Python从门到精通(五):文件处理-02-CSV文件处理（python能处理csv文件）

Python从门到精通(五):文件处理-02-CSV文件处理（python能处理csv文件）

解决CSV文件需要用到csv库，如果需要做分析需要引入pandas库。

一、读处理

--course.scv--CourseName,Score,Date,Time,Finished,TotalClasspython,0.5,6/1/2020,8:30am,3,30java,0.2,6/1/2020,9:30am,2,20php,0.25,6/1/2020,10:30am,5,10

import csvwith open('course.csv') as f: f_csv = csv.reader(f) header_list = next(f_csv) for row in f_csv: #这种用下标的方式比较容易出错 print(f'course name: {row[0]}, total class: {row[5]}')from collections import namedtuplewith open('course.csv') as f: f_csv = csv.reader(f) headings = next(f_csv) #所以建议把下载换成这种方式 Row = namedtuple('Row', headings) for r in f_csv: row = Row(*r) print(f'course name: {row.CourseName}, total class: {row.TotalClass}')#装数据读取到dict中import csvwith open('course.csv') as f: f_csv = csv.DictReader(f) for row in f_csv: print(f'dict read: {row}')

二、写处理

import csvfrom collections import namedtuple"""========写一个csv文件=========="""header_list = ['CourseName','Score','Date','Time','Finished','TotalClass']row_list = [('python', 0.5, '6/1/2020', '8:30am', 3, 30), ('java', 0.2, '6/1/2020', '9:30am', 2, 20), ('php', 0.25, '6/1/2020', '10:30am', 5, 10), ]with open('course.csv','w') as f: f_csv = csv.writer(f) f_csv.writerow(header_list) f_csv.writerows(row_list)#字典方式写header_list = ['CourseName','Score','Date','Time','Finished','TotalClass']row_list = [{'CourseName': 'python', 'Score': 0.5, 'Date': '6/1/2020', 'Time': '8:30am', 'Finished': 3, 'TotalClass': 30}, {'CourseName': 'java', 'Score': 0.2, 'Date': '6/1/2020', 'Time': '9:30am', 'Finished': 2, 'TotalClass': 20}, {'CourseName': 'php', 'Score': 0.25, 'Date': '6/1/2020', 'Time': '10:30am', 'Finished': 5, 'TotalClass': 10}, ]with open('course.csv','w') as f: f_csv = csv.DictWriter(f, header_list) f_csv.writeheader() f_csv.writerows(row_list)#以逗号分隔的csv文件处理with open('course.csv') as f: for line in f: row_list = line.split(',') print(f'split row: {row_list}')#以tab分隔的csv文件处理with open('course.csv') as f: f_tsv = csv.reader(f, delimiter='\t') for row in f_tsv: print(f'read row: {row}')#处理非法表头import rewith open('course.csv') as f: f_csv = csv.reader(f) header_list = [re.sub('[^a-zA-Z_]', '_', h) for h in next(f_csv)] Row = namedtuple('Row', header_list) for r in f_csv: row = Row(*r) print(f'named tuple read: {row}')col_types = [str, float, str, str, int, int]with open('course.csv') as f: f_csv = csv.reader(f) header_list = next(f_csv) for row in f_csv: row = tuple(convert(value) for convert, value in zip(col_types, row)) print(f'row read: {row}')

三、列格式处理

#默认的csv全是字符串类型的，如果需要做其它转换，需手动进行print('Reading as dicts with type conversion')field_types = [ ('Score', float), ('Finished', float), ('TotalClass', int) ]with open('course.csv') as f: for row in csv.DictReader(f): row.update((key, conversion(row[key])) for key, conversion in field_types) print(f'update row: {row}')

四、数据统计

import pandasrats = pandas.read_csv('rats.csv', skip_footer=1)print(rats)print(rats['Current Activity'].unique())crew_dispatched = rats[rats['Current Activity'] == 'Dispatch Crew']print(len(crew_dispatched))print(crew_dispatched['ZIP Code'].value_counts()[:10])dates = crew_dispatched.groupby('Completion Date')print(len(dates))date_counts = dates.size()date_counts[0:10]date_counts.sort()print(date_counts[-10:])

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