Understanding inplace True

Question

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

User · Answer

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

User · Answer

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

User · Answer

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

User · Answer

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

User · Answer

Save it to the same variable  data  column01   where data  column01   lt  5  inplace True   Save it to a separate variable  data  column02     data  column01   where data  column1   lt  5   But  you can always overwrite the variable  data  column01     data  column01   where data  column1   lt  5   FYI  In default inplace   False

User · Answer

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

User · Answer

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   quot color intensity quot    quot ci quot         assign color filter lambda x  np where  x hue  gt  1   amp   x ci  gt  7   1  0        query  quot alcohol  gt  14 and color filter    1 quot        sort values  quot alcohol quot   ascending False       reset index drop True       loc      quot alcohol quot    quot ci quot    quot hue quot       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

User · Answer

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

User · Answer

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

User · Answer

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

User · Answer

In pandas  is inplace   True considered harmful  or not  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    gt  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

[python] Understanding inplace=True

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