Value Error: Negative Dimensions Are Not Allowed When Merging

I am merging 2 dataframes together. They are originally .csv files which are only 7 megabytes each (2 columns and 290,000 rows). I am merging like this: merge=pd.merge(df1,df2, o

Solution 1:

On a 32-bit machine, the default NumPy integer dtype is int32. On a 64-bit machine, the default NumPy integer dtype is int64.

The largest integers representable by an int32 and int64 are:

In[88]: np.iinfo('int32').maxOut[88]: 2147483647In[87]: np.iinfo('int64').maxOut[87]: 9223372036854775807

So the integer index created by pd.merge will support a maximum of 2147483647 = 2**31-1 rows on a 32-bit machine, and 9223372036854775807 = 2**63-1 rows on a 64-bit machine.

In theory, two 290000 row DataFrames merged with an outer join may have as many as 290000**2 = 84100000000 rows. Since

In [89]: 290000**2 > np.iinfo('int32').max
Out[89]: True

the 32-bit machine may not be able to generate an integer index big enough to index the merged result.

And although the 64-bit machine can in theory generate an integer index big enough to accommodate the result, you may not have enough memory to build a 84 billion-row DataFrame.

Now, of course, the merged DataFrame may have fewer than 84 billion rows (the exact number depends on how many duplicate values appear in df1['POINTID'] and df2['POINTID']) but the above back-of-the envelope calculation shows that the behavior you are seeing is consistent with having a lot of duplicates.

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PS. You can get negative values when adding or multiplying positive integers in NumPy arrays if there is arithmetic overflow:

In [92]: np.int32(290000)*np.int32(290000)
Out[92]: -1799345920

My guess is that this is the reason for the exception:

ValueError: negative dimensions are not allowed