csv-wizard 0.4.0

Handy extension to Python csv standard library package

CSV Divider

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Import a CSV file by only giving the CSVParser class a string containing the full name of the file without the .csv part.

new_file = CSVParser("my_file")

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Handling decoding/encoding on a file.

All methods have an optional encoding argument. If left empty, csv_parser will attempt to automatically figure out the encoding, however, if a UnicodeDecodeError or UnicodeEncodeError error are raised, the encoding should be specified manually. The encoding parameter accepts strings.

> file1.get_row_count(encoding='utf-8') 

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Getting the characters that define the CSV file's layout. The get_dialect method.

Calling this method on a CSVParser object returns it's dialect property. The dialect property contains:

• lineterminator
• quoting
• doublequote
• delimiter
• quotechar
• skipinitialspace

new_file.get_dialect().delimiter

new_file.get_dialect().lineterminator

new_file.get_dialect().quoting

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The create(). An easier way to create a file, optionally specify a path.

The create method receives a string as required argument and it creates a new CSV file using that string.

Specifing a path to create the folder in

Optionally, this method receives a path argument to specify a folder to create the file in. If this is not specified, the file will be created in the current directory.

For security reasons, when using this method in a client-facing app feature, it is recommended to limit the access to parent folders, to avoid the creation of files in vulnerable locations.

Predefined paths

There are three predefined global variables you can use:

• CURRENT_DIR to get the current directory
• CURRENT_PARENT_DIR to get the parent directory of the current location
• ABSOLUTE_PATH to get the current absolute path

CSVParser.create(name="new-file", path=CURRENT_PARENT_DIR)

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Getting the headers row. The get_headers() method.

Returns only the first line on the CSV file, which is presumed to be the one containing the headers.

new_file.get_headers() # ["Name", "Email"]

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Getting the total number of rows in the CSV file. The get_row_count() method.

This method is used internally by other methods to be know how many rows a file has. It returns the number of rows that the file contains, without taking into account the headers row.

new_file.get_row_count() # 45

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Getting the content of all rows in the CSV file. The get_all_rows() method.

Returns a list containing the content of each individual row, including the header row. This is different from get_row_count() in that it returns the complete content of the rows, not only how many there are.

new_file.get_all_rows() # [['John', '[e-mail]'], ['Anne', '[e-mail]]]

This method accepts an optional argument called row_structure. get_all_rows() will always return a list containin all the rows. But, by default, all the rows themselves will be inside lists. With the row_structure argument you can specify that the rows be enclosed in tuples or sets. Dicts won't work.

get_all_rows(row_structure='tuple') # [('John', '[e-mail]'), ('Anne', '[e-mail])]

get_all_rows(row_structure='set') # [{'John', '[e-mail]'}, {'Anne', '[e-mail]}]

get_all_rows(row_structure='dict') # ['ERROR => Unsupported type: dict']

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Slicing the CSV file in half. The slice() method.

Returns a dictionary with two key-value pairs. The keys are called "First_Half" and "Second_Half", and they respectively contain each half of the file's rows. If the total nomber of rows is odd, the first half will contain one more row to compensate.

Neither half will contain the header column. That must be obtained with the get_headers() method.

new_file.slice() # { 'First_Half':[], 'Second_Half':[] }

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Dividing the CSV file in n number of parts. The divide() method.

Returns a list containing smaller lists that in themselves contain equal amounts of elements (one for every part asked for in the method call). If the number of parts is not even, the amount of elements will be spread in the most equitative way possible.

The headers are not included on any of the returned lists. That must be obtained with the get_headers() method.

new_file.divide(3) # [[1,2,3,4,5],[6,7,8,9,10],[11,12,13,14,15]]

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Adding a headers row to a file. The write_headers() method.

This method takes a list as the only argument. It expects a list with the same format as outputed by the get_headers() method. It can be used on a file with data as well as an empty one.

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Overwriting the whole file with a new set of rows. The overwrite() method.

The overwrite() method taks as argument an object with a structure of list[list[str]]. It is the kind of object returned by the reading methods in the library like slice() or divide(). The file is truncated (all the content is deleted) and the object is usedd to add new rows to the file.

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Adding new rows without deleting the existing ones. The append_rows() method.

It takes an argument of the same kind of object as the overwrite() method, that is, a list[list[str]]. It adds the rows in that object to the file below the already existing rows.

Appending the set of rows at the top of the file.

The append_rows() method accepts an optional boolean argument called append_on_top. By default its set to False, which makes the method append the new rows below the existing rows.

If set to True, the append_on_top argument makes the method place the new rows on top of the file, just below the headers, moving down the existing rows.

After the rows are appended, they will be seen in reverse order on the file. The last element on the rows_object will be on top at the file.

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Getting the common rows between two instances of the CSVParser class. The find_common_rows() method.

The find_common_rows() method compares the complete set of rows of two instances of the CSVParser class and returns a list containing the rows that are present on both instances. One CSVParser instance is the one the method is called on, the other one is passed as an argument.

This method internally calls the get_all_rows() method, and it specifically asks fora row_structure of tuples. This is done for performance reasons. So the rows will be returned in the format [('col1', 'col2')].

file1 = CSVParser('fileNo1')
file2 = CSVParser('fileNo2')
file1.find_common_rows(file2)
> [(row1"), ("row2), ("row3)] 

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Finding the different rows between two instances of the CSVParser class. The find_different_rows() method.

The find_different_rows() method compares the complete set of rows of two instances of the CSVParser class and returns a list containing only the rows that are present on the firts file but not on the second. One CSVParser instance is the one the method is called on, the other one is passed as an argument.

This method internally calls the get_all_rows() method, and it specifically asks fora row_structure of tuples. This is done for performance reasons. So the rows will be returned in the format [('col1', 'col2')].

file1 = CSVParser('fileNo1')
file2 = CSVParser('fileNo2')
file1.find_different_rows(file2)
> [(row5"), ("row8), (row14)] 

file1 = CSVParser('fileNo1')
file2 = CSVParser('fileNo2')
file2.find_different_rows(file1)
> [(row1"), ("row4), (row34)] 

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Finding duplicate rows in a file. The get_duplicates() method.

When called on an instance of the CSVParser class, this method will return a dictionary with the following format: {'row_name': number_of occurrences}

file1 = CSVParser('fileNo1')
file1.get_duplicates()
> {"[' Alex ', '[e-mail]']": 2, "[' Adriana ', '[e-mail]']": 5}

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In case there's empty rows in the file. The delete_blanks() method.

Executing this method on a CSVParser instance will delete all blank rows from the CSV file. If there are many empty rows, the method may fail to delete them all in one run. If this happens, running it again should eventually delete them all.

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Usage warnings

1. When finding common rows or different rows between very large CSV files, keep in mind that execution time can be slower. To provide a frame of reference, while testing, comparing two files of a bit over 91000 rows, took between 1.7 and 2.1 seconds.
2. When comparing files, if they contain special characters like spanish tildes (eg. á, í), if the files' encoding differs, and on of them recognizes this characters but the other one doesn't, they will be read as different characters, thus being recognized as different rows.
3. When writing to an empty file (created manually or with the create method), an encoding must be specified.