# Python lists to Dataset

**URL:** <https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473>\
**Category:** Ignition\
**Created:** [October 31, 2018, 7:03pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473 "2018-10-31T19:03:46Z")\
**Posts on this page:** 11\
**Page:** 1

<div class="post-metadata">

**Author:** ![kaushik.shah](https://avatars.discourse-cdn.com/v4/letter/k/34f0e0/32.png) [@kaushik.shah](https://forum.inductiveautomation.com/u/kaushik.shah)\
**Post date:** [October 31, 2018, 7:03pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/1 "2018-10-31T19:03:46Z")

</div>

Hello Everyone,  
I have the python list as shown below,  
I want to convert that into dataset so that I can draw the chart. Note, that every element in list has 2 values separated by comma. So the first value would be my X plot and another would be Y.

```auto
['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']
 Pressure Rate Curve

```

I have tried,

```auto
ds = system.dataset.toDataSet(['Column name'], Curve1data)

```

But, it gives me an error as,

```auto
Traceback (most recent call last):

  File "<event:propertyChange>", line 133, in <module>

  File "<event:propertyChange>", line 18, in ReadFile

  File "<event:propertyChange>", line 127, in CheckData

IndexError: Row 0 doesn't have the same number of columns as header list.

Ignition v7.8.5 (b2016120813)
Java: Oracle Corporation 1.8.0_131

```

Suggestion appreciated.  
Thanks.

---

<div class="post-metadata">

**Author:** ![pturmel](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/pturmel/32/3295_2.png) [@pturmel](https://forum.inductiveautomation.com/u/pturmel)\
**Post date:** [October 31, 2018, 7:14pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/2 "2018-10-31T19:14:43Z")

</div>

toDataSet() needs its rows in the form of a list of lists. Each inner list becomes a row, and must have the same number of items as the list of column names.

---

<div class="post-metadata">

**Author:** ![ethomason](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/ethomason/32/43338_2.png) [@ethomason](https://forum.inductiveautomation.com/u/ethomason)\
**Post date:** [October 31, 2018, 7:32pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/3 "2018-10-31T19:32:52Z")

</div>

What Phil said ^

Not sure where your list came from but the best thing to do is groom it into a list of lists there. You will also have trouble using the strings in a chart so will need to convert them to floats. This works:

```auto
myList = ['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']

myNewList = []
for item in myList:
	newItem = item.split(',')
	newItem = map(float, newItem)
	myNewList.append(newItem)

myHeader = ['col1', 'col2']

ds = system.dataset.toDataSet(myHeader, myNewList)

```

![](https://us1.discourse-cdn.com/inductiveautomation/original/2X/d/da3de08f7542061e052165f6562a3bf8713c119a.png)

---

<div class="post-metadata">

**Author:** ![raymond1982](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/raymond1982/32/6490_2.png) [@raymond1982](https://forum.inductiveautomation.com/u/raymond1982)\
**Post date:** [November 1, 2018, 1:44am UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/4 "2018-11-01T01:44:19Z")

</div>

Only posting to say @ethomason use of map is cunning and here is a one liner variation if youre into list comprehension.

```auto
myList = ['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']

system.dataset.toDataSet(['col1', 'col2'], [map(float, i.split(',')) for i in myList])

```

---

<div class="post-metadata">

**Author:** ![kaushik.shah](https://avatars.discourse-cdn.com/v4/letter/k/34f0e0/32.png) [@kaushik.shah](https://forum.inductiveautomation.com/u/kaushik.shah)\
**Post date:** [November 1, 2018, 1:13pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/5 "2018-11-01T13:13:33Z")

</div>

Hello @raymond1982 and @ethomason…  
I tried both ideas which basically the same and nice, but the List contains an whole number (see the second element in list) (I believe) which throws an error as,

```auto
Traceback (most recent call last):

  File "<event:propertyChange>", line 143, in <module>

  File "<event:propertyChange>", line 18, in ReadFile

  File "<event:propertyChange>", line 131, in CheckData

ValueError: invalid literal for __float__ : ['10'

Ignition v7.8.5 (b2016120813)
Java: Oracle Corporation 1.8.0_131

```

Thanks for your help.

