modin | Pandas workflows by changing a single line | SQL Database library
kandi X-RAY | modin Summary
kandi X-RAY | modin Summary
For the complete documentation on Modin, visit our ReadTheDocs page.
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Top functions reviewed by kandi - BETA
- Make a proxy class .
- Return a dict of command - line arguments for distutils .
- Create a grid from a dataframe .
- Merge two DataFrames .
- Handle aggregation results .
- Return an OrderedDict of block indices .
- Check to see if read_csv is supported .
- Initializes the ray .
- Assign row partitions to each actor .
- Read partitioned file .
modin Key Features
modin Examples and Code Snippets
Lines of Code : 70License : Non-SPDX (NOASSERTION)
Copyimport modin.config
# This explicitly sets the number of partitions
modin.config.NPartitions.put(4)
import modin.pandas as pd
import pandas
# Create Modin DataFrame from the external file
pd_dataframe = pd.read_csv("test_data.csv")
# Create Modin from modin.core.storage_formats import BaseQueryCompiler
class DefaultToPandasQueryCompiler(BaseQueryCompiler):
def __init__(self, pandas_df):
self._pandas_df = pandas_df
@classmethod
def from_pandas(cls, df, *args, **kwargs):
import os
# Setting `MODIN_STORAGE_FORMAT` environment variable.
# Also can be set outside the script.
os.environ["MODIN_STORAGE_FORMAT"] = "Hdk"
import modin.config
import modin.pandas as pd
# Checking initially set `StorageFormat` config,
# whic # Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you # Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you # Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you df = pd.DataFrame({'ID' : [1, 2],
'Property' : ['A', 'B'],
'Info1' : ['x', 'a'],
'Info2' : ['y', 'b'],
'Info3' : ['z', 'c'],
})
data=df.melt(id>>> from datar.all import f, tibble, bind_cols, expand, nesting
>>>
>>> df1 = tibble(
... name=["John", "Nick", "Eric"], job=["engineer", "architect", "deisgner"]
... )
>>> df2 = tibble(
... citimport modin.pandas as m_pd
if not isinstance(X, m_pd.DataFrame):
raise TypeError(
"X is not a pandas dataframe. The dataset should be a pandas dataframe.")
import os
os.environ["MODIN_ENGINE"] = "ray"
import ray
ray.init()
import modin.pandas as pd
Community Discussions
Trending Discussions on modin
QUESTION
I need to import and transform xlsx files. They are written in a wide format and I need to reproduce some of the cell information from each row and pair it up with information from all the other rows:
[Edit: changed format to represent the more complex requirements]
Source format
ID Property Activity1name Activity1timestamp Activity2name Activity2timestamp 1 A a 1.1.22 00:00 b 2.1.22 10:05 2 B a 1.1.22 03:00 b 5.1.22 20:16Target format
ID Property Activity Timestamp 1 A a 1.1.22 00:00 1 A b 2.1.22 10:05 2 B a 1.1.22 03:00 2 B b 5.1.22 20:16The following code works fine to transform the data, but the process is really, really slow:
...ANSWER
Answered 2022-Mar-24 at 15:06The df.melt function should be able to do this type of operation much faster.
QUESTION
I try to replace pandas with modin pandas in the code:
...ANSWER
Answered 2022-Feb-20 at 22:39As mentioned by devin-petersohn on Github related to this issue you can simply import modin.pandas as such:
QUESTION
I am trying to accelerate my pandas data processing using modin
...ANSWER
Answered 2022-Feb-09 at 23:35Try initing ray before you import modin:
QUESTION
I am trying to use Modin package to import a sparse matrix created with scipy (specifically, a scipy.sparse.csr_matrix).
Invoking the method:
...ANSWER
Answered 2021-Dec-21 at 14:43This is a bug. The code in this package uses a classmethod to call an instance method, and as a result the self reference is not bound to the inference, but is instead a reference to the first argument (which here is a function).
This is the code that fails:
QUESTION
Suppose I have a .csv which follows this format:
Name, Salary, Department, Mandatory
Rob, 5500, Aviation, Yes
Bob, 1000, Facilities, No
Tom, 6000, IT, Yes
After exporting this to pandas/modin, I'd like to perform row-differentiated checks, where:
People named Rob working in aviation cannot earn less than 5000
People named Bob working in facilities cannot earn less than 1000
Whoever works in facilities has to report their salary, while people working in aviation or IT can choose to leave their salary unreported.
If any check is violated, we store this in a dataframe and pass forward this case to the human resources department for further investigation.
How would you validate this .csv using Pandera?
Sorry if that is a noobish question but I've read the entire Pandera documentation from A to Z and found no straightforward answer to the task at hand.
...ANSWER
Answered 2021-Dec-21 at 01:54Depending on which API you're using, you can check out the wide checks for the object-based API or dataframe checks for the class-based API.
Note: the code snippets below aren't tested, but should be going in the right direction
Class-based API:
QUESTION
There is something about Ray that I could not find a clear answer. Ray is a distributed framework for dataprocessing and training. In order to make it work in a distributed fashion Modin or some other distributed data analysis tool supported by Ray must be used so the data can flow on the whole cluster, but what if I want to use a model like Facebook's Prophet or ARIMA that takes pandas dataframe as input? When I use pandas dataframe as the arguments of the model functions will it work on just a single node or is there a possible workaround for it to work on the cluster?
...ANSWER
Answered 2021-Nov-22 at 16:19Ray is able to train models with pandas dataframes as inputs!
Currently, there is a slight work-around needed for ARIMA, since it typically uses the statsmodels library behind the scenes. In order to ensure the models are serialized correctly, an extra pickle step is needed. Ray might eliminate the need for the pickle work-around in the future.
See explanation of pickle work-around: https://alkaline-ml.com/pmdarima/1.0.0/serialization.html
Here is an excerpt of code for python 3.8 and ray 1.8. Notice that the inputs to train_model() and inference_model() functions are pandas dataframes. The extra pickle step is embedded within those functions. https://github.com/christy/AnyscaleDemos/blob/main/forecasting_demos/nyctaxi_arima_simple.ipynb
QUESTION
I am trying to perform IO operations with multiple directories using ray, modin(with ray backend) and python. The file writes pause and the memory and disk usages do not change at all and the program is blocked.
SetupI have a ray actor set up as this
...ANSWER
Answered 2021-Sep-27 at 13:27For any future readers,
modin.DataFrame.to_csv() pauses unexplainably for unknown reasons, but modin.Dataframe.to pickle() doesnt with the same logic.
There is also a significant performance increase in terms of read/write times, when data is stored as .pkl files.
QUESTION
I was trying to compute the pandas.plotting.scatter_matrix() values for very large pandas.DataFrame() (relatively speaking for this specific operation, most libraries either run OOM most of the time or implement a row count check of 50000, see vaex-scatter).
The 'Time series' DataFrame shape I have is (10000000, 41). Every value is either a float or an integer.
Q1: So the first thing I would already like to ask is how do I do that memory and space efficiently.
What I tried for Q1I tried to do it typically (like in the examples in the documentation) using matplotlib and
modin.pandas.DataFrameslooping over each pair, so the indexing and operations/calculations I want to do are relatively fast including theto_numpy()method. How ever as you might have already seen from the image 1 pair takes 18.1 secs at least and 41x41 pairs are too difficult to handle in my task and I feel there is a relatively faster way of doing things. :)I tried using the pandas scatter plot function which is also too slow and crashes my memory. This is done using the native
pandaspackage and not themodin.pandas. This was done by first converting themodin.pandas.DataFrametopandas.DataFramevia the privatemodin.pandas.DataFrame._to_pandas()accessor. This approach is too slow too. I stopped waiting after I ran out of memory 1 hour later.I tried plotting with vaex. This was the fastest but I ran into other errors which arent related to the question.
please do not suggest seaborn's pair plot. Tried and it takes around 5 mins to generate a
pairplot()for apandas.DataFrameof shape(1000x8), also is cantered around pandas.
- I am plotting a scatter matrix of all the features sampled 10000 times. so
modin.DataFrame.sample(10000)since it kind of is okay to view at the general trend but i do not wish to do this if there is a better option. - Converting it to
pandas.DataFrameand usingpandas.plotting.scatter_matrixlike this, so that i dont have to wait for it to be rendered onto the jupyter notebook.
ANSWER
Answered 2021-Jul-30 at 15:08For future readers, the process I opted was to use datashader.org as @JodyKlymak suggested in his comment(Thanks) with pandas.DataFrame.
please bear in mind that this approach answers both the questions.
- Convert your
modin.pandas.DataFrametopandas.DataFramewith the privatemodin.pandas.DataFrame._to_pandas() - plot the graphs first to an xarray image like so xarray-imshow.
QUESTION
I am using modin to read an sql table, however I am getting this warning
...ANSWER
Answered 2021-Apr-13 at 06:20It seems you are using Modin in which engine initialization is being occurred while importing, i.e. at this moment import modin.pandas as pd. You don't need to create dask client yourself after that because dask environment has already been initialized. But if you want to create dask client yourself, you just need to move some lines:
QUESTION
I am using modin.pandas to remove the duplicates from dataframe.
...ANSWER
Answered 2021-Mar-09 at 06:36It looks like that Modin version, which you are using, is old enough. I don't have the issue on the latest master. Please, try install Modin from sources:
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