Python processes csv files

CSV (Comma-Separated Values) is comma-separated values, which can be opened and viewed with Excel. Since it is plain text, it can be opened by any editor. Unlike Excel files, in CSV files:

• The values ​​have no type, all values ​​are strings

• You cannot specify styles such as font color

• Cannot specify the width and height of cells, and cannot merge cells

• There are no multiple worksheets

• Cannot embed image charts

In the CSV file, use , as delimiters to separate two cells. Like this a,,c means there is a blank cell between cell a and cell c. So on and so forth.

Not every comma represents a demarcation between cells. So even if the CSV is a plain text file, insist on using a dedicated module for processing. Python has a built-in csv module. Let’s look at a simple example first.

Read data from CSV file

import csv

filename = 'F:/Jupyter Notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv'with open(filename) as f:
    reader = csv.reader(f)print(list(reader))

data cannot be printed directly. The outermost layer of list(data) is list, and each row of data in the inner layer is in a In the list, it looks a bit like this

[['name', 'age'], ['Bob', '14'], ['Tom', '23'], ...]

So we can access Bob’s age like thisreader[1][1], and traverse it in the for loop as follows

import csv

filename = 'F:/Jupyter Notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv'with open(filename) as f:
    reader = csv.reader(f)for row in reader:# 行号从1开始print(reader.line_num, row)

To Note that the reader can only be traversed once. Since reader is an iterable object, you can use the next method to get one row at a time.

import csv

filename = 'F:/Jupyter Notebook/matplotlib_pygal_csv_json/sitka_weather_2014.csv'with open(filename) as f:
    reader = csv.reader(f)# 读取一行，下面的reader中已经没有该行了head_row = next(reader)for row in reader:# 行号从2开始print(reader.line_num, row)

Write data to the csv file

There are readers that can read, and of course there are writers that can write. You can write one line at a time or multiple lines at a time.

import csv# 使用数字和字符串的数字都可以datas = [['name', 'age'],
         ['Bob', 14],
         ['Tom', 23],
        ['Jerry', '18']]with open('example.csv', 'w', newline='') as f:
    writer = csv.writer(f)for row in datas:
        writer.writerow(row)        # 还可以写入多行writer.writerows(datas)

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

name,age
Bob,14
Tom,23
Jerry,18
name,age
Bob,14
Tom,23
Jerry,18

DictReader and DictWriter objects

Using DictReader, you can operate data like a dictionary, using the first row of the table (usually the header) as the key. Use key to access the data corresponding to that key in the row.

import csv

filename = '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是表第一行的某个数据，作为keymax_temp = row['Max TemperatureF']print(max_temp)

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

import csv

headers = ['name', 'age']

datas = [{'name':'Bob', 'age':23},
        {'name':'Jerry', 'age':44},
        {'name':'Tom', 'age':15}
        ]with open('example.csv', 'w', newline='') as f:# 标头在这里传入，作为第一行数据writer = csv.DictWriter(f, headers)
    writer.writeheader()for row in datas:
        writer.writerow(row)        # 还可以写入多行writer.writerows(datas)

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