---

<div class="post-metadata">

**Author:** ![raymond1982](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/raymond1982/32/6490_2.png) [@raymond1982](https://forum.inductiveautomation.com/u/raymond1982)\
**Post date:** [November 1, 2018, 1:57pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/6 "2018-11-01T13:57:10Z")

</div>

I tested it in script console before posting it.  
Prior to making a basic dataset it prints like this.

```auto
[[9.4, -104.55], [10.0, -89.09], [10.6, -74.55], [11.2, -62.73], [11.8, -50.0], [12.4, -37.27], [13.0, -20.91], [13.6, -7.27], [14.2, 7.27], [14.8, 20.91], [15.4, 41.82], [16.0, 58.18], [16.6, 78.18], [17.2, 100.0], [17.8, 121.82], [18.4, 141.82], [19.0, 158.18], [19.6, 174.55], [20.2, 184.55], [20.8, 193.21], [21.4, 198.08], [22.0, 203.66], [22.6, 208.94], [23.2, 213.01], [23.7, 215.82], [24.3, 217.35], [24.9, 217.59], [25.5, 216.56], [26.1, 218.91], [26.7, 219.09], [27.3, 219.09], [27.9, 217.27], [28.5, 213.64], [29.1, 213.64], [29.7, 217.27], [30.3, 219.09], [30.9, 219.09], [31.5, 217.27], [32.1, 213.64], [32.7, 218.18], [33.3, 220.91], [33.9, 221.82], [34.5, 220.91], [35.1, 218.18], [35.7, 218.18], [36.3, 220.91], [36.9, 221.82], [37.5, 220.91], [38.1, 222.73], [38.7, 221.82], [39.3, 223.64], [39.9, 223.64], [40.5, 223.53], [41.1, 225.77], [41.7, 224.94], [42.3, 226.59], [42.9, 230.68], [43.4, 231.62], [44.0, 229.39], [44.6, 234.26], [45.2, 235.92], [45.8, 234.46], [46.4, 234.55], [47.0, 232.73], [47.6, 232.73], [48.2, 234.55], [48.8, 232.73], [49.4, 232.73], [50.0, 234.55], [50.6, 232.73], [51.2, 237.27], [51.8, 238.18], [52.4, 235.45], [53.0, 239.09], [53.6, 239.09], [54.2, 240.0], [54.8, 241.82], [55.4, 244.55], [56.0, 242.73], [56.6, 246.36], [57.2, 245.45], [57.8, 250.0], [58.4, 250.0], [59.0, 250.0], [59.6, 250.0], [60.2, 250.0], [60.8, 250.0], [61.4, 250.0], [62.0, 250.0], [62.6, 250.0], [63.2, 251.9], [63.8, 253.4], [64.4, 259.2], [65.0, 259.08], [65.6, 263.23], [66.1, 266.04], [66.7, 267.48], [67.3, 267.59], [67.9, 270.96], [68.5, 267.48], [69.1, 267.27], [69.7, 267.27], [70.3, 270.0], [70.9, 275.45], [71.5, 273.64], [72.1, 274.55], [72.7, 278.18], [73.3, 279.09], [73.9, 281.82], [74.5, 286.36], [75.1, 287.27], [75.7, 289.09], [76.3, 291.82], [76.9, 295.45], [77.5, 300.0], [78.1, 300.0], [78.7, 300.0], [79.3, 300.0], [79.9, 300.0], [80.5, 300.0], [81.1, 300.0], [81.7, 300.0], [82.3, 300.0], [82.9, 304.55], [83.5, 310.42], [84.1, 315.06], [84.7, 318.39], [85.3, 320.33], [85.9, 320.84], [86.4, 319.9], [87.0, 317.52], [87.6, 