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Detailed explanation of Python processing csv file examples

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Release: 2018-05-17 16:24:58
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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))
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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'], ...]
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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)
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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)
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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)
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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
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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)
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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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