Как преобразовать строку с "," и "/ n" в DataFrame панд? - PullRequest
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Как преобразовать строку с "," и "/ n" в DataFrame панд?

0 голосов
/ 20 февраля 2019

У меня есть этот кусок кода: 

csvData = str(request.GET.get('csvData'))

print("TYPE csvData", type(csvData))

print("CSV", csvData)

arr = np.array(csvData.splitlines())

print("ARR", arr)

print("TYPE arr", type(arr))

csvData - это строка.

Код выводит следующий вывод:

TYPEcsvData

CSV "NUM, AIRLINE_ARR_ICAO, WAKE, SIBT, SOBT, PLANNED_TURNAROUND, DISTANCE_FROM_ORIGIN, DISTANCE_TO_TARGET \ n1, AEA, H, 2016-01-01 04: 05: 00,2016-01: 00,605,9920,67,5776,89 \ n2, AEA, H, 2016-01-01 04: 25: 00,2016-01-01 06: 30: 00,125.0,10060.80,483.93 \ n3, AVA, H, 2016-01-0105: 05: 00,2016-01-01 07: 05: 00,120.0,8033,86,8033,86 \ n4, IBE, H, 2016-01-01 05: 20: 00,2016-01-01 10: 40: 00 320,0,0,00, 8507.73 \ n5, IBE, H, 2016-01-01 05: 25: 00,2016-01-01 10: 50: 00,325.0,6698.42,6698.42 \ n6, IBE, H, 2016-01-01 05:30:00,2016-01-01 08: 10: 00,160.0,10699.06,1246.30 \ n7, IBE, H, 2016-01-01 05: 30: 00,2016-01-01 11: 00: 00,330.0,9081.35,8033.86 \ n8, IBE, H, 2016-01-01 05: 40: 00,2016-01-01 11: 35: 00,355.0,5776.89,8749.87 \ n9, ANE, M, 2016-01-01 05: 50: 00,2016-01-01 14: 50: 00,540.0,284.73,284.73 \ n10, ETD, H, 2016-01-01 06: 35: 00,2016-01-01 08: 00: 00,85.0,5647.10,5647.10 \ n11, IBS, М, 2016-01-01 06: 50: 00,2016-01-01 08: 00: 00,70.0,547.36,1460,92 \ n12, IBE, H, 2016-01-01 06: 50: 00,2016-01-01 10: 35: 00,225.0,6763.16, 6763.16 \ n13, IBE, H, 2016-01-01 06: 50: 00,2016-01-01 10: 50: 00,240.0,7120.40,7120.40 \ n14, IBE, H, 2016-01-01 06:50:00,2016-01-01 10: 55: 00,245.0,7010.08,0.00 \ n15, QTR, H, 2016-01-01 06: 55: 00,2016-01-01 08: 30: 00,95.0,5338.52,5338.52\ n16, IBS, M, 2016-01-01 07: 00: 00,2016-01-01 07: 45: 00,45.0,485.52, 1721.09 \ n17, IBS, M, 2016-01-01 07:00:00,2016-01-01 07: 45: 00,45.0,394.98,429.37 \ n18, ELY, M, 2016-01-01 07: 05: 00,2016-01-01 08: 30: 00,85.0,3550.483550,48 \ n19, AAL, H, 2016-01-01 07: 05: 00,2016-01-01 12: 05: 00,300,0,5925,61,5925,61 \ n20, TVF, M, 2016-01-01 07:30:00,2016-01-01 08: 10: 00,40.0,1030.31,1030.31 \ n "