318.41], [88.2, 316.97], [88.8, 313.27], [89.4, 312.73], [90.0, 313.64], [90.6, 313.64], [91.2, 312.73], [91.8, 310.91], [92.4, 308.18], [93.0, 304.55], [93.6, 300.0], [94.2, 300.0], [94.8, 300.0], [95.4, 300.0], [96.0, 300.0], [96.6, 300.0], [97.2, 300.0], [97.8, 300.0], [98.4, 295.45], [99.0, 291.82], [99.6, 289.09], [100.2, 287.27], [100.8, 286.36], [101.4, 286.36], [102.0, 282.73], [102.6, 280.91], [103.2, 280.91], [103.8, 278.18], [104.4, 278.18], [105.0, 275.45], [105.6, 270.0], [106.2, 269.27], [106.8, 266.33], [107.4, 266.63], [108.0, 265.62], [108.6, 263.22], [109.1, 259.47], [109.7, 260.0], [110.3, 254.54], [110.9, 253.39], [111.5, 251.83], [112.1, 245.45], [112.7, 246.36], [113.3, 242.73], [113.9, 240.0], [114.5, 238.18], [115.1, 237.27], [115.7, 232.73], [116.3, 230.0], [116.9, 224.55], [117.5, 226.36], [118.1, 225.45], [118.7, 221.82], [119.3, 220.91], [119.9, 218.18], [120.5, 213.64], [121.1, 212.73], [121.7, 210.91], [122.3, 208.18], [122.9, 204.55], [123.5, 200.0], [124.1, 195.45], [124.7, 191.82], [125.3, 189.09], [125.9, 187.27], [126.5, 186.36], [127.1, 186.36], [127.7, 182.73], [128.3, 180.91], [128.9, 180.91], [129.5, 178.18], [130.1, 178.18], [130.7, 175.45], [131.3, 170.0], [131.9, 167.27], [132.5, 167.27], [133.1, 165.45], [133.7, 167.27], [134.3, 162.73], [134.9, 161.82], [135.5, 160.0], [136.1, 157.27], [136.7, 153.64], [137.3, 154.55], [137.9, 150.0], [138.5, 145.45], [139.1, 146.36], [139.7, 142.73], [140.3, 144.55], [140.9, 141.82], [141.5, 140.0], [142.1, 139.09], [142.7, 139.09], [143.3, 135.45], [143.9, 133.64], [144.5, 133.64], [145.1, 130.0], [145.7, 128.18], [146.3, 123.64], [146.9, 126.36], [147.5, 126.36], [148.1, 123.64], [148.7, 123.64], [149.3, 121.82], [149.9, 118.18], [150.5, 112.73], [151.1, 110.91], [151.7, 108.18], [152.3, 109.09], [152.9, 108.18], [153.5, 110.91], [154.1, 112.73], [154.7, 113.64], [155.3, 113.64], [155.9, 112.73], [156.5, 110.91], [157.1, 108.18], [157.7, 100.0], [158.3, 92.57], [158.9, 90.38], [159.5, 88.93], [160.1, 88.26], [160.7, 88.39], [161.2, 89.33], [161.8, 91.06], [162.4, 93.57], [163.0, 92.17], [163.6, 92.47], [164.2, 89.09], [164.8, 87.27], [165.4, 86.36], [166.0, 81.82], [166.6, 79.09], [167.2, 78.18], [167.8, 79.09], [168.4, 77.27], [169.0, 78.18], [169.6, 76.36], [170.2, 76.36], [170.8, 73.64], [171.4, 73.64], [172.0, 76.36], [172.6, 71.82], [173.2, 70.0], [173.8, 70.91], [174.4, 70.0], [175.0, 67.27], [175.6, 62.73], [176.2, 61.82], [176.8, 64.55], [177.4, 60.91], [180.4, 63.93], [180.4, 63.93]]

