Python CSV Toolkit
整理了一些个人在利用python处理csv文件时经常用到的一些自定义方法，放在这里主要方便自己查阅，也可以给其他人做参考
目录
输出CSV文件某列的匹配/不匹配的记录
调整csv文件的列的顺序
CSV转换器
抽取特定列
除去完全重复的记录
根据列名排序
键值互换
输出CSV文件某列的匹配/不匹配的记录
主要用于从csv文件中抽取出匹配特定列的特定字段集合的记录，比如现有这么一个csv文件（表格化后）
| name | age | sex |
|---|---|---|
| Danny | 24 | male |
| Daisy | 23 | female |
| Lancelot | 23 | unknown |
| Lydia | 21 | female |
| ... | ... | ... |
需要输出其中age为23的记录到新的csv文件，则我们可以先把23这么个关键词用一个列表收集起来，然后通过下列代码从csv文件中找出所有符合条件的记录并输出
import sys
import csv
# try to fix '_csv.Error: field larger than field limit (131072)'
csv.field_size_limit(sys.maxint)
# write to common csv file with delimiter ','
# output the rows with matched id in id_list to a new csv file
def csv_match(id_list,key,input_file,output_file):
with open(input_file, 'rb') as f:
reader = csv.DictReader(f)
rows = [row for row in reader if row[key] in set(id_list)]
header = rows[0].keys()
with open(output_file, 'w') as f:
f.write(','.join(header))
f.write('\n')
for data in rows:
f.write(",".join(data[h] for h in header))
f.write('\n')调用的时候：
lst=['23']
csv_match(lst,'age','in.csv','out.csv')key为需要匹配的列名，另外我们也可以提取不符合该条件的记录，‘取个反’就行了
# output the rows with not matched id in id_list to a new csv file
def csv_not_match(id_list, key, input_file, output_file):
with open(input_file, 'rb') as f:
reader = csv.DictReader(f)
rows = [row for row in reader if not row[key] in set(id_list)]
header = rows[0].keys()
with open(output_file, 'w') as f:
f.write(','.join(header))
f.write('\n')
for data in rows:
f.write(",".join(data[h] for h in header))
f.write('\n')对于需要判断csv文件中多个列的值的情况，只需修改对应的判别条件和传入参数情况即可
# output the rows with matched key1 or key2 in refer_list to a new csv file
# @params
# refer_list: the list referred to
# key,key2: column name of csv file to check the value in the refer_list or not
def csv_match2(refer_list, key1, key2, input_file, output_file):
with open(input_file, 'rb') as f:
reader = csv.DictReader(f)
rows = [row for row in reader if (row[key1] in set(refer_list)) or (row[key2] in set(refer_list))]
header = rows[0].keys()
with open(output_file, 'w') as f:
f.write(','.join(header))
f.write('\n')
for data in rows:
f.write(",".join(data[h] for h in header))
f.write('\n')
调整csv文件的列的顺序
有时候我们输出的或者拿到的csv文件的列的顺序不够‘人性化’，为了让我们看起来更加直观，更舒服一点，我们可以按照我们的需要调整列的顺序
import csv
# reorder the column of the csv file to what you want
def csv_reorder(in_file, out_file,lst_order):
with open(in_file, 'rb') as infile, open(out_file, 'wb') as outfile:
fieldnames=lst_order
writer = csv.DictWriter(outfile, fieldnames=fieldnames)
writer.writeheader()
for row in csv.DictReader(infile):
writer.writerow(row)其中lst_order为我们需要的列名顺序，用list存储，举个例子
season_id,league_name,league_size
2003,scottish-premiership,12
2016,1-hnl,10
2004,alka-superligaen,12
2006,allsvenskan,14
1992,premier-league,22
...现在我们想调整他的顺序，按照league_name,season_id,league_size的顺序重新组合一下
则调用
lst_order = ['league_name','season_id','league_size']
csv_reorder('leagues_size.csv', 'leagues_size_new.csv', lst_order)得到结果
league_name,season_id,league_size
scottish-premiership,2003,12
1-hnl,2016,10
alka-superligaen,2004,12
allsvenskan,2006,14
premier-league,1992,22
...CSV转换器
这个主要是用来进行csv和python的一些内置的容器例如list,dict之类的转换，包括一些特殊的多级字典，或者是嵌套列表的字典等等，这里只是把他们打个包放在一起，具体的可以参照我之前写的一篇文章
import csv
#---------------------------------------------------csv <--> dict--------------------------------------------
# convert csv file to dict
# @params:
# key/value: the column of original csv file to set as the key and value of dict
def csv2dict(in_file,key,value):
new_dict = {}
with open(in_file, 'rb') as f:
reader = csv.reader(f, delimiter=',')
fieldnames = next(reader)
reader = csv.DictReader(f, fieldnames=fieldnames, delimiter=',')
for row in reader:
new_dict[row[key]] = row[value]
return new_dict
# convert csv file to dict(key-value pairs each row)
# default: set row[0] as key and row[1] as value of the dict
def row_csv2dict(csv_file):
dict_club={}
with open(csv_file)as f:
reader=csv.reader(f,delimiter=',')
for row in reader:
