def order_ads_limited(csv_file_path: str, column_name: str) -> str:
"""
the main function
:param csv_file_path: the csv's path
:param column_name: the requested column name
:return: the sorted file
"""
column_id: int = _get_column_id(csv_file_path, column_name)
out_file_path: str = io_utils.get_output_file_path(csv_file_path)
csvsorter.csvsort(csv_file_path, [column_id],
output_filename=out_file_path,
max_size=2000)
return out_file_path
def merge_dups(tensor_name, num_modes, merge_func=sum):
""" Remove duplicate non-zeros from a tensor file. """
sorted_f = tensor_name + '.sorted'
tmp_name = str(uuid.uuid4().hex) + '.tns'
try:
# Sort the "CSV" tensor -- this library prints to stdout in old versions,
# so suppress that
with open(os.devnull, 'w') as redirect:
with redirect_stdout(redirect):
csvsort(tensor_name,
range(num_modes),
column_types=int,
output_filename=sorted_f,
max_size=800,
delimiter=' ',
has_header=False)
# Merge duplicate non-zeros
with open(tmp_name, 'w') as fout:
dup_lines = []
with open(sorted_f, 'r') as fin:
for line in fin:
line = line.split()
# indices do not match -- merge previous duplicates
if len(dup_lines) > 0 and line[:-1] != dup_lines[0][:-1]:
vals = [literal_eval(x[-1]) for x in dup_lines]
inds = [str(x) for x in dup_lines[0][:-1]]
print('{} {}'.format(' '.join(inds), merge_func(vals)),
file=fout)
dup_lines = []
dup_lines.append(line)
# final flush
vals = [eval(x[-1]) for x in dup_lines]
inds = [str(x) for x in dup_lines[0][:-1]]
print('{} {}'.format(' '.join(inds), merge_func(vals)), file=fout)
# overwrite original data
os.rename(tmp_name, tensor_name)
except:
# if there was an exception, we don't want to overwrite the original data
os.remove(tmp_name)
finally:
os.remove(sorted_f)
cheader = file1.readline() #把第一行讀出來即丟棄 file1_rows = csv.reader(file1, delimiter = "\t") #每一列為tab stockfn_in = "C:\\Users\\b1013\\Desktop\\Pythoncode2\\stock_tmp3.csv" file_in = open(stockfn_in, "w", encoding = "utf-8", newline = "") #輸入檔案 writer_in = csv.writer(file_in) for i in file1_rows: #利用for去寫入 將個股空白去除 i = map( lambda x: x.strip(), i) writer_in.writerow(i) import csvsorter wd = "C:\\Users\\b1013\\Desktop\\Pythoncode2" os.chdir(wd) stockfn_tmp3 = "C:\\Users\\b1013\\Desktop\\Pythoncode2\\stock_tmp3.csv" stocksorted = "C:\\Users\\b1013\\Desktop\\Pythoncode2\\stocksorted.csv" csvsorter.csvsort( stockfn_tmp3, [0,2], output_filename = stocksorted , has_header = True) # 來源檔, 排序方式, 目的檔, 是否有標題 #匯入market大盤資料 marketfn = "C:\\Users\\b1013\\Desktop\\Pythoncode2\\bigmarket.txt" filebigmarket = open(marketfn, "r", encoding = "cp950" ,newline = "") cheader1 = filebigmarket.readline() #把第一行讀出來即丟棄 file_big_rows = csv.reader( filebigmarket, delimiter = "\t") bigmarket_in = "C:\\Users\\b1013\\Desktop\\Pythoncode2\\bigmarket_in.csv" file_bigmarket_in = open(bigmarket_in, "w", encoding = "utf-8", newline ="") writer_big_in = csv.writer(file_bigmarket_in) for i in file_big_rows: #作空白去除 i = map( lambda x: x.strip(), i) writer_big_in.writerow(i) bigmarket = "C:\\Users\\b1013\\Desktop\\Pythoncode2\\bigmarket_in.csv"
#寫檔案
stockfn_tmp1 = "C:\\Users\\Master\\Desktop\\大學 課程\\python 商管程式設計 台大MOOC (二)\\上課練習檔案\\week4\\stock_c.csv"
fh3 = open(stockfn_tmp1, 'w', encoding='utf-8', newline='')
writer3 = csv.writer(fh3)
fh3.write('COID,Name,MDATE,ROI,MV,CLOSE\n') #寫入標題
for arow in reader1:
arow = map(lambda x: x.strip(), arow)
writer3.writerow(arow)
fh3.close()
fh1.close()
#把剛剛的檔案按照股票及日期 做排序
stockfn_tmp1 = "C:\\Users\\Master\\Desktop\\大學 課程\\python 商管程式設計 台大MOOC (二)\\上課練習檔案\\week4\\stock_c.csv"
stockfn_sorted = "C:\\Users\\Master\\Desktop\\大學 課程\\python 商管程式設計 台大MOOC (二)\\上課練習檔案\\week4\\stock_sorted.csv"
csvsorter.csvsort(stockfn_tmp1, [0, 2],
output_filename=stockfn_sorted,
has_header=True)
#把市場報酬率的資料 加進字典
mktfn = "C:\\Users\\Master\\Desktop\\大學 課程\\python 商管程式設計 台大MOOC (二)\\上課練習檔案\\week4\\market.csv"
fh1 = open(mktfn, 'r', newline='')
reader1 = csv.DictReader(fh1)
mktret = dict()
for arow in reader1:
mktret[arow['MDATE']] = float(arow['MKT'])
fh1.close()
print("read %d market return data" % len(mktret))
#把已經排序好的股票excel 跟市場報酬率做合併
coidlist = []
namelist = []
alphalist = []