```

Do me a favor and run this and post the result.

```auto
print map(type, myList[:20])

```

---

<div class="post-metadata">

**Author:** ![kaushik.shah](https://avatars.discourse-cdn.com/v4/letter/k/34f0e0/32.png) [@kaushik.shah](https://forum.inductiveautomation.com/u/kaushik.shah)\
**Post date:** [November 1, 2018, 2:31pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/7 "2018-11-01T14:31:15Z")

</div>

@raymond1982  
I have the same answer as yours in Script console,  
If I run code as below,

```auto
myList = ['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']

print myList
print map(type, myList[:20])

ds =[map(float, i.split(',')) for i in myList]

print ds

print map(type, ds[:20])

```

I get this,

```auto

>>> 
['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']
[<type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>, <type 'str'>]
[[9.4, -104.55], [10.0, -89.09], [10.6, -74.55], [11.2, -62.73], [11.8, -50.0], [12.4, -37.27], [13.0, -20.91], [13.6, -7.27], [14.2, 7.27], [14.8, 20.91], [15.4, 41.82], [16.0, 58.18], [16.6, 78.18], [17.2, 100.0], [17.8, 121.82], [18.4, 141.82], [19.0, 158.18], [19.6, 174.55], [20.2, 184.55], [20.8, 193.21], [21.4, 198.08], [22.0, 203.66], [22.6, 208.94], [23.2, 213.01], [23.7, 215.82], [24.3, 217.35], [24.9, 217.59], [25.5, 216.56], [26.1, 218.91], [26.7, 219.09], [27.3, 219.09], [27.9, 217.27], [28.5, 213.64], [29.1, 213.64], [29.7, 217.27], [30.3, 219.09], [30.9, 219.09], [31.5, 217.27], [32.1, 213.64], [32.7, 218.18], [33.3, 220.91], [33.9, 221.82], [34.5, 220.91], [35.1, 218.18], [35.7, 218.18], [36.3, 220.91], [36.9, 221.82], [37.5, 220.91], [38.1, 222.73], [38.7, 221.82], [39.3, 223.64], [39.9, 223.64], [40.5, 223.53], [41.1, 225.77], [41.7, 224.94], [42.3, 226.59], [42.9, 230.68], [43.4, 231.62], [44.0, 229.39], [44.6, 234.26], [45.2, 235.92], [45.8, 234.46], [46.4, 234.55], [47.0, 232.73], [47.6, 232.73], [48.2, 234.55], [48.8, 232.73], [49.4, 232.73], [50.0, 234.55], [50.6, 232.73], [51.2, 237.27], [51.8, 238.18], [52.4, 235.45], [53.0, 239.09], [53.6, 239.09], [54.2, 240.0], [54.8, 241.82], [55.4, 244.55], [56.0, 242.73], [56.6, 246.36], [57.2, 245.45], [57.8, 250.0], [58.4, 250.0], [59.0, 250.0], [59.6, 250.0], [60.2, 250.0], [60.8, 250.0], [61.4, 250.0], [62.0, 250.0], [62.6, 250.0], [63.2, 251.9], [63.8, 253.4], [64.4, 259.2], [65.0, 259.08], [65.6, 263.23], [66.1, 266.04], [66.7, 267.48], [67.3, 267.59], [67.9, 270.96], [68.5, 267.48], [69.1, 267.27], [69.7, 267.27], [70.3, 270.0], [70.9, 275.45], [71.5, 273.64], [72.1, 274.55], [72.7, 278.18], [73.3, 279.09], [73.9, 281.82], [74.5, 286.36], [75.1, 287.27], [75.7, 289.09], [76.3, 291.82], [76.9, 295.45], [77.5, 300.0], [78.1, 300.0], [78.7, 300.0], [79.3, 300.0], [79.9, 300.0], [80.5, 300.0], [81.1, 