dict_club[row[0]]=row[1]
return dict_club
# write dict to csv file
# write each key/value pair on a separate row
def dict2csv(dict, file):
with open(file, 'wb') as f:
w = csv.writer(f)
# write each key/value pair on a separate row
w.writerows(dict.items())
# write dict to csv file
# write all keys on one row and all values on the next
def dict2csv2(dict, file):
with open(file, 'wb') as f:
w = csv.writer(f)
# write all keys on one row and all values on the next
w.writerow(dict.keys())
w.writerow(dict.values())
# build a dict of list like {key:[...element of lst_inner_value...]}
# key is certain column name of csv file
# the lst_inner_value is a list of specific column name of csv file
def build_list_dict(source_file, key, lst_inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
for element in lst_inner_value:
new_dict.setdefault(row[key], []).append(row[element])
return new_dict
# sample:
# test_club=build_list_dict('test_info.csv','season',['move from','move to'])
# print test_club
# build specific nested dict from csv files
# @params:
# source_file
# outer_key:the outer level key of nested dict
# inner_key:the inner level key of nested dict,and rest key-value will be store as the value of inner key
def build_level2_dict(source_file,outer_key,inner_key):
new_dict = {}
with open(source_file, 'rb')as csv_file:
reader = csv.reader(csv_file, delimiter=',')
fieldnames = next(reader)
inner_keyset=fieldnames
inner_keyset.remove(outer_key)
inner_keyset.remove(inner_key)
csv_file.seek(0)
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
item = new_dict.get(row[outer_key], dict())
item[row[inner_key]] = {k: row[k] for k in inner_keyset}
new_dict[row[outer_key]] = item
return new_dict
# build specific nested dict from csv files
# @params:
# source_file
# outer_key:the outer level key of nested dict
# inner_key:the inner level key of nested dict
# inner_value:set the inner value for the inner key
def build_level2_dict2(source_file,outer_key,inner_key,inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
item = new_dict.get(row[outer_key], dict())
item[row[inner_key]] = row[inner_value]
new_dict[row[outer_key]] = item
return new_dict
# build specific nested dict from csv files
# @params:
# source_file
# outer_key:the outer level key of nested dict
# lst_inner_value: a list of column name,for circumstance that the inner value of the same outer_key are not distinct
# {outer_key:[{pairs of lst_inner_value}]}
def build_level2_dict3(source_file,outer_key,lst_inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
new_dict.setdefault(row[outer_key], []).append({k: row[k] for k in lst_inner_value})
return new_dict
# build specific nested dict from csv files
# @params:
# source_file
# outer_key:the outer level key of nested dict
# lst_inner_value: a list of column name,for circumstance that the inner value of the same outer_key are not distinct
# {outer_key:{key of lst_inner_value:[...value of lst_inner_value...]}}
def build_level2_dict4(source_file,outer_key,lst_inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
# print row
item = new_dict.get(row[outer_key], dict())
# item.setdefault('move from',[]).append(row['move from'])
# item.setdefault('move to', []).append(row['move to'])
for element in lst_inner_value:
item.setdefault(element, []).append(row[element])
new_dict[row[outer_key]] = item
return new_dict
# build specific nested dict from csv files
# @params:
# source_file
# outer_key:the outer level key of nested dict
# lst_inner_key:a list of column name
# lst_inner_value: a list of column name,for circumstance that the inner value of the same lst_inner_key are not distinct
# {outer_key:{lst_inner_key:[...lst_inner_value...]}}
def build_list_dict2(source_file,outer_key,lst_inner_key,lst_inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
# print row
item = new_dict.get(row[outer_key], dict())
item.setdefault(row[lst_inner_key], []).append(row[lst_inner_value])
new_dict[row[outer_key]] = item
