所以我需要帮助我的代码来获取这个
Column Sum
Company 27
Booth 27
Full-Time 27
Full-Time Visa Sponsor 27
Part-Time 27
Internship 27
Freshman 27
Sophomore 27
Junior 27
Senior 27
Post-Bacs 27
MS 27
PhD 27
Alumni 27
但是我的代码现在输出了这个
company_dict = {0:"Company", 1:"Booth",
2:"Full-Time", 3:"Full-Time Visa Sponsor",
4:"Part-Time", 5:"Internship",
6:"Freshman", 7:"Sophomore",
8:"Junior", 9:"Senior",
10:"Post-Bacs", 11:"MS",
12:"PhD", 13:"Alumni"}
#Loop to organize the company_dict
for lines in company_dict:
print(repr(lines),company_dict[lines])
keywords = ("AIG","Baylor","CGG","Citi","ExxonMobil","Flow-Cal Inc.", #I used a list to help me get the information I wanted from the csv file
"Global SHop Solutions","Harris Count CTS","HCSS",
"Hitachi Consulting", "HP Inc.","INT Inc.","JPMorgan Chase & Co",
"Leidos","McKesson","MRE Consulting Ltd.","NetIQ","PROS",
"San Jacinto College","SAS","Smartbridge","Sogeti USA",
"Southwest Research Institute","The Reynolds and Reynolds Company",
"UH Enterprise Systems","U.S. Marine Corps","ValuD Consuting LLC","Wipro")
DataList = [] #I made a blank list
with f as filterf: #This loop will look for the keywords in the file, and only add those keywords
output_line_counter = 0 #I needed it to print with rows, so I set it to 0
for line in filterf:
if any(keyword in line for keyword in keywords): #The actual code that looks for keywords in the line in my file
output_line_counter += 1 #Adds the column (might not be necessary but it works for me)
DataList.append(line)
CleanerData = sorted(set(DataList)) #I made a new 'cleaner' list so that it would be alphabetically without spaces
line_counter = 0
for i in CleanerData: #I had to do another loop to add rows again, it now prints what is required in the question
line_counter += 1
print(line_counter, i, end='')
data_employer = {'No': ('Column', 'Sum')}
for empdata in range(14):
sum = 0
for i in CleanerData:
if i[empdata] != '':
sum += 1
data_employer[empdata] = (company_dict[empdata], sum)
for k in data_employer:
print(list(data_employer.keys()).index(k), data_employer[k][0], data_employer[k][1])
我不得不使用来自csv文件的信息,清理它,现在我必须像这样组织它。我对这部分的代码如下:
ALPHABETICAL ORDER,,,,,,,,,,,,,
,,Positions,,,,Classifications,,,,,,,
Company,Booth,Full-Time,"Full-Time Visa Sponsor",Part-Time,Internship,Freshman,Sophomore,Junior,Senior,Post-Bacs,MS,PhD,Alumni
AIG,10,,,,Yes,,,Jr,,,MS,,
Baylor College of Medicine,19,Yes,Yes,,,,,,,,,,Recent
CGG,17,Yes,Yes,,,,,,,,MS,PhD,Recent
Citi,27/28,Yes,,,Yes,,,Jr,Sr,,,,
ExxonMobil,11,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,,,
,...
Flow-Cal Inc.,16,Yes,,,Yes,,,Jr,Sr,,,,All
Global Shop Solutions,18,Yes,,,Yes,,,,Sr,PB,,,All
Harris County CTS,22,Yes,,,Yes,,,Jr,Sr,PB,MS,PhD,All
HCSS,29,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,Recent
Hitachi Consulting,13,Yes,,,,,,,Sr,,MS,,
HP Inc.,1,Yes,,,Yes,,,Jr,,,MS,,Recent
INT Inc.,20,Yes,Yes,,Yes,,,Jr,Sr,,MS,PhD,
JPMorgan Chase & Co,3,Yes,,,Yes,,,Jr,Sr,,,,
Leidos,390,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,
McKesson,26,Yes,,,,,,,Sr,,,,
,,,,,,,,,,,,,
MRE Consulting Ltd.,2,Yes,,,,,,,Sr,PB,MS,,All
NetIQ,7,,,,Yes,,Soph,Jr,Sr,PB,,,
PROS,21,Yes,,,,,,,Sr,,MS,PhD,All
San Jacinto College ,14,,,,Yes,,Soph,Jr,Sr,PB,MS,,
SAS,4,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,Recent
Smartbridge,8,Yes,,,,,,,Sr,PB,MS,,
Sogeti USA,15,Yes,,,,,,,Sr,PB,MS,,
Southwest Research Institute,12,Yes,,,Yes,,,Jr,Sr,PB,MS,PhD,All