300.0], [81.7, 300.0], [82.3, 300.0], [82.9, 304.55], [83.5, 310.42], [84.1, 315.06], [84.7, 318.39], [85.3, 320.33], [85.9, 320.84], [86.4, 319.9], [87.0, 317.52], [87.6, 318.41], [88.2, 316.97], [88.8, 313.27], [89.4, 312.73], [90.0, 313.64], [90.6, 313.64], [91.2, 312.73], [91.8, 310.91], [92.4, 308.18], [93.0, 304.55], [93.6, 300.0], [94.2, 300.0], [94.8, 300.0], [95.4, 300.0], [96.0, 300.0], [96.6, 300.0], [97.2, 300.0], [97.8, 300.0], [98.4, 295.45], [99.0, 291.82], [99.6, 289.09], [100.2, 287.27], [100.8, 286.36], [101.4, 286.36], [102.0, 282.73], [102.6, 280.91], [103.2, 280.91], [103.8, 278.18], [104.4, 278.18], [105.0, 275.45], [105.6, 270.0], [106.2, 269.27], [106.8, 266.33], [107.4, 266.63], [108.0, 265.62], [108.6, 263.22], [109.1, 259.47], [109.7, 260.0], [110.3, 254.54], [110.9, 253.39], [111.5, 251.83], [112.1, 245.45], [112.7, 246.36], [113.3, 242.73], [113.9, 240.0], [114.5, 238.18], [115.1, 237.27], [115.7, 232.73], [116.3, 230.0], [116.9, 224.55], [117.5, 226.36], [118.1, 225.45], [118.7, 221.82], [119.3, 220.91], [119.9, 218.18], [120.5, 213.64], [121.1, 212.73], [121.7, 210.91], [122.3, 208.18], [122.9, 204.55], [123.5, 200.0], [124.1, 195.45], [124.7, 191.82], [125.3, 189.09], [125.9, 187.27], [126.5, 186.36], [127.1, 186.36], [127.7, 182.73], [128.3, 180.91], [128.9, 180.91], [129.5, 178.18], [130.1, 178.18], [130.7, 175.45], [131.3, 170.0], [131.9, 167.27], [132.5, 167.27], [133.1, 165.45], [133.7, 167.27], [134.3, 162.73], [134.9, 161.82], [135.5, 160.0], [136.1, 157.27], [136.7, 153.64], [137.3, 154.55], [137.9, 150.0], [138.5, 145.45], [139.1, 146.36], [139.7, 142.73], [140.3, 144.55], [140.9, 141.82], [141.5, 140.0], [142.1, 139.09], [142.7, 139.09], [143.3, 135.45], [143.9, 133.64], [144.5, 133.64], [145.1, 130.0], [145.7, 128.18], [146.3, 123.64], [146.9, 126.36], [147.5, 126.36], [148.1, 123.64], [148.7, 123.64], [149.3, 121.82], [149.9, 118.18], [150.5, 112.73], [151.1, 110.91], [151.7, 108.18], [152.3, 109.09], [152.9, 108.18], [153.5, 110.91], [154.1, 112.73], [154.7, 113.64], [155.3, 113.64], [155.9, 112.73], [156.5, 110.91], [157.1, 108.18], [157.7, 100.0], [158.3, 92.57], [158.9, 90.38], [159.5, 88.93], [160.1, 88.26], [160.7, 88.39], [161.2, 89.33], [161.8, 91.06], [162.4, 93.57], [163.0, 92.17], [163.6, 92.47], [164.2, 89.09], [164.8, 87.27], [165.4, 86.36], [166.0, 81.82], [166.6, 79.09], [167.2, 78.18], [167.8, 79.09], [168.4, 77.27], [169.0, 78.18], [169.6, 76.36], [170.2, 76.36], [170.8, 73.64], [171.4, 73.64], [172.0, 76.36], [172.6, 71.82], [173.2, 70.0], [173.8, 70.91], [174.4, 70.0], [175.0, 67.27], [175.6, 62.73], [176.2, 61.82], [176.8, 64.55], [177.4, 60.91], [180.4, 63.93], [180.4, 63.93]]
[<type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>, <type 'list'>]
>>> 