return new_dict
# dct=build_list_dict2('test_info.csv','season','move from','move to')
# build specific nested dict from csv files
# a dict like {outer_key:{inner_key1:{inner_key2:{rest_key:rest_value...}}}}
# the params are extract from the csv column name as you like
def build_level3_dict(source_file,outer_key,inner_key1,inner_key2):
new_dict = {}
with open(source_file, 'rb')as csv_file:
reader = csv.reader(csv_file, delimiter=',')
fieldnames = next(reader)
inner_keyset=fieldnames
inner_keyset.remove(outer_key)
inner_keyset.remove(inner_key1)
inner_keyset.remove(inner_key2)
csv_file.seek(0)
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
item = new_dict.get(row[outer_key], dict())
sub_item = item.get(row[inner_key1], dict())
sub_item[row[inner_key2]] = {k: row[k] for k in inner_keyset}
item[row[inner_key1]] = sub_item
new_dict[row[outer_key]] = item
return new_dict
# build specific nested dict from csv files
# a dict like {outer_key:{inner_key1:{inner_key2:inner_value}}}
# the params are extract from the csv column name as you like
def build_level3_dict2(source_file,outer_key,inner_key1,inner_key2,inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
item = new_dict.get(row[outer_key], dict())
sub_item = item.get(row[inner_key1], dict())
sub_item[row[inner_key2]] = row[inner_value]
item[row[inner_key1]] = sub_item
new_dict[row[outer_key]] = item
return new_dict
# build specific nested dict from csv files
# a dict like {outer_key:{inner_key1:{inner_key2:[inner_value]}}}
# for multiple inner_value with the same inner_key2,thus gather them in a list
# the params are extract from the csv column name as you like
def build_level3_dict3(source_file,outer_key,inner_key1,inner_key2,inner_value):
new_dict = {}
with open(source_file, 'rb')as csv_file:
data = csv.DictReader(csv_file, delimiter=",")
for row in data:
item = new_dict.get(row[outer_key], dict())
sub_item = item.get(row[inner_key1], dict())
sub_item.setdefault(row[inner_key2], []).append(row[inner_value])
item[row[inner_key1]] = sub_item
new_dict[row[outer_key]] = item
return new_dict
#----------------------------------------------------------------------------------------------------------
#---------------------------------------------------csv <--> list--------------------------------------------
def list2csv(list, file):
# def list2csv(list):
# wr = csv.writer(open(file, 'wb'), quoting=csv.QUOTE_ALL)
wr=open(file,'w')
for word in list:
# print ''.join(word)
# wr.writerow([word])
wr.write(word+'\n')
# wr.writerow(str.split(word,'"')[0])
# print [word]
# test_list = ['United States', 'China', 'America', 'England']
# list2csv(test_list,'small_test.csv')
# write nested list of dict to csv
def nestedlist2csv(list, out_file):
with open(out_file, 'wb') as f:
w = csv.writer(f)
fieldnames=list[0].keys() # solve the problem to automatically write the header
w.writerow(fieldnames)
for row in list:
w.writerow(row.values())
# my_list = [{'players.vis_name': 'Khazri', 'players.role': 'Midfielder', 'players.country': 'Tunisia',
# 'players.last_name': 'Khazri', 'players.player_id': '989', 'players.first_name': 'Wahbi',
# 'players.date_of_birth': '08/02/1991', 'players.team': 'Bordeaux'},
# {'players.vis_name': 'Khazri', 'players.role': 'Midfielder', 'players.country': 'Tunisia',
# 'players.last_name': 'Khazri', 'players.player_id': '989', 'players.first_name': 'Wahbi',
# 'players.date_of_birth': '08/02/1991', 'players.team': 'Sunderland'},
# {'players.vis_name': 'Lewis Baker', 'players.role': 'Midfielder', 'players.country': 'England',
# 'players.last_name': 'Baker', 'players.player_id': '9574', 'players.first_name': 'Lewis',
# 'players.date_of_birth': '25/04/1995', 'players.team': 'Vitesse'}
# ]
# nestedlist2csv(my_list, 'dict2csv_test.csv')
# collect and convert the first column of csv file to list
def csv2list(csv_file):
lst = []
with open(csv_file, 'rb')as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
lst.append(row[0])
return list(set(lst))
