Is `inplace=True` in pandas Harmful?

In pandas, is inplace = True considered harmful, or not?

In short, yes, inplace = True is considered harmful in pandas. This GitHub issue explicitly proposes deprecating the inplace argument api-wide in the near future. Here are some reasons why:

• Copies are often created anyway: Contrary to its name, inplace = True often does not prevent copies from being created. It (almost) never offers any performance benefits. Most in-place and out-of-place versions of a method create a copy of the data regardless, with the in-place version automatically assigning the copy back.
• Hindering method chaining: Inplace = True also hinders method chaining. Compare the working of:

result = df.some_function1().reset_index().some_function2()

As opposed to:

temp = df.some_function1()
temp.reset_index(inplace=True)
result = temp.some_function2()

• Unintended pitfalls: Calling inplace = True can trigger the SettingWithCopyWarning, which can cause unexpected behavior:

df = pd.DataFrame({'a': [3, 2, 1], 'b': ['x', 'y', 'z']})

df2 = df[df['a'] > 1]
df2['b'].replace({'x': 'abc'}, inplace=True)
# SettingWithCopyWarning: 
# A value is trying to be set on a copy of a slice from a DataFrame

Additionally, it's worth noting that pandas operations default to inplace = False for a reason. This allows for chained/functional syntax (e.g., df.dropna().rename().sum()), avoids expensive SettingWithCopy checks, and provides consistent behavior behind the scenes.

Therefore, it's generally recommended to avoid using inplace = True unless you have a specific need for it.

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