```

Which completely makes sense and that’s what we need.

But, when I run below code in property change script of timer,

```auto
Curve1data = ['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']

ds = [map(float, i.split(',')) for i in Curve1data]

print ds

```

I get below error in my console,

```auto
Traceback (most recent call last):
  File "<event:propertyChange>", line 145, in <module>
  File "<event:propertyChange>", line 18, in ReadFile
  File "<event:propertyChange>", line 137, in CheckData
ValueError: invalid literal for __float__ : ['10'

	at org.python.core.Py.ValueError(Py.java:309)
	at org.python.core.PyString.atof(PyString.java:1545)
	at org.python.core.PyString. __float__ (PyString.java:772)
	at org.python.core.PyFloat.float_new(PyFloat.java:61)
	at org.python.core.PyFloat$exposed ___new__.new_impl(Unknown Source)
	at org.python.core.PyType.invokeNew(PyType.java:466)
	at org.python.core.PyType.type ___call__ (PyType.java:1558)
	at org.python.core.PyType. __call__ (PyType.java:1548)
	at org.python.core.PyObject. __call__ (PyObject.java:357)
	at org.python.core. __builtin__.map( __builtin__.java:756)
	at org.python.core.BuiltinFunctions.fancyCall( __builtin__.java:253)
	at org.python.core.BuiltinFunctions. __call__ ( __builtin__.java:172)
	at org.python.core.PyObject. __call__ (PyObject.java:408)
	at org.python.pycode._pyx2499.CheckData$2(<event:propertyChange>:138)
	at org.python.pycode._pyx2499.call_function(<event:propertyChange>)
	at org.python.core.PyTableCode.call(PyTableCode.java:165)
	at org.python.core.PyBaseCode.call(PyBaseCode.java:134)
	at org.python.core.PyFunction. __call__ (PyFunction.java:317)
	at org.python.pycode._pyx2499.ReadFile$1(<event:propertyChange>:20)
	at org.python.pycode._pyx2499.call_function(<event:propertyChange>)
	at org.python.core.PyTableCode.call(PyTableCode.java:165)
	at org.python.core.PyBaseCode.call(PyBaseCode.java:120)
	at org.python.core.PyFunction. __call__ (PyFunction.java:307)
	at org.python.pycode._pyx2499.f$0(<event:propertyChange>:145)
	at org.python.pycode._pyx2499.call_function(<event:propertyChange>)
	at org.python.core.PyTableCode.call(PyTableCode.java:165)
	at org.python.core.PyCode.call(PyCode.java:18)
	at org.python.core.Py.runCode(Py.java:1275)
	at com.inductiveautomation.ignition.common.script.ScriptManager.runCode(ScriptManager.java:631)
	at com.inductiveautomation.factorypmi.application.binding.action.ActionAdapter.runActions(ActionAdapter.java:168)
	at com.inductiveautomation.factorypmi.application.binding.action.ActionAdapter.access$000(ActionAdapter.java:40)
	at com.inductiveautomation.factorypmi.application.binding.action.ActionAdapter$ActionRunner.run(ActionAdapter.java:280)
	at java.awt.event.InvocationEvent.dispatch(Unknown Source)
	at java.awt.EventQueue.dispatchEventImpl(Unknown Source)
	at java.awt.EventQueue.access$500(Unknown Source)
	at java.awt.EventQueue$3.run(Unknown Source)
	at java.awt.EventQueue$3.run(Unknown Source)
	at java.security.AccessController.doPrivileged(Native Method)
	at java.security.ProtectionDomain$JavaSecurityAccessImpl.doIntersectionPrivilege(Unknown Source)
	at java.awt.EventQueue.dispatchEvent(Unknown Source)
	at java.awt.EventDispatchThread.pumpOneEventForFilters(Unknown Source)
	at java.awt.EventDispatchThread.pumpEventsForFilter(Unknown Source)
	at java.awt.EventDispatchThread.pumpEventsForHierarchy(Unknown Source)
	at java.awt.EventDispatchThread.pumpEvents(Unknown Source)
	at java.awt.EventDispatchThread.pumpEvents(Unknown Source)
	at java.awt.EventDispatchThread.run(Unknown Source)