#----------------------------------------------------------------------------------------------------------抽取特定列
抽取特定列的所有值并存储于列表
根据下标抽取特定列到某个新的csv文件
抽取特定列的所有值并存储于列表
获取某列原始的数据并保存为列表
# get certain column value of csv(for common csv file(','))
def get_origin_column_value(file, column_name):
with open(file, 'rb') as f:
role_list = []
reader = csv.reader(f, delimiter=',')
fieldnames = next(reader)
reader = csv.DictReader(f, fieldnames=fieldnames, delimiter=',')
for row in reader:
role_list.append(row[column_name])
return role_list对于某些有特殊需要的可以直接修改代码，比如对原始的列的值进行除重和排序后获取，如下
# get certain column value of csv(for common csv file(',')),and judge if it's repeated
def get_column_value2(file, column_name):
with open(file, 'rb') as f:
role_list = []
reader = csv.reader(f, delimiter=',')
fieldnames = next(reader)
reader = csv.DictReader(f, fieldnames=fieldnames, delimiter=',')
for row in reader:
role_list.append(row[column_name])
role_set = set(role_list)
return sorted(list(role_set))根据下标抽取特定列到某个新的csv文件
import csv
# extract certain column from csv file according to the column#
def column_extract(file_in,file_out,index):
with open(file_in,'r') as f_in:
with open(file_out,'w') as f_out:
for line in f_in:
f_out.write(line.split(',')[index])
f_out.write('\n') # comment if a new line already exists除去完全重复的记录
# eliminated the completely repeated record in repeated file for further analysis
def eliminate_repeated_row(in_file,out_file):
with open(in_file,'rb') as in_file,open(out_file,'wb')as out_file:
seen=set()
for line in in_file:
# print line
if line in seen:continue
seen.add(line)
out_file.write(line)对csv文件按照某一列排序
# sort the csv file by certain column to put the similar record together for further analysis
def sort_csv_byColumn(in_file, out_file,column_name):
with open(in_file, 'rb') as f:
reader = csv.reader(f, delimiter=',')
fieldnames = next(reader)
reader = csv.DictReader(f, fieldnames=fieldnames, delimiter=',')
sorted_list = sorted(reader, key=lambda row: row[column_name], reverse=True)
# print sorted_list
csv_converter.nestedlist2csv(sorted_list, out_file)例如我们按照league_name排序(注意这里调用了csv转换器中的方法将列表的字典转换为csv文件)
sort_csv_byColumn('leagues_size.csv','ordered_leagues_size.csv','league_name')得到结果
season_id,league_name,league_size
2016,ykkonen,9
2003,ykkonen,14
2005,ykkonen,14
2006,ykkonen,14
2007,ykkonen,14
2010,ykkonen,13
2011,ykkonen,10
2009,ykkonen,14
2008,ykkonen,14
2012,ykkonen,10
2013,ykkonen,10
2014,ykkonen,10
2015,ykkonen,10
2016,wiener-stadtliga,16
1988,wiener-stadtliga,16
1993,wiener-stadtliga,16
1994,wiener-stadtliga,16
1995,wiener-stadtliga,16
1996,wiener-stadtliga,16
1997,wiener-stadtliga,16
1998,wiener-stadtliga,16如果我们按league_size排序
sort_csv_byColumn('leagues_size.csv',
'orderedbysize_leagues_size.csv','league_size')得到结果
season_id,league_name,league_size
2008,virsliga,9
2010,virsliga,9
2012,a-lyga,9
2012,a-pojat-sm-sarja,9
2013,a-pojat-sm-sarja,9
1953,salzburger-liga,9
2010,3-lig-grup-1,9
2013,armenian-first-league,9
2016,ykkonen,9
2014,stirling-sports-premiership,9
2014,hong-kong-premier-league,9
2015,hong-kong-premier-league,9
1996,s-league,9
2015,s-league,9
2013,united-football-league,9
2016,i-league,9键值互换
csv文件每一条记录其实可以看作是一个字典，有时csv文件里有不同的键对应同一个值的情况，我们想讲记录反转一下，即让值作为键，对应的键作为值
# return a dict with the same value in original as new key and keys as value
def dict_same_value(original_dict):
new_dict={}
for k,v in original_dict.iteritems():
new_dict.setdefault(v,[]).append(k)
return new_dict最后欢迎大家fork关于这个的github上的repository，一起丰富更多好玩的功能~
更新日志
1、2016-12-18 修复了从csv文件中获取特定的列的值保存为集合的问题，而是存储为原始的列表
2、2016-12-22 改进了csv转换器中的构建二级字典的方法，使其变得更加灵活
3、2016年12月24日14:57:48 在csv转换器部分加入三级字典构造的参照方法
4、2017年1月9日11:28:45 在csv转换器部分，三级字典构造中，加入了最内部存储值为列表的构造方法
5、2017年1月16日10:43:41 在csv转换器部分，加入了构造列表字典的方法以及构造特殊的二级字典（内部为列表）的方法
6、2017年2月9日10:58:17 在csv转换器部分，加入了新的构造特殊的二级字典（内部为列表）的方法
7、2017年2月10日11:21:45 在csv转换器部分，改进了简单的csv文件转换为字典的方法，此外在Csv_Match部分，加入了匹配判断多个列对应的元素条件的方法
python
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