Understanding inplace=True in pandas

In the pandas library many times there is an option to change the object inplace such as with the following statement...

df.dropna(axis='index', how='all', inplace=True)

I am curious what is being returned as well as how the object is handled when inplace=True is passed vs. when inplace=False.

Are all operations modifying self when inplace=True? And when inplace=False is a new object created immediately such as new_df = self and then new_df is returned?

When inplace=True is passed, the data is renamed in place (it returns nothing), so you'd use:

df.an_operation(inplace=True)

When inplace=False is passed (this is the default value, so isn't necessary), performs the operation and returns a copy of the object, so you'd use:

df = df.an_operation(inplace=False) 

https://stackoverflow.com/questions/45570984/

TLDR; Yes, yes it is.

• inplace, contrary to what the name implies, often does not prevent copies from being created, and (almost) never offers any performance benefits
• inplace does not work with method chaining
• inplace can lead to SettingWithCopyWarning if used on a DataFrame column, and may prevent the operation from going though, leading to hard-to-debug errors in code

The pain points above are common pitfalls for beginners, so removing this option will simplify the API.

---

I don't advise setting this parameter as it serves little purpose. See this GitHub issue which proposes the inplace argument be deprecated api-wide.

It is a common misconception that using inplace=True will lead to more efficient or optimized code. In reality, there are absolutely no performance benefits to using inplace=True. Both the in-place and out-of-place versions create a copy of the data anyway, with the in-place version automatically assigning the copy back.

inplace=True is a common pitfall for beginners. For example, it can trigger the SettingWithCopyWarning:

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

Calling a function on a DataFrame column with inplace=True may or may not work. This is especially true when chained indexing is involved.

As if the problems described above aren't enough, inplace=True also hinders method chaining. Contrast 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()

The former lends itself to better code organization and readability.

---

Another supporting claim is that the API for set_axis was recently changed such that inplace default value was switched from True to False. See GH27600. Great job devs!

The way I use it is

# Have to assign back to dataframe (because it is a new copy)
df = df.some_operation(inplace=False) 

Or

# No need to assign back to dataframe (because it is on the same copy)
df.some_operation(inplace=True)

CONCLUSION:

 if inplace is False
      Assign to a new variable;
 else
      No need to assign

The inplace parameter:

df.dropna(axis='index', how='all', inplace=True)

in Pandas and in general means:

1. Pandas creates a copy of the original data

2. ... does some computation on it

3. ... assigns the results to the original data.

4. ... deletes the copy.

As you can read in the rest of my answer's further below, we still can have good reason to use this parameter i.e. the inplace operations, but we should avoid it if we can, as it generate more issues, as:

1. Your code will be harder to debug (Actually SettingwithCopyWarning stands for warning you to this possible problem)

2. Conflict with method chaining

So there is even case when we should use it yet?

Definitely yes. If we use pandas or any tool for handeling huge dataset, we can easily face the situation, where some big data can consume our entire memory. To avoid this unwanted effect we can use some technics like method chaining:

(
    wine.rename(columns={"color_intensity": "ci"})
    .assign(color_filter=lambda x: np.where((x.hue > 1) & (x.ci > 7), 1, 0))
    .query("alcohol > 14 and color_filter == 1")
    .sort_values("alcohol", ascending=False)
    .reset_index(drop=True)
    .loc[:, ["alcohol", "ci", "hue"]]
)

which make our code more compact (though harder to interpret and debug too) and consumes less memory as the chained methods works with the other method's returned values, thus resulting in only one copy of the input data. We can see clearly, that we will have 2 x original data memory consumption after this operations.

Or we can use inplace parameter (though harder to interpret and debug too) our memory consumption will be 2 x original data, but our memory consumption after this operation remains 1 x original data, which if somebody whenever worked with huge datasets exactly knows can be a big benefit.

Final conclusion:

Avoid using inplace parameter unless you don't work with huge data and be aware of its possible issues in case of still using of it.

Save it to the same variable

data["column01"].where(data["column01"]< 5, inplace=True)

Save it to a separate variable

data["column02"] = data["column01"].where(data["column1"]< 5)

But, you can always overwrite the variable

data["column01"] = data["column01"].where(data["column1"]< 5)

FYI: In default inplace = False

When trying to make changes to a Pandas dataframe using a function, we use 'inplace=True' if we want to commit the changes to the dataframe. Therefore, the first line in the following code changes the name of the first column in 'df' to 'Grades'. We need to call the database if we want to see the resulting database.

df.rename(columns={0: 'Grades'}, inplace=True)
df

We use 'inplace=False' (this is also the default value) when we don't want to commit the changes but just print the resulting database. So, in effect a copy of the original database with the committed changes is printed without altering the original database.

Just to be more clear, the following codes do the same thing:

#Code 1
df.rename(columns={0: 'Grades'}, inplace=True)
#Code 2
df=df.rename(columns={0: 'Grades'}, inplace=False}

Yes, in Pandas we have many functions has the parameter inplace but by default it is assigned to False.

So, when you do df.dropna(axis='index', how='all', inplace=False) it thinks that you do not want to change the orignial DataFrame, therefore it instead creates a new copy for you with the required changes.

But, when you change the inplace parameter to True

> Then it is equivalent to explicitly say that I do not want a new copy > of the DataFrame instead do the changes on the given DataFrame

This forces the Python interpreter to not to create a new DataFrame

But you can also avoid using the inplace parameter by reassigning the result to the orignal DataFrame

df = df.dropna(axis='index', how='all')

inplace=True is used depending if you want to make changes to the original df or not.

df.drop_duplicates()

will only make a view of dropped values but not make any changes to df

df.drop_duplicates(inplace  = True)

will drop values and make changes to df.

Hope this helps.:)

inplace=True makes the function impure. It changes the original dataframe and returns None. In that case, You breaks the DSL chain. Because most of dataframe functions return a new dataframe, you can use the DSL conveniently. Like

df.sort_values().rename().to_csv()

Function call with inplace=True returns None and DSL chain is broken. For example

df.sort_values(inplace=True).rename().to_csv()

will throw NoneType object has no attribute 'rename'

Something similar with python’s build-in sort and sorted. lst.sort() returns None and sorted(lst) returns a new list.

Generally, do not use inplace=True unless you have specific reason of doing so. When you have to write reassignment code like df = df.sort_values(), try attaching the function call in the DSL chain, e.g.

df = pd.read_csv().sort_values()...

As Far my experience in pandas I would like to answer.

The 'inplace=True' argument stands for the data frame has to make changes permanent eg.

    df.dropna(axis='index', how='all', inplace=True)

changes the same dataframe (as this pandas find NaN entries in index and drops them). If we try

    df.dropna(axis='index', how='all')

pandas shows the dataframe with changes we make but will not modify the original dataframe 'df'.

If you don't use inplace=True or you use inplace=False you basically get back a copy.

So for instance:

testdf.sort_values(inplace=True, by='volume', ascending=False)

will alter the structure with the data sorted in descending order.

then:

testdf2 = testdf.sort_values( by='volume', ascending=True)

will make testdf2 a copy. the values will all be the same but the sort will be reversed and you will have an independent object.

then given another column, say LongMA and you do:

testdf2.LongMA = testdf2.LongMA -1

the LongMA column in testdf will have the original values and testdf2 will have the decrimented values.

It is important to keep track of the difference as the chain of calculations grows and the copies of dataframes have their own lifecycle.