```

Please let me know if you want me to send you the whole code.

Thanks @raymond1982

---

<div class="post-metadata">

**Author:** ![raymond1982](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/raymond1982/32/6490_2.png) [@raymond1982](https://forum.inductiveautomation.com/u/raymond1982)\
**Post date:** [November 1, 2018, 2:32pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/8 "2018-11-01T14:32:47Z")

</div>

Just post the whole code here! Ill try and replicate.

---

<div class="post-metadata">

**Author:** ![kaushik.shah](https://avatars.discourse-cdn.com/v4/letter/k/34f0e0/32.png) [@kaushik.shah](https://forum.inductiveautomation.com/u/kaushik.shah)\
**Post date:** [November 1, 2018, 2:43pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/9 "2018-11-01T14:43:55Z")

</div>

Here is the code,

```auto
import time
import threading
import os	
import glob

def ReadFile():		
	contents = "C:\\Users\\kshah2\\Desktop\\MDR1.asc"
	if system.file.fileExists(contents):			
		File = system.file.readFileAsString(contents)
		event.source.parent.getComponent('Text Area').text = File
		event.source.running = 0
		event.source.value = 0
 		line_num2 = 0
 		Read = open(contents, "r")
 		for line in Read.readlines():
 			line_num2 += 1
 			if line.find("DATAPOINT") >= 0:
 				CheckData(line_num2 - 1)
	elif event.source.value == 19:
		system.gui.messageBox("No File Exists")

def CheckData(line_num2):
	contents = open("C:\\Users\\kshah2\\Desktop\\MDR1.asc", "r")
	Result = {}
	caption = []
	values = []
	Curve1name = ""
	Curve2name = ""
	Curve3name = ""
	Curve4name = ""
	Curve5name = ""
	Curve6name = ""
	Curve7name = ""
	Curve8name = ""
	Curve9name = ""
	Curve10name = ""
	Curve1data = []
	Curve2data = []
	Curve3data = []
	Curve4data = []
	Curve5data = []
	Curve6data = []
	Curve7data = []
	Curve8data = []
	Curve9data = []
	Curve10data = []
	pointer = 0
	curves = 0
	curve_number = 0
	curve_pointer = 0
	
	for line in contents.readlines():
			if pointer == 0 and line.find("DATAPOINT") >= 0:
				pointer = 1
					
			elif pointer == 1 and line.find("Variable.Caption") >= 0:
				pointer = 2
				C_data = line.split("=")
				C_data = C_data[1].strip()
				caption.append(str(C_data))
					
			elif pointer == 2 and line.find("Variable.Value") >= 0:
				pointer = 0
				C_data = line.split("=")
				C_data = C_data[1].strip()							
				values.append(str(C_data))
			
			elif pointer == 0 and line.find("[CURVE") >= 0 :
				curves = 1
				curve_number += 1
				curve_pointer = 0
					
			elif curves == 1:
				
					if line.find("Variable.Caption") >= 0 and curve_pointer == 0:
						curve_pointer = 1
						C_data = line.split("=")
						C_data = C_data[1].strip()
						exec("Curve"+str(curve_number)+ "name" + " =' "+ str(C_data) + "'")	
							
					elif curve_pointer == 2:
						if curve_number == 1:						
							if line == '\n':
								curves = 0				
							else:
								C_data = line.split("=")
								C_data = C_data[1].strip()
								C_data = C_data.split(",")
								Curve1data.append(C_data)
								
								
						elif curve_number == 2:						
							if line == '\n':
								curves = 0
							else:
								C_data = line.split("=")
								C_data = C_data[1].strip()
								Curve2data.append(str(C_data))
						
						elif curve_number == 3:					
							if line == '\n':
								curves = 0
							else:
								C_data = line.split("=")
								C_data = C_data[1].strip()
								Curve3data.append(str(C_data))
								
					elif line.find("D0") >= 0 and curve_pointer == 1:
						if curve_number == 1:	
							curve_pointer = 2
							C_data = line.split("=")
							C_data = C_data[1].strip()
							Curve1data.append(str(C_data))		
							
						elif curve_number == 2:
							curve_pointer = 2
							C_data = line.split("=")
							C_data = C_data[1].strip()
							Curve2data.append(str(C_data))
							
						elif curve_number == 3:
							curve_pointer = 2
							C_data = line.split("=")
							C_data = C_data[1].strip()
							Curve3data.append(str(C_data))
				
	contents.close()						
	Curve1data1 = []
#	for item in Curve1data:
# newItem = item.split(',')
# newItem = map(float, newItem)
# Curve1data1.append(newItem)
# myheader = ['col1', 'col2']
# ds = system.dataset.toDataSet(myheader, Curve1data1)					
#	Result = dict(zip(caption, values))
#	print Curve1data
#	ds = [map(float, i.split(',')) for i in Curve1data]
#	print ds
#	event.source.parent.getComponent('Table').data = ds
#	event.source.parent.getComponent('Label').text = str(Result)
#	event.source.parent.getComponent('Text Area 1').text = str(Curve1data1)
#	event.source.parent.getComponent('Label 2').text = str(Curve1name)

	
ReadFile()

