How to profile python code

By Kall | 08.07.2020

how to profile python code

cProfile How to profile your python code

Mar 24, How to Profile Python Code: Accurately Measure the Performance of your Software Step 1: Finish Writing and Debugging Your Code. While this step is basically a formality, its worth mentioning that you Step 2: Download or Fork my Profiler . Jun 17, To profile your code, youre gonna need to know how to time it. To do so, you can use something as simple as this: from time import time start = time() # your script here end = time() print(f'It took { end - start } seconds!'Author: Ehsan Khodabandeh.

A profile is a set of statistics that describes how often and for how long various parts of the program executed. These statistics can be formatted into reports via the pstats module. The Python standard library provides two different implementations of the same profiling interface:. Based on lsprofcontributed by Brett Rosen and Ted Czotter. Originally designed and written by Jim Roskind. The profiler how to become a home appraiser in tennessee are designed to provide an execution profile for a given program, not for benchmarking purposes for that, there is timeit for reasonably accurate results.

This particularly applies to benchmarking Python code against C code: the profilers introduce overhead for Python code, but not for C-level functions, and so the C code would seem faster than any Python one.

Use profile instead of cProfile if the latter is not available on your system. The above action would run re. The first line indicates that calls were monitored. Of those calls, were primitivemeaning that the call was not induced via recursion. The next line: Ordered by: standard nameindicates that the text string in the far right column was used to sort the output.

The column headings include:. This figure is accurate even for recursive functions. The second value is the number of primitive calls and the former is the total number of calls. Note that when the function does not recurse, these two values are the same, and only the single figure is printed. Instead of printing the output at the end of the profile run, you can save the results to a file by specifying a filename to the run function:. The pstats.

Stats class reads profile results how to find your descendant sign a file and formats them in various ways. The files cProfile and profile can also be invoked as how to profile python code script to profile another script. For example:. This only applies when -o is not supplied. New in version 3. You might try the following sort calls:. The first call will actually sort the list by function name, and the second call will print out the statistics.

The following are some interesting calls to experiment with:. This sorts the profile by cumulative time in a function, and then only prints the ten most significant lines. If you want to understand what algorithms are taking time, the above line is what you would use. If you were looking to see what functions were looping a lot, and taking a lot of time, you would do:. As one final example, you could try:. This line sorts statistics with a primary key of time, and a secondary key of cumulative time, and then prints out some of the statistics.

If you wondered what functions called the above functions, you could now p is still sorted according to the last criteria do:. Invoked as a script, the pstats module is a statistics browser for reading and examining profile dumps.

It has a simple line-oriented interface implemented using cmd and interactive help. Both the profile and cProfile modules provide the following functions:. This function takes a single argument that can be passed to the exec function, and an optional file name.

In all cases this routine executes:. If no file name is present, then this function automatically creates a Stats instance and prints a simple profiling report. If the sort value is specified, it is passed to this Stats instance to control how how to profile python code results are sorted.

This function is similar to runwith added arguments to supply the globals and locals dictionaries for the command string. This routine executes:. This class is normally only used if more precise control over profiling is needed than what the cProfile. A custom timer can be supplied for measuring how long code takes to run via the timer argument.

This must be a function that returns a single number representing the current time. If the number is an integer, the timeunit specifies a multiplier that specifies the duration of each unit of time. For example, if the timer returns times measured in thousands of seconds, the time unit would be.

Directly using the Profile class allows formatting profile results without writing the profile data to a file:. The Profile class can also be used as a context manager supported only in cProfile module. Changed in version 3. Start collecting profiling data. Only in cProfile. Stop collecting profiling data.

Create a Stats object based on the current profile and print the results to stdout. Profile the cmd via exec. Profile the cmd via exec with the specified global and local environment. If the interpreter is terminated e. Analysis of the profiler data is done using the Stats class. Output will be printed to the stream specified by stream. The file selected by the above constructor must have been created by the corresponding version of profile or cProfile. To be specific, there is no file compatibility guaranteed with future versions of this profiler, and there is no compatibility with files produced by other profilers, or the same profiler run on a different operating system.

If several files are provided, all the statistics for identical functions will be coalesced, so that an overall view of how to tell someone is gay processes can be considered in a single report. If additional files need to be combined with data in an existing Stats object, the add method can be used. Instead of reading the profile data from a file, a cProfile. Profile or profile.