ARR ['' NUM, AIRLINE_ARR_ICAO, WAKE, SIBT, SOBT, PLANNED_TURNAROUND, DISTANCE_FROM_ORIGIN, DISTANCE_TO_TARGET \ n, H, 2016-01-01 04: 05: 00,2016-01-01 14: 10: 00,605,9920,67,5776,89 \ n2, AEA, H, 2016-01-01 04: 25: 00,2016-01-01 06: 30: 00,125.0,10060.80,483.93 \ n3, AVA, H, 2016-01-01 05: 05: 00,2016-01-01 07: 05: 00,120.0,8033,86,8033,86 \ n4, IBE, H, 2016-01-01 05: 20: 00,2016-01-01 10: 40: 00 320,0,0,00,8507,73 \ n5, IBE, H, 2016-01-01 05:25: 00,2016-01-01 10: 50: 00,325.0,6698.42,6698.42 \ n6, IBE, H, 2016-01-01 05: 30: 00,2016-01-01 08: 10: 00,160.0,10699.06,1246.30 \n7, IBE, H, 2016-01-01 05: 30: 00,2016-01-01 11: 00: 00,330.0,9081.35,8033.86 \ n8, IBE, H, 2016-01-01 05: 40: 00,2016-01-01 11: 35: 00,355.0,5776,89,8749,87 \ n9, ANE, M, 2016-01-01 05: 50: 00,2016-01-01 14: 50: 00,540.0,284.73,284.73 \ n10, ETD,H, 2016-01-01 06: 35: 00,2016-01-01 08: 00: 00,85.0,5647.10,5647.10 \ n11, IBS, M, 2016-01-01 06: 50: 00,2016-01-01 08: 00: 00,70.0,547.36,1460.92 \ n12, IBE, H, 2016-01-01 06: 50: 00,2016-01-01 10: 35: 00,225.0,6763.16,6763.16 \ n13, IBE,H, 2016-01-01 06: 50: 00,2016-01-01 10: 50: 00,240.0,7120.40,7120.40 \ n14, IBE, H, 2016-01-01 06: 50: 00,2016-01-0110: 55: 00 245,0,7010,08,00 \ n15, QTR, H, 2016-01-01 06: 55: 00,2016-01-01 08: 30: 00,95.0,5338.52,5338.52 \ n16, IBS, M,2016-01-01 07: 00: 00,2016-01-01 07: 45: 00,45.0,485.52,1721.09 \ n17, IBS, M, 2016-01-01 07: 00: 00,2016-01-0107: 45: 00,45.0,394.98,429.37 \ n18,ELY, M, 2016-01-01 07: 05: 00,2016-01-01 08: 30: 00,85.0,3550.48,3550.48 \ n19, AAL, H, 2016-01-01 07: 05: 00,2016-01-01 12: 05: 00,300.0,5925,61,5925,61 \ n20, TVF, M, 2016-01-01 07: 30: 00,2016-01-01 08: 10: 00,40.0,1030.31,1030.31 \ n "']

TYPE arr

Мне нужно преобразовать arr в pandas DataFrame.Я написал этот код: 

new = []
for i in range(0,len(arr)):
    line = arr[i].split(",")
    new.append(line)

X = pd.DataFrame(new[1:],columns=new[0])

print("X",X.head())

Но он не работает должным образом.Я предполагаю, что это не работает, потому что arr это ['".."'] вместо [..].

Любая помощь высоко ценится.

ОБНОВЛЕНИЕ: 

csvData = pd.read_csv(io.StringIO((request.GET.get('csvData'))))

print("TYPE csvData", type(csvData))

print("CSV", csvData.head())

TYPE csvData <class 'pandas.core.frame.DataFrame' CSV Empty DataFrame
Columns:
[NUM,AIRLINE_ARR_ICAO,WAKE,SIBT,SOBT,PLANNED_TURNAROUND,DISTANCE_FROM_ORIGIN,DISTANCE_TO_TARGET\n1,AEA,H,2016-01-01
04:05:00,2016-01-01 14:10:00,605,9920.67,5776.89\n2,AEA,H,2016-01-01
04:25:00,2016-01-01 06:30:00,125.0,10060.80,483.93\n3,AVA,H,2016-01-01
05:05:00,2016-01-01 07:05:00,120.0,8033.86,8033.86\n4,IBE,H,2016-01-01
05:20:00,2016-01-01 10:40:00,320.0,0.00,8507.73\n5,IBE,H,2016-01-01
05:25:00,2016-01-01 10:50:00,325.0,6698.42,6698.42\n6,IBE,H,2016-01-01
05:30:00,2016-01-01
08:10:00,160.0,10699.06,1246.30\n7,IBE,H,2016-01-01
05:30:00,2016-01-01 11:00:00,330.0,9081.35,8033.86\n8,IBE,H,2016-01-01
05:40:00,2016-01-01 11:35:00,355.0,5776.89,8749.87\n9,ANE,M,2016-01-01
05:50:00,2016-01-01 14:50:00,540.0,284.73,284.73\n10,ETD,H,2016-01-01
06:35:00,2016-01-01 08:00:00,85.0,5647.10,5647.10\n11,IBS,M,2016-01-01
06:50:00,2016-01-01 08:00:00,70.0,547.36,1460.92\n12,IBE,H,2016-01-01
06:50:00,2016-01-01
10:35:00,225.0,6763.16,6763.16\n13,IBE,H,2016-01-01
06:50:00,2016-01-01
10:50:00,240.0,7120.40,7120.40\n14,IBE,H,2016-01-01
06:50:00,2016-01-01 10:55:00,245.0,7010.08,0.00\n15,QTR,H,2016-01-01
06:55:00,2016-01-01 08:30:00,95.0,5338.52,5338.52\n16,IBS,M,2016-01-01
07:00:00,2016-01-01 07:45:00,45.0,485.52,1721.09\n17,IBS,M,2016-01-01
07:00:00,2016-01-01 07:45:00,45.0,394.98,429.37\n18,ELY,M,2016-01-01
07:05:00,2016-01-01 08:30:00,85.0,3550.48,3550.48\n19,AAL,H,2016-01-01
07:05:00,2016-01-01
12:05:00,300.0,5925.61,5925.61\n20,TVF,M,2016-01-01
07:30:00,2016-01-01 08:10:00,40.0,1030.31,1030.31\n] 