```

I know where is the problem now…  
When I append the data, list is something like,

```auto
['9.4,-104.55', ['10', '-89.09'], ['10.6', '-74.55'], ['11.2', '-62.73'], ['11.8', '-50.00'], ['12.4', '-37.27'], ['13', '-20.91'], ['13.6', '-7.27'], ['14.2', '7.27'], ['14.8', '20.91'], ['15.4', '41.82'], ['16', '58.18'], ['16.6', '78.18'], ['17.2', '100.00'], ['17.8', '121.82'], ['18.4', '141.82'], ['19', '158.18'], ['19.6', '174.55'], ['20.2', '184.55'], ['20.8', '193.21'], ['21.4', '198.08'], ['22', '203.66'], ['22.6', '208.94'], ['23.2', '213.01'], ['23.7', '215.82'], ['24.3', '217.35'], ['24.9', '217.59'], ['25.5', '216.56'], ['26.1', '218.91'], ['26.7', '219.09'], ['27.3', '219.09'], ['27.9', '217.27'], ['28.5', '213.64'], ['29.1', '213.64'], ['29.7', '217.27'], ['30.3', '219.09'], ['30.9', '219.09'], ['31.5', '217.27'], ['32.1', '213.64'], ['32.7', '218.18'], ['33.3', '220.91'], ['33.9', '221.82'], ['34.5', '220.91'], ['35.1', '218.18'], ['35.7', '218.18'], ['36.3', '220.91'], ['36.9', '221.82'], ['37.5', '220.91'], ['38.1', '222.73'], ['38.7', '221.82'], ['39.3', '223.64'], ['39.9', '223.64'], ['40.5', '223.53'], ['41.1', '225.77'], ['41.7', '224.94'], ['42.3', '226.59'], ['42.9', '230.68'], ['43.4', '231.62'], ['44', '229.39'], ['44.6', '234.26'], ['45.2', '235.92'], ['45.8', '234.46'], ['46.4', '234.55'], ['47', '232.73'], ['47.6', '232.73'], ['48.2', '234.55'], ['48.8', '232.73'], ['49.4', '232.73'], ['50', '234.55'], ['50.6', '232.73'], ['51.2', '237.27'], ['51.8', '238.18'], ['52.4', '235.45'], ['53', '239.09'], ['53.6', '239.09'], ['54.2', '240.00'], ['54.8', '241.82'], ['55.4', '244.55'], ['56', '242.73'], ['56.6', '246.36'], ['57.2', '245.45'], ['57.8', '250.00'], ['58.4', '250.00'], ['59', '250.00'], ['59.6', '250.00'], ['60.2', '250.00'], ['60.8', '250.00'], ['61.4', '250.00'], ['62', '250.00'], ['62.6', '250.00'], ['63.2', '251.90'], ['63.8', '253.40'], ['64.4', '259.20'], ['65', '259.08'], ['65.6', '263.23'], ['66.1', '266.04'], ['66.7', '267.48'], ['67.3', '267.59'], ['67.9', '270.96'], ['68.5', '267.48'], ['69.1', '267.27'], ['69.7', '267.27'], ['70.3', '270.00'], ['70.9', '275.45'], ['71.5', '273.64'], ['72.1', '274.55'], ['72.7', '278.18'], ['73.3', '279.09'], ['73.9', '281.82'], ['74.5', '286.36'], ['75.1', '287.27'], ['75.7', '289.09'], ['76.3', '291.82'], ['76.9', '295.45'], ['77.5', '300.00'], ['78.1', '300.00'], ['78.7', '300.00'], ['79.3', '300.00'], ['79.9', '300.00'], ['80.5', '300.00'], ['81.1', '300.00'], ['81.7', '300.00'], ['82.3', '300.00'], ['82.9', '304.55'], ['83.5', '310.42'], ['84.1', '315.06'], ['84.7', '318.39'], ['85.3', '320.33'], ['85.9', '320.84'], ['86.4', '319.90'], ['87', '317.52'], ['87.6', '318.41'], ['88.2', '316.97'], ['88.8', '313.27'], ['89.4', '312.73'], ['90', '313.64'], ['90.6', '313.64'], ['91.2', '312.73'], ['91.8', '310.91'], ['92.4', '308.18'], ['93', '304.55'], ['93.6', '300.00'], ['94.2', '300.00'], ['94.8', '300.00'], ['95.4', '300.00'], ['96', '300.00'], ['96.6', '300.00'], ['97.2', '300.00'], ['97.8', '300.00'], ['98.4', '295.45'], ['99', '291.82'], ['99.6', '289.09'], ['100.2', '287.27'], ['100.8', '286.36'], ['101.4', '286.36'], ['102', '282.73'], ['102.6', '280.91'], ['103.2', '280.91'], ['103.8', '278.18'], ['104.4', '278.18'], ['105', '275.45'], ['105.6', '270.00'], ['106.2', '269.27'], ['106.8', '266.33'], ['107.4', '266.63'], ['108', '265.62'], ['108.6', '263.22'], ['109.1', '259.47'], ['109.7', '260.00'], ['110.3', '254.54'], ['110.9', '253.39'], ['111.5', '251.83'], ['112.1', '245.45'], ['112.7', '246.36'], ['113.3', '242.73'], ['113.9', '240.00'], ['114.5', '238.18'], ['115.1', '237.27'], ['115.7', '232.73'], ['116.3', '230.00'], ['116.9', '224.55'], ['117.5', '226.36'], ['118.1', '225.45'], ['118.7', '221.82'], ['119.3', '220.91'], ['119.9', '218.18'], ['120.5', '213.64'], ['121.1', '212.73'], ['121.7', '210.91'], ['122.3', '208.18'], ['122.9', '204.55'], ['123.5', '200.00'], ['124.1', '195.45'], ['124.7', '191.82'], ['125.3', '189.09'], ['125.9', '187.27'], ['126.5', '186.36'], ['127.1', '186.36'], ['127.7', '182.73'], ['128.3', '180.91'], ['128.9', '180.91'], ['129.5', '178.18'], ['130.1', '178.18'], ['130.7', '175.45'], ['131.3', '170.00'], ['131.9', '167.27'], ['132.5', '167.27'], ['133.1', '165.45'], ['133.7', '167.27'], ['134.3', '162.73'], ['134.9', '161.82'], ['135.5', '160.00'], ['136.1', '157.27'], ['136.7', '153.64'], ['137.3', '154.55'], ['137.9', '150.00'], ['138.5', '145.45'], ['139.1', '146.36'], ['139.7', '142.73'], ['140.3', '144.55'], ['140.9', '141.82'], ['141.5', '140.00'], ['142.1', '139.09'], ['142.7', '139.09'], ['143.3', '135.45'], ['143.9', '133.64'], ['144.5', '133.64'], ['145.1', '130.00'], ['145.7', '128.18'], ['146.3', '123.64'], ['146.9', '126.36'], ['147.5', '126.36'], