# Common Problems

This page describes common problems you may encounter with Pandas, and how to fix them.

## Memory Explosion

Symptoms: notebook or kernel crashes, system grinds to a halt.

There are some seemingly-innocuous things that can cause Pandas to eat an absurd amount of memory. The common ones involve either hierarchical indexes or non-unique indexes. I've seen it happen with:

• groupby on a frame with a hierarchical index
• groupby on a frame with a non-unique index
• join with a non-unique index

If you are experiencing this problem, make sure that your frames have single-level, unique indexes. If in doubt, just reset the index to get a range index.

For more on indexes, see Indexing.

## Chained Assignment

Symptoms: Pandas issues a warning about chained assignment or assigning to a copy of a slice.

When you slice or subset a data frame, Pandas returns a view backed by the original data. If you then try to modify that slice, Pandas is not sure if you want to modify the original data or the view (which is supposed to act like a copy, but save memory).

For example, if you write:

frame[mask]['column'] = 400


Pandas will issue the warning. There are two solutions, depending on what you want to do:

• If you want to modify the underlying frame, use .loc (or .iloc) to index and modify the appropriate subset:

frame.loc[mask, 'column'] = 400

• If you want to modify a copy, use .copy() to make a copy of the frame:

f2 = frame[mask].copy()
f2['column'] = 400


In this version, the contents of frame are unchanged.