The Reynolds and Reynolds Company,23,Yes,Yes,,Yes,Fr,Soph,Jr,Sr,PB,,,All
UH Enterprise Systems,9,Yes,Yes,Yes,Yes,Fr,Soph,Jr,Sr,PB,MS,PhD,All
U.S. Marine Corps,25,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,All
ValuD Consuting LLC,5,Yes,,,,,,,Sr,PB,,,All
Wipro,24,Yes,,,,,,,Sr,PB,,,
BOOTH ORDER,,,,,,,,,,,,,
,Booth,Positions,,,,Classifications,,,,,,,
Company,#,Full-Time,"Full-Time
Visa Sponsor",Part-Time,Internship,Freshman,Sophomore,Junior,Senior,Post-Bacs,MS,PhD,Alumni
HP�Inc.,1,Yes,,,Yes,,,Jr,,,MS,,Recent
"MRE Consulting, Ltd.",2,Yes,,,,,,,Sr,PB,MS,,All
JPMorgan Chase & Co,3,Yes,,,Yes,,,Jr,Sr,,,,
SAS,4,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,Recent
ValuD Consuting LLC,5,Yes,,,,,,,Sr,PB,,,All
NetIQ,7,,,,Yes,,Soph,Jr,Sr,PB,,,
Smartbridge,8,Yes,,,,,,,Sr,PB,MS,,
UH Enterprise Systems,9,Yes,Yes,Yes,Yes,Fr,Soph,Jr,Sr,PB,MS,PhD,All
AIG,10,,,,Yes,,,Jr,,,MS,,
ExxonMobil,11,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,,,
Southwest Research Institute,12,Yes,,,Yes,,,Jr,Sr,PB,MS,PhD,All
Hitachi Consulting,13,Yes,,,,,,,Sr,,MS,,
San Jacinto College ,14,,,,Yes,,Soph,Jr,Sr,PB,MS,,
Sogeti USA,15,Yes,,,,,,,Sr,PB,MS,,
"Flow-Cal, Inc.",16,Yes,,,Yes,,,Jr,Sr,,,,All
CGG,17,Yes,Yes,,,,,,,,MS,PhD,Recent
Global Shop Solutions,18,Yes,,,Yes,,,,Sr,PB,,,All
Baylor College of Medicine,19,Yes,Yes,,,,,,,,,,Recent
"INT, Inc.",20,Yes,Yes,,Yes,,,Jr,Sr,,MS,PhD,
PROS,21,Yes,,,,,,,Sr,,MS,PhD,All
Harris County CTS,22,Yes,,,Yes,,,Jr,Sr,PB,MS,PhD,All
The Reynolds and Reynolds Company,23,Yes,Yes,,Yes,Fr,Soph,Jr,Sr,PB,,,All
Wipro,24,Yes,,,,,,,Sr,PB,,,
U.S. Marine Corps,25,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,All
McKesson,26,Yes,,,,,,,Sr,,,,
Citi,27/28,Yes,,,Yes,,,Jr,Sr,,,,
HCSS,29,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,Recent
Leidos,30,Yes,,,Yes,Fr,Soph,Jr,Sr,PB,MS,,
我真的没有得到27来自哪里,我猜它是因为我没有看到的一些逻辑错误。这是我对代码的尝试,任何输入都将不胜感激。
谢谢!
原始CSV文件
<dependency>
<groupId>com.outworkers</groupId>
<artifactId>phantom-dsl_2.11</artifactId>
<version>2.24.1</version>
</dependency>
更新:我已经放置了更多代码以帮助澄清。仍在弄清楚为什么它只打印27个列表。我不能在这个项目中使用pandas
答案 0 :(得分:0)
替换最后一行candleDiv.data[0].open[candleDiv.data[0].open.length - 1] = updatedOpenValue;
candleDiv.data[0].close[candleDiv.data[0].close.length - 1] = updatedCloseValue;
candleDiv.data[0].high[candleDiv.data[0].high.length - 1] = updatedHighValue;
candleDiv.data[0].low[candleDiv.data[0].low.length - 1] = updatedLowValue;
Plotly.restyle(candleDiv, 'data[0]', candleDiv.data[0], [0]);
与
print(data_employer[k][0], data_employer[k][1])
答案 1 :(得分:0)
这是一个使用pandas的简单解决方案
import pandas as pd
csv_file_in = 'lines.csv'
csv_file_out = 'return.csv'
df = pd.read_csv(csv_file_in, header=2) # Read in CSV header=2 makes the headers ALumni, PhD etc etc
headers = list(df.columns.values) # get a list of columns to count (headers as row 2)
temp_df = pd.DataFrame([]) # create temp df
for i in headers: #iterate through the columns
try:
new_df = pd.DataFrame({'Sum': df[i].count().sum()}, index=[i]) # new dataframe as holding (will be overwritten in the next iteration)
temp_df = pd.concat([new_df, temp_df]) # concat to temp_df
except KeyError as e:
print(e)
temp_df.to_csv(csv_file_out) #output to csv
print(temp_df)
输出
Sum
Alumni 15
PhD 6
MS 17
Post-Bacs 17
Senior 24
Junior 17
Sophomore 9
Freshman 7
Internship 18
Part-Time 1
Full-Time Visa Sponsor 5
Full-Time 25
Booth 28
Company 29