  Result:  name val1 val2 val3 1 B 2 2 3 3 D 4 2 2 4 E 5 5 2  OR You can drop rows where all values in val1, val2, val3 are equal , here is docs which you can use to reference it drop df_drop = df[~df[['val1', 'val2', 'val3']].eq(df['val1'], axis=0).all(axis=1)]  print(df_drop)  name val1 val2 val3 1 B 2 2 3 3 D 4 2 2 4 E 5 5 2
Question: I need to find the first non-null value in a column over a grouped pl.DataFrame. import polars as pl df = pl.DataFrame( { "symbol": ["s1", "s1", "s2", "s2"], "trade": [None, 1, -1, None], } ) shape: (4, 2) ┌────────┬───────┐ │ symbol ┆ trade │ │ --- ┆ --- │ │ str ┆ i64 │ ╞════════╪═══════╡ │ s1 ┆ null │ │ s1 ┆ 1 │ │ s2 ┆ -1 │ │ s2 ┆ null │ └────────┴───────┘  How can I get the row numbers/index values of the first non-null value in the trade columns while group_by symbol? I am actually looking for the row/index numbers 1 and 0. Maybe the result could be something like this: shape: (2, 2) ┌────────┬────────────────┐ │ symbol ┆ first-non-null │ │ --- ┆ --- │ │ str ┆ i64 │ ╞════════╪════════════════╡ │ s1 ┆ 1 │ │ s2 ┆ 0 │ └────────┴────────────────┘  I am actually looking for the equivalent to pd.first_valid_index()

Answer: Here's one way using .arg_true().first(): print( df.group_by("symbol").agg( pl.col("trade").is_not_null().arg_true().first().alias("first-non-null") ) )  Output: ┌────────┬────────────────┐ │ symbol ┆ first-non-null │ │ --- ┆ --- │ │ str ┆ u32 │ ╞════════╪════════════════╡ │ s1 ┆ 1 │ │ s2 ┆ 0 │ └────────┴────────────────┘
Question: Consider the following pl.DataFrame: import polars as pl df = pl.DataFrame( { "symbol": ["s1", "s1", "s2", "s2"], "signal": [0, 1, 2, 0], "trade": [None, 1, None, -1], } ) shape: (4, 3) ┌────────┬────────┬───────┐ │ symbol ┆ signal ┆ trade │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 │ ╞════════╪════════╪═══════╡ │ s1 ┆ 0 ┆ null │ │ s1 ┆ 1 ┆ 1 │ │ s2 ┆ 2 ┆ null │ │ s2 ┆ 0 ┆ -1 │ └────────┴────────┴───────┘  Now, I need to group the dataframe by symbol and check if the first row in every group in column signal is not equal to 0 (zero). It this equals to True, I need to replace the corresponding cell in column trade with the value in the cell in signal. Here's what I am actually looking for: shape: (4, 3) ┌────────┬────────┬───────┐ │ symbol ┆ signal ┆ trade │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 │ ╞════════╪════════╪═══════╡ │ s1 ┆ 0 ┆ null │ │ s1 ┆ 1 ┆ 1 │ │ s2 ┆ 2 ┆ 2 │ <- copy value from the ``signal`` column │ s2 ┆ 0 ┆ -1 │ └────────┴────────┴───────┘ 

Answer: For this, a when-then-otherwise construct may be used.  We create a condition that evaluates to True exactly for the first rows (create index on the fly using pl.int_range) in each group with signal not equal to 0. Based on that condition, we either select the value in the signal or trade column.  df.with_columns( trade=pl.when( pl.col("signal") != 0, pl.int_range(pl.len()) == 0, ).then("signal").otherwise("trade").over("symbol") )  shape: (4, 3) ┌────────┬────────┬───────┐ │ symbol ┆ signal ┆ trade │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 │ ╞════════╪════════╪═══════╡ │ s1 ┆ 0 ┆ null │ │ s1 ┆ 1 ┆ 1 │ │ s2 ┆ 2 ┆ 2 │ │ s2 ┆ 0 ┆ -1 │ └────────┴────────┴───────┘
Question: Is there a way to include the column (not rows!) index name in the output when calling Pandas' dataframe.to_csv() method? For example: import pandas as pd iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') pivot_iris = iris.pivot_table(index='species', columns='sepal_length', values='sepal_width') print(pivot_iris.columns) print(pivot_iris) pivot_iris.to_csv('pivot_iris.csv', index=True, header=True)  After calling pivot, the column index name is set to sepal_length as you can see in the prints Index([4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.3, 7.4, 7.6, 7.7, 7.9], dtype='float64', name='sepal_length')  and sepal_length 4.3 4.4 4.5 4.6 4.7 ... 7.3 7.4 7.6 7.7 7.9 species ... setosa 3.0 3.033333 2.3 3.325 3.2 ... NaN NaN NaN NaN NaN versicolor NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN virginica NaN NaN NaN NaN NaN ... 2.9 2.8 3.0 3.05 3.8 [3 rows x 35 columns]  Unfortunately the output file produced with to_csv() is missing the label in front of the column names: species,4.30,4.40,4.50,4.60,4.70,4.80,4.90,5.00,5.10,5.20,5.30,5.40,5.50,5.60,5.70,5.80,5.90,6.00,6.10,6.20,6.30,6.40,6.50,6.60,6.70,6.80,6.90,7.00,7.10,7.20,7.30,7.40,7.60,7.70,7.90 setosa,3.00,3.03,2.30,3.33,3.20,3.18,3.20,3.36,3.60,3.67,3.70,3.66,3.85,,4.10,4.00,,,,,,,,,,,,,,,,,,, versicolor,,,,,,,2.40,2.15,2.50,2.70,,3.00,2.44,2.82,2.82,2.67,3.10,2.80,2.88,2.55,2.70,3.05,2.80,2.95,3.07,2.80,3.10,3.20,,,,,,, virginica,,,,,,,2.50,,,,,,,2.80,2.50,2.73,3.00,2.60,2.80,3.10,2.93,2.92,3.05,,3.04,3.10,3.13,,3.00,3.27,2.90,2.80,3.00,3.05,3.80  is there a way to include it?

Answer: You can't really include the index names in a CSV. What you could do is to create a MultiIndex: pivot_iris = (pd.concat({'sepal_length': iris.pivot_table(index='species', columns='sepal_length', values='sepal_width')}, axis=1) .rename_axis(columns=(None, 'species')).reset_index() ) pivot_iris.to_csv('pivot_iris.csv', index=False)  Output: species,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length,sepal_length ,4.3,4.4,4.5,4.6,4.7,4.8,4.9,5.0,5.1,5.2,5.3,5.4,5.5,5.6,5.7,5.8,5.9,6.0,6.1,6.2,6.3,6.4,6.5,6.6,6.7,6.8,6.9,7.0,7.1,7.2,7.3,7.4,7.6,7.7,7.9 setosa,3.0,3.03,2.3,3.32,3.2,3.18,3.2,3.36,3.6,3.67,3.7,3.66,3.85,,4.1,4.0,,,,,,,,,,,,,,,,,,, versicolor,,,,,,,2.4,2.15,2.5,2.7,,3.0,2.44,2.82,2.82,2.67,3.1,2.8,2.88,2.55,2.7,3.05,2.8,2.95,3.07,2.8,3.1,3.2,,,,,,, virginica,,,,,,,2.5,,,,,,,2.8,2.5,2.73,3.0,2.6,2.8,3.1,2.93,2.92,3.05,,3.04,3.1,3.13,,3.0,3.27,2.9,2.8,3.0,3.05,3.8