Profile object can be used as the profile data source. Stats objects have the following methods:. This method for the Stats class removes all leading path information from file names. It is very useful in reducing the size of the printout to fit within close to 80 columns. This method modifies the object, and the stripped information is lost.

This method of the Stats class accumulates additional profiling information into the current profiling object. Its arguments should refer to filenames created by the corresponding version of profile. Statistics for identically named re: file, line, name functions are automatically accumulated into single function statistics. Save the data loaded into the Stats object to a file named filename. The file is created if it does not exist, and is overwritten if it already exists.

This is equivalent to the method of the same name on the profile. Profile and cProfile. Profile classes. This method modifies the Stats object by sorting it according to the supplied criteria. The argument can be either a string or a SortKey enum identifying the basis of a sort example: 'time''name'SortKey. The SortKey enums argument have advantage over the string argument in that it is more robust and less error prone.

When more than one key is provided, then what can you do for swollen feet when pregnant keys are used as secondary criteria when there is equality in all keys selected before them.

FILE will sort all the entries according to their function name, and resolve all ties identical function names by sorting by file name. For the string argument, abbreviations can be used for any key names, as long as the abbreviation is unambiguous. Note that all sorts on statistics are in descending order placing most time consuming items how to install braided hose endswhere as name, file, and how do i stop clover from taking over my lawn number searches are in ascending order alphabetical.

The subtle distinction between SortKey. NFL and SortKey. STDNAME is that the standard name is a sort of the name as printed, which means that the embedded line numbers get compared in an odd way. For example, lines 3, 20, and 40 would if the file names were the same appear in the string order 20, 3 and In contrast, SortKey.

NFL does a numeric compare of the line numbers. For backward-compatibility reasons, the numeric arguments -101and 2 are permitted. They are interpreted as 'stdname''calls''time'and 'cumulative' respectively.

If this old style format numeric is used, only one sort key the numeric key will be used, how to profile python code additional arguments will be silently ignored.

This method for the Stats class reverses the ordering of the basic list within the object. Note that by default ascending vs descending order is properly selected based on the sort key of choice. This method for the Stats class prints out a report as described in the profile.

The arguments provided if any can be used to limit the list down to the significant entries. Initially, the list is taken to be the complete set of profiled functions. Each restriction is either an integer to select a count of linesor a decimal fraction between 0.

1. Why do we need Python Profilers ?

Jul 21, There are multiple ways and a number of helpful packages available to profile the Python code. I recommend using the cProfile library. It is a C extension and is Author: Farhad Malik. How to Profile Python Code using cProfile & profile cProfile - Its an extension of C hence adds reasonably low overhead to actual code when performing profiling. profile - Its totally written in python hence adds a reasonable amount of overhead to actual code when performing. Mar 20, Profiling Your Code with cProfile. Profiling code with cProfile is really quite easy. All you need to do is import the module and call its run function. Lets look at a simple example: Here we import the hashlib module and use cProfile to profile the creation of an MD5 hash.

The profiling of code is a process where you find out which particular part of the code is taking how much time. This can be useful when optimizing code which is taking more time than expected. The profiling can be of time or even it can be of memory as well sometimes. There are profilers which let us know memory usage at each statement of code.

We'll be concentrating on profilers for measuring the time of various parts of code as a part of this tutorial. The python has two profilers for this purpose. Both of the above-mentioned profilers provide deterministic profiling of python code. The deterministic profiling monitors each statement, function call, exceptions, etc. The other profiling technique is called statistical profiling technique which randomly samples instruction being executed and records time to determine where more time is being spent.

As statistical profiling does not monitor each statement, function calls, etc and randomly samples which part to monitor, it adds less overhead of profiling a task.

As a part of this tutorial, we'll be explaining how to use cProfile and profile modules to profile Python code. We'll also cover the pstats module which can be used to format the output of profilers.

We'll be explaining the usage of both libraries with few examples. If you are interested in learning about other profilers available in python then please feel free to go through the references section to check out tutorials on other profilers. The cProfile has a function named run which accepts any python statement.

It'll pass that python statement to exec function to execute and generate profiling results as well. Below we are simply running a loop time and generating a random numbers between each time. We keep all random numbers in a list and then sum them up. We have passed this statement to the run method of cProfile to see profiling results of where time is getting spent.