Index: []

Обновление 2: 

csvData = pd.read_csv(io.StringIO((request.GET.get('csvData').replace('\\n', '\n'))))

print("TYPE csvData", type(csvData))

print("CSV", csvData.head())

TYPE csvData <class 'pandas.core.frame.DataFrame'>
CSV Empty DataFrame

Columns: [NUM,AIRLINE_ARR_ICAO,WAKE,SIBT,SOBT,PLANNED_TURNAROUND,DISTANCE_FROM_ORIGIN,DISTANCE_TO_TARGET
1,AEA,H,2016-01-01 04:05:00,2016-01-01 14:10:00,605,9920.67,5776.89
2,AEA,H,2016-01-01 04:25:00,2016-01-01 06:30:00,125.0,10060.80,483.93
3,AVA,H,2016-01-01 05:05:00,2016-01-01 07:05:00,120.0,8033.86,8033.86
4,IBE,H,2016-01-01 05:20:00,2016-01-01 10:40:00,320.0,0.00,8507.73
5,IBE,H,2016-01-01 05:25:00,2016-01-01 10:50:00,325.0,6698.42,6698.42
6,IBE,H,2016-01-01 05:30:00,2016-01-01 08:10:00,160.0,10699.06,1246.30
7,IBE,H,2016-01-01 05:30:00,2016-01-01 11:00:00,330.0,9081.35,8033.86
8,IBE,H,2016-01-01 05:40:00,2016-01-01 11:35:00,355.0,5776.89,8749.87
9,ANE,M,2016-01-01 05:50:00,2016-01-01 14:50:00,540.0,284.73,284.73
10,ETD,H,2016-01-01 06:35:00,2016-01-01 08:00:00,85.0,5647.10,5647.10
11,IBS,M,2016-01-01 06:50:00,2016-01-01 08:00:00,70.0,547.36,1460.92
12,IBE,H,2016-01-01 06:50:00,2016-01-01 10:35:00,225.0,6763.16,6763.16
13,IBE,H,2016-01-01 06:50:00,2016-01-01 10:50:00,240.0,7120.40,7120.40
14,IBE,H,2016-01-01 06:50:00,2016-01-01 10:55:00,245.0,7010.08,0.00
15,QTR,H,2016-01-01 06:55:00,2016-01-01 08:30:00,95.0,5338.52,5338.52
16,IBS,M,2016-01-01 07:00:00,2016-01-01 07:45:00,45.0,485.52,1721.09
17,IBS,M,2016-01-01 07:00:00,2016-01-01 07:45:00,45.0,394.98,429.37
18,ELY,M,2016-01-01 07:05:00,2016-01-01 08:30:00,85.0,3550.48,3550.48
19,AAL,H,2016-01-01 07:05:00,2016-01-01 12:05:00,300.0,5925.61,5925.61
20,TVF,M,2016-01-01 07:30:00,2016-01-01 08:10:00,40.0,1030.31,1030.31
]

Index: []

Обновление 3:

Так создается csvData, 

    var reader = new FileReader();
    reader.onload =  (e) => {
      // Use reader.result
      this.setState({
        csvData: reader.result
      })
      this.props.setCsvData(reader.result)
    }
    reader.readAsText(files[0])

Затем я отправил его на сервер таким образом: 

'&csvData='+JSON.stringify(this.state.csvData)

1 Ответ

0 голосов
/ 20 февраля 2019

Используйте метод read_csv для загрузки ваших данных. 

import io

data = pandas.read_csv(io.StringIO(request.GET.get('csvData')))
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