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Python tutorials for beginners

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Learn Data Science by completing interactive coding challenges and watching videos by expert instructors.

In this section you will learn everything you need to know about python, each lesson expands on the previous one, so i recommend to start sequentially.

Python Excel Tutorial: The Definitive Guide

This package is generally recommended if you want to read and write .xlsx, xlsm, xltx, and xltm files.

The workbook with the data that you’re trying to get in Python has the following sheets: The load_workbook() function takes, as you can see, the filename as an argument and return a workbook object, which represents the file.

Pass the row and the column arguments and add values to these arguments that correspond to the values of the cell that you want to retrieve and, of course, don’t forget to add the attribute value: Note that if you don’t specify the attribute value, you’ll get back <Cell Sheet3.B1>, which doesn’t tell you anything about the value that is contained within that particular cell.

The two functions already state more or less what you can retrieve by using them, but for clarity it’s best to make them explicit: while you can retrieve the letter of the column with the former, you can do the reverse or get the index of a column when you pass a letter to the latter.

Note again how the selection of the area is very similar to selecting, getting and indexing list and NumPy array elements, where you also use square brackets and a colon : to indicate the area of which you want to get the values.

To make the above explanation and code visual, you might want to check out the result that you’ll get back once the loop has finished: Lastly, there are some attributes that you can use to check up on the result of your import, namely max_row and max_column.

You can use the DataFrame() function from the Pandas package to put the values of a sheet into a DataFrame: If you want to specify headers and indices, you need to add a little bit more code: Next, you can start manipulating the data with all the functions that the Pandas package has to offer.

The openpyxl package offers you high flexibility on how you write your data back to Excel files, changing cell styles or using the write-only mode, which makes it one of the packages that you definitely need to know when you're often working with spreadsheets.

Reading and Writing Files in Python Tutorial

Flat files are data files that contain records with no structured relationships between the records and there's also no structure for indexing, like you typically find it in relational databases.

If the path is in current working directory, you can just provide the filename, just like in the following examples: If the file resides in a directory other than that, you have to provide the full path with the file name: Make sure file name and path given is correct, otherwise you'll get a FileNotFoundError: You can catch the exception with a try-finally block: Access modes define in which way you want to open a file, you want to open a file for read only, write only or for both.

Take a look at the following table: As you have seen in the first section, there are two types of flat files and this is also why there's also an option to specify in which format you want to open file, such as text or binary.

Create a file as below: Let's see what each read method does: The read() method just outputs the entire file if number of bytes are not given in the argument.

To set the cursor at the beginning, you can use the seek() method of file object: The tell() method of file object tells at which byte the file cursor is located.

In seek(offset,reference_point) the reference points are 0 (the beginning of the file and is default), 1 (the current position of file) and 2 (the end of the file).

Let's try out passing another reference point and offset and see the output: Note the use of .seek() and .truncate(): the argument in .truncate() is 5 that says that truncate the file till 5 bytes of text are left.

Square brackets are used to define arrays in more complex JSON files, as you can see in the following excerpt: Note that JSON files can hold different data types in one object as well!

If you have an object x, you can view its JSON string representation with a simple line of code: To write the JSON in a file, you can use the .dump() method: Note: that it is a good practice to use with-open method to open a file because it closes the file properly if any exception is raised on the way.

Buffering is useful when you don't know the size of file you are working with, if the file size is greater than computer memory then processing unit will not function properly.

Optionally, you can pass an integer to buffering to set the buffering policy: When you don’t specify any policy, the default is: Note that if you are using all arguments in the order that is specified in open(file, mode='r', buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None), you don't need to write argument name!

Note that universal newlines are a manner of interpreting text streams in which all of the following are recognized as ending a line: the Unix end-of-line convention '\n', the Windows convention '\r\n', and the old Macintosh convention '\r'.

Note also that os.linesep returns the system's default line separator: encoding represents the character encoding, which is the coding system that uses bits and byte to represent a character.

Now, try out the following code for these: If closefd is False and a file descriptor, rather than a filename was given, the underlying file descriptor will be kept open when the file is closed.

If you have an integer file descriptor already open for a I/O channel you can wrap a file object around it as below: In this case filedes_object will close the underlying file object file.

Closing file will give OSError Bad file descriptor: To prevent closing the underlying file object, you can use closefd=False: Up until now, you have learned pretty much all about reading text files in Python, but as you have read many times throughout this tutorial, these are not the only files that you can import: there’s also binary files.

You can access each byte through iteration like below and it will return integer byte values (decimal of the 8-bit binary representation of each character) instead of byte strings:

The os module of Python allows you to perform Operating System dependent operations such as making a folder, listing contents of a folder, know about a process, end a process etc.

Comprehensive Guide to Learning Python for Data Analysis and Data Science

Python is widely used for data analysis and you might have considered learning it yourself (if not, or if you’re still looking for that bit of extra motivation to get started, see why you should be learning Python below).

We will discuss steps you should take for learning Python accompanied with some essential resources, such as the free Python for Data Analysis courses and tutorials from DataCamp as well as reading and learning materials.

There are good reasons why Python is being adopted so widely by computer scientists, and why it’s a data analysis tool of choice for so many, the main one being the ease of learning and using Python.

The most convenient way to go about this is to download the free Anaconda package from Continuum Analytics, as it contains the core Python language, as well as all of the essential libraries including NumPy, Pandas, SciPy, Matplotlib, and IPython.

This free course consist of video tutorials and interactive in browser exercises and is a great way to learn by doing, as opposed to simply reading concepts and looking at examples.

While this course is not about data, but rather programming with Python, it is a great way to both practice with Python syntax and gain exposure to programming concepts that will be useful to you when working with data.

Pandas Tutorial: DataFrames in Python

Pay attention to how the code chunks above select elements from the NumPy array to construct the DataFrame: you first select the values that are contained in the lists that start with Row1 and Row2, then you select the index or row numbers Row1 and Row2 and then the column names Col1 and Col2.

For data in the example above, you go and look in the rows at index 1 to end and you select all elements that come after index 1.

Note that the index of your Series (and DataFrame) contains the keys of the original dictionary, but that they are sorted: Belgium will be the index at 0, while United States will be the index at 3.

You can use the shape property or the len() function in combination with the .index property: These two options give you slightly different information on your DataFrame: the shape property will give you the dimensions of your DataFrame.

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