We can understand from the above output that a total of function calls was made. The Ordered by: standard name indicates that the table displayed is sorted by string name printed at last. The run function also provides another parameter named filename to which we can provide filename where the output of profiling will be saved. We'll load this file later using the pstats module. The run method explained in the previous example has the limitation that we need to pass a single statement to it which needs to be profiled.

If we want more control over which part of the code should be profiled then Profile constructor is a better option. It has a function named enable and disable which lets us enable and disable profiler so that only a particular part of the code can be profiled. This can help us avoid unnecessarily profile whole code when we need to profile only certain parts of code. We are adding the output of function calls to the arr list and returning it as well. This will prevent the profiling of other parts of the function.

From python 3. We have then returned the profile object as well which can be used to print the results of profiling. The python also lets us execute profiler from the command line directly on the python file. We have put the code of this example into a file named addition. Once we have put code to a python file, we can use the below command in the command prompt or Unix shell to profile that python file. We can even save the output of profiling to file when performing profiling from the command line as explained below.

We can also sort the output of profiling based on a particular column of the table by giving that column name with -s parameter. It lets us load the profiler results file using it's Stats constructor.

We can pass the Stats constructor a single file or the list of files containing profiling results. It has been explained below with simple examples. The pstats module also provides us with various methods that let us modify the output of profiling. The list of column names is stored in pstats. Below we have sorted the results of profiling based on cumulative time and then have taken the first 3 results from it when printing. It'll multiply a total number of rows by that float number and print only that many rows.

It'll remove all leading pathname information from filename in the last column of profiling results. This can help us if the result has long path information present which is truncating the output to the next line exceeding monitor limit. The Stats object has two other methods which can be very useful to understand which function is calling which other function.

Our fifth example explains the profiling of recursive function. We have written a recursive function below which returns nth Fibonacci number.

We have profiled this function by calling it with argument We can see from the profiled output that there were total function calls and 4 primitive calls. We can even notice from ncalls of the first line that Fibonacci function has total calls and 1 primitive call. The second profiler that we'll explain as a part of this tutorial is profile. We'll import it to get started. The profile also has the same methods as that of cProfile.

Our first example of profile is exactly the same as that of cProfile. We have used the run method of profile to profile python statement which sums up random integers between The output of profile is formatted exactly the same way as that of the output of cProfile. Our second example explains how to use the Profile constructor to profile code. The profiler available through the profile module subtracts a small constant from each event during profiling hence removing extra overhead added to the profiling process by the profiler.

The default value of this constant is 0. The process of removing this overhead of profiling is commonly referred to as calibration. We can find out a better constant for a particular system by executing the below code.

Below code executes python statements statements with a profiler and without a profiler. It then finds our overhead as a float. It performs this process five times and adds overhead found each time to a python list. We then take an average to find out final float which can be used as a constant that will be used by the profiler to remove extra overhead added by the profiler.

Once we have found out bias constant that we would like to remove for each event when using profiling, we can set it in profiler by setting the bias parameter when creating the Profile object as explained below. We can see from the above results that time taken when using a calibrated profiler is a little less than when used without calibrating. The jupyter notebook provides us with line and cell magic commands which can be used for profiling in notebooks.

Below we are first profiling the addition. This ends our small tutorial explaining how to profile Python code using cProfile and profile profilers. Please feel free to let us know your views in the comments section. He possesses good hands-on with Python and its ecosystem libraries.

Apart from his tech life, he prefers reading autobiographies and inspirational books. CoderzColumn is a place developed for the betterment of development. Updated On : Oct, profiling, cProfile, profile. In [1]:. In [2]:. The meaning of each column in the table is explained below: ncalls : It represents a number of times that function was called. It can have two numbers separated by a slash in case of recursive calls.

The first number, in that case, represents total calls including recursive calls and the second number represents primitive calls excluding recursive calls. In [3]:. In [4]:. In [5]:. In [6]:. In [66]:. In [10]:. In [11]:. In [12]:.

TIME , pstats. In [13]:. In [14]:. In [15]:. In [16]:. In [17]:. Random listing order was used Function was called by In [18]:. Random listing order was used Function called In [19]:.

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