Effective Python: 59 Specific Ways to Write Better Python by Brett Slatkin

By Brett Slatkin

It's effortless to begin writing code with Python: that's why the language is so immensely well known. notwithstanding, Python has distinctive strengths, charms, and expressivity that may be difficult to know first and foremost -- in addition to hidden pitfalls which can simply journey you up if you happen to aren't conscious of them. potent Python can help you harness the complete strength of Python to jot down particularly strong, effective, maintainable, and well-performing code. using the concise, scenario-driven variety pioneered in Scott Meyers's best-selling powerful C++, Brett Slatkin brings jointly fifty nine Python top practices, suggestions, shortcuts, and life like code examples from professional programmers.

Through life like examples, Slatkin uncovers little-known Python quirks, intricacies, and idioms that powerfully impression code habit and function. You'll find out how to select the best and potent strategy to accomplish key projects while a number of suggestions exist, and the way to put in writing code that's more uncomplicated to appreciate, hold, and improve.

Drawing on his deep realizing of Python's services, Slatkin bargains sensible recommendation for every significant sector of improvement with either Python 3.x and Python 2.x. assurance includes:

Algorithms
Objects
Concurrency
Collaboration
integrated modules
creation techniques
And more

Each part includes particular, actionable instructions geared up into goods, every one with rigorously worded suggestion supported via distinct technical arguments and illuminating examples. utilizing potent Python, you could systematically increase the entire Python code you write: now not by means of blindly following ideas or mimicking incomprehensible idioms, yet through gaining a deep realizing of the technical the reason why they make feel.

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If the format of the dates is not very strange, you can try the autodetection routines that specify the column that contains the date data. csv', parse_dates=[0]) fake_dataset Out: Date Temperature_city_1 Temperature_city_2 Which_destination 0 2014-09-10 80 32 40 1 1 2014-09-11 100 50 36 2 2 2014-09-12 102 55 46 1 3 2014-09-13 60 20 4 2014-09-14 60 NaN 32 3 5 2014-09-15 NaN 57 42 2 35 Temperature_city_3 3 Now, to get rid of the missing data that is indicated as NaN, replace them with a more meaningful number (let's say 50 Fahrenheit, for example).

In line [3], we use the magic function timeit to a Python assignment (x=100). The timeit function runs this instruction many times and stores the computational time needed to execute it. Finally, it prints the average time that was taken to run the Python function. We complete the overview with a list of all the possible IPython special functions by running the helper function quickref, as shown in line [4]. [ 14 ] Chapter 1 As you noticed, each time we use IPython, we have an input cell and optionally, an output cell, if there is something that has to be printed on stdout.

The pandas library offers similar functionality (and functions) to load MS Excel, HDFS, SQL, JSON, HTML, and Stata datasets. Since they're not used often, the understanding of how one can load and handle them is left to you, who can refer to the verbose documentation available on the website. A basic example on how to load an SQL table is available in the code that accompanies the book. Finally, pandas DataFrames can be created by merging series or other list-like data. '}) my_own_dataset Out: 0 Col1 Col2 Col3 Col4 0 1 1 Hello World!

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