When working with pandas, the distinction between shallow and deep copies isn’t just academic—it directly impacts both performance and correctness. If you’re not careful, changes to what you consider is a separate DataFrame can sneak back and alter your original data.
A shallow copy means you get a new object, but the underlying data is still shared. Modifying the data in one can affect the other. Deep copies, on the other hand, duplicate everything, making two independent objects. The trade-off? Deep copies consume more memory and take longer to create.
Here’s a quick demonstration:
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
shallow = df.copy(deep=False)
deep = df.copy(deep=True)
shallow.loc[0, 'a'] = 99
print(df.loc[0, 'a']) # Output: 99, because shallow shares data
deep.loc[1, 'a'] = 88
print(df.loc[1, 'a']) # Output: 2, because deep is a full copy
Notice how modifying the shallow copy changed the original DataFrame’s data. That’s because the shallow copy only copies the DataFrame structure—the indices, column labels, and the object itself—while the underlying data buffers remain shared.
These buffers are numpy arrays or other memory blocks, so a shallow copy essentially means two pandas objects point to the same memory. That’s efficient when you want a different view or subset but don’t want to pay the cost of copying all data.
But there’s a catch: certain pandas operations trigger implicit copies or views unpredictably. For example, slicing a DataFrame with df.loc or df.iloc can return either a view or a copy depending on internal heuristics, which is a source of confusion and bugs.
To illustrate, slicing a column returns a view:
col_view = df['a'] col_view[0] = 77 print(df.loc[0, 'a']) # 77, because col_view is a view
But slicing rows might return a copy:
row_slice = df.iloc[0:2] row_slice.loc[0, 'a'] = 55 print(df.loc[0, 'a']) # 77 still, row_slice is a copy here
This ambiguity is why pandas warns about chained assignments—they’re often an indication you’re modifying a copy and not the original, or vice versa. The rule of thumb is: if you want to guarantee isolation, use copy(deep=True). If you want to save memory and are confident about your data flow, shallow copies or views can speed things up.
Under the hood, shallow copies use __finalize__ and internal memory sharing mechanics, so the cost isn’t just in copying but also in the complexity of tracking what shares memory with what. That is a subtle, but critical point when handling large datasets where memory footprint matters.
One last note: pandas has a copy() method, but also you can use pd.DataFrame() constructor to create copies, sometimes implicitly deep, sometimes shallow depending on the input. So always check the docs or test behavior if you want to be sure.
In summary, shallow copies equal new containers pointing to the same data buffers, deep copies equal new containers with duplicated data buffers. Your choice depends on whether you prioritize memory and speed or data integrity and isolation. Most bugs come from ignoring this difference or assuming slicing always returns views, which it doesn’t.
Next, we’ll look at how to optimize data manipulation by carefully choosing copy strategies to balance performance and correctness. But before that, let’s explore some typical pitfalls when mixing shallow copies and in-place modifications in pandas—
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Optimizing data manipulation with copy strategies
When optimizing data manipulation, the first principle is to minimize unnecessary copying. If you can work with views or shallow copies safely, do it. Avoid deep copies unless you explicitly need to isolate data changes. This is especially true in pipelines where data is filtered, transformed, or aggregated repeatedly.
Ponder the common pattern of filtering rows and then modifying some values. Instead of copying the filtered DataFrame, modify it in place when possible:
filtered = df.loc[df['a'] > 1] # This returns a copy, so modifying filtered won't affect df filtered['b'] = filtered['b'] * 2
Here, if you want the changes to reflect in the original df, you must either assign back or operate directly on the original DataFrame using boolean indexing:
df.loc[df['a'] > 1, 'b'] *= 2
This avoids creating an intermediate copy and keeps memory usage down. The key is to leverage pandas’ in-place operations and indexing mechanisms to work on the original data buffer.
When you must create a copy, explicitly specify whether you want a deep or shallow one. For example, if you want to add new columns derived from existing data without affecting the original DataFrame:
df_copy = df.copy(deep=True) df_copy['c'] = df_copy['a'] + df_copy['b']
This guarantees df remains untouched, which is critical in multi-threaded environments or when debugging complex pipelines.
Another optimization is to avoid chained indexing and assignments, which create temporary copies and cause performance hits. Instead of:
df.loc[df['a'] > 1]['b'] = 0 # Warning: SettingWithCopyWarning
Use:
df.loc[df['a'] > 1, 'b'] = 0
This directly modifies df without creating an intermediate copy. The difference is subtle but important for both performance and avoiding silent bugs.
When working with large datasets, memory mapping or chunked processing may be necessary. In those cases, shallow copies or views can drastically reduce memory overhead:
for chunk in pd.read_csv('largefile.csv', chunksize=100000):
# Operate on chunk, which is a small DataFrame slice
chunk['new_col'] = chunk['existing_col'] * 2
process(chunk)
Here, each chunk is a fresh DataFrame, but within the chunk, you can avoid copying data by careful use of views. This pattern keeps peak memory low while working with datasets that don’t fit in RAM.
Lastly, when creating new DataFrames from existing ones, the constructor behavior varies:
new_df = pd.DataFrame(df) # Usually shallow copy of data buffers new_df = pd.DataFrame(df.values.copy(), columns=df.columns) # Deep copy of data
The first line copies the DataFrame container but shares underlying numpy arrays; the second line forces a deep copy of the raw data. This distinction especially important when you want to prevent side effects from mutations.
The performance and correctness of pandas data manipulation hinge on understanding when copies are made, what kind of copies they’re, and using pandas indexing and assignment idioms that avoid unnecessary duplication. Mastering these concepts lets you write faster, more memory-efficient code without subtle bugs.
Source: https://www.pythonlore.com/using-pandas-dataframe-copy-to-create-data-copies/
