Python handles CSV file instances in detail

Python handles CSV files

CSV (comma-separated values) is a comma-separated value that can be opened for viewing in Excel. Because it is plain text, any editor can also be opened. Unlike the Excel file, the CSV file:

• Value has no type, all values are string

• Styles such as font color cannot be specified

• Cannot specify a cell's width height, cannot merge cells

• No More worksheets

• Cannot embed image chart

Separates two cells in a CSV file , as delimiters. This a,,c means that a c there is a blank cell between the cell and the cell. And so on

Not every comma represents the demarcation between cells. So even if the CSV is a plain text file, it is also insisted on using specialized modules for processing. Python has a CSV module built in. Let's take a look at a simple example.

Reading data from a CSV file

Import csvfilename = ' F:/jupyter notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv ' with open (filename) as F:    reader = Csv.reader (f) Print (list (reader))

dataCan not print directly, list (data) outermost is a list, the inside of each row of data in a list, a bit like this

[[' Name ', ' age '], [' Bob ', ' + '], [' Tom ', ' 23 '], ...]

So we can access Bob's age in this way reader[1][1] , traversing the for loop as follows

Import csvfilename = ' F:/jupyter notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv ' with open (filename) as F:    reader = Csv.reader (f) for row in reader:# line number starting with 1 print (Reader.line_num, row)

It is important to note that reader can only be traversed once. Because reader is an iterative object, you can use a next method to get one row at a time.

Import csvfilename = ' F:/jupyter notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv ' with open (filename) as F: Reader    = Csv.reader (f) # reads a line, and the following reader does not already have the line Head_row = Next (reader) for row in reader:# line number starting with 2 print (Reader.line_ num, Row)

Write data to a CSV file

There is reader to read, and of course writer can write. Write one line at a time, and write more than one line at a time.

Import csv# use numbers and string numbers to Datas = [[' Name ', ' age '], [' Bob ', ' + '], [' Tom ', ' + '         ], ['        Jerry ', ']]with ' open (' Example.csv ', ' W ', newline= ') as f:    writer = Csv.writer (f) for row in datas:        writer.writerow (Row)        # You can also write multiple lines of writer.writerows (datas)

If not specified newline='' , a blank line is written to each write line. The above code generates the following.

name,agebob,14tom,23jerry,18name,agebob,14tom,23jerry,18

Dictreader and Dictwriter objects

Using Dictreader, you can manipulate the data like a dictionary, and use the first row of the table (usually the header) as the key. Use key to access the data for that key in the row.

Import csvfilename = ' F:/jupyter notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv ' with open (filename) as F:    reader = csv. Dictreader (f) for row in reader:# Max Temperaturef is a data for the first row of the table, as Keymax_temp = row[' Max temperaturef ']print (max_temp)

Using the Dictwriter class, you can write data in the form of a dictionary, and the same key is also the header (the first row of the table).

Import csvheaders = [' name ', ' age ']datas = [{' Name ': ' Bob ', ' age ': +},        {' name ': ' Jerry ', ' age ': ', '},        {' name ': ' Tom ', ' age ':        ]with open (' Example.csv ', ' W ', newline= ') as f:# header is passed here as the first line of data writer = csv. Dictwriter (f, headers)    Writer.writeheader () for row in datas:        writer.writerow (Row)        # You can also write multiple lines of writer.writerows (datas)