We hope your experience with Modin is bug-free, but there are some quirks about Modin that may require troubleshooting.

Frequently encountered issues

This is a list of the most frequently encountered issues when using Modin. Some of these are working as intended, while others are known bugs that are being actively worked on.

Error During execution: ArrowIOError: Broken Pipe

One of the more frequently encountered issues is an ArrowIOError: Broken Pipe. This error can happen in a couple of different ways. One of the most common ways this is encountered is from pressing CTRL + C sending a KeyboardInterrupt to Modin. In Ray, when a KeyboardInterrupt is sent, Ray will shutdown. This causes the ArrowIOError: Broken Pipe because there is no longer an available plasma store for working on remote tasks. This is working as intended, as it is not yet possible in Ray to kill a task that has already started computation.

The other common way this Error is encountered is to let your computer go to sleep. As an optimization, Ray will shutdown whenever the computer goes to sleep. This will result in the same issue as above, because there is no longer a running instance of the plasma store.


Restart your interpreter or notebook kernel.

Avoiding this Error

Avoid using KeyboardInterrupt and keeping your notebook or terminal running while your machine is asleep. If you do KeyboardInterrupt, you must restart the kernel or interpreter.

Error during execution: ArrowInvalid: Maximum size exceeded (2GB)

Encountering this issue means that the limits of the Arrow plasma store have been exceeded by the partitions of your data. This can be encountered during shuffling data or operations that require multiple datasets. This will only affect extremely large DataFrames, and can potentially be worked around by setting the number of partitions. This error is being actively worked on and should be resolved in a future release.


import modin.pandas as pd

This will set the number of partitions to a higher count, and reduce the size in each. If this does not work for you, please open an issue.

Hanging on import modin.pandas as pd

This can happen when Ray fails to start. It will keep retrying, but often it is faster to just restart the notebook or interpreter. Generally, this should not happen. Most commonly this is encountered when starting multiple notebooks or interpreters in quick succession.


Restart your interpreter or notebook kernel.

Avoiding this Error

Avoid starting many Modin notebooks or interpreters in quick succession. Wait 2-3 seconds before starting the next one.

Importing heterogeneous data by read_csv

Since Modin read_csv imports data in parallel, it can occur that data read by different partitions can have different type (this happens when columns contains heterogeneous data, i.e. column values are of different types), which are handled differntly. Example of such behaviour is shown below.

import os
import pandas
import modin.pandas as pd
from modin.config import NPartitions


test_filename = "test.csv"
# data with heterogeneous values in the first column
data = """one,2
kwargs = {
    # names of the columns to set, if `names` parameter is set,
    # header inffering from the first data row/rows will be disabled
    "names": ["col1", "col2"],

    # explicit setting of data type of column/columns with heterogeneous
    # data will force partitions to read data with correct dtype
    # "dtype": {"col1": str},

try :
    with open(test_filename, "w") as f:

    pandas_df = pandas.read_csv(test_filename, **kwargs)
    pd_df = pd.read_csv(test_filename, **kwargs)



  col1  col2
0  one     2
1    3     4
2    5     6
3    7     8
4  9.0    10

  col1  col2
0  one     2
1    3     4
2    5     6
3  7.0     8
4  9.0    10

In this case DataFrame read by pandas in the column col1 contain only str data because of the first string value (“one”), that forced pandas to handle full column data as strings. Modin the fisrt partition (the first three rows) read data similary to pandas, but the second partition (the last two rows) doesn’t contain any strings in the first column and it’s data is read as floats because of the last column value and as a result 7 value was read as 7.0, that differs from pandas output.

The above example showed the mechanism of occurence of pandas and Modin read_csv outputs discrepancy during heterogeneous data import. Please note, that similar situations can occur during different data/parameters combinations.


In the case if heterogeneous data is detected, corresponding warning will be showed in the user’s console. Currently, the discrepancies of such type doesn’t properly handled by Modin, and to avoid this issue, it is needed to set dtype parameter of read_csv function manually to force correct data type definition during data import by partitions. Note, that to avoid excessive performance degradation, dtype value should be set fine-grained as it possible (specify dtype parameter only for columns with heterogeneous data).

Setting of dtype parameter works well for most of the cases, but, unfortunetely, it is ineffective if data file contain column which should be interpreted as index (index_col parameter is used) since dtype parameter is responsible only for data fields. For example, if in the above example, kwargs will be set in the next way:

kwargs = {
    "names": ["col1", "col2"],
    "dtype": {"col1": str},
    "index_col": "col1",

Resulting Modin DataFrame will contain incorrect value as in the case if dtype is not set:

one      2
3        4
5        6
7.0      8
9.0     10

In this case data should be imported without setting of index_col parameter and only then index column should be set as index (by using DataFrame.set_index funcion for example) as it is shown in the example below:

pd_df = pd.read_csv(filename, dtype=data_dtype, index_col=None)
pd_df = pd_df.set_index(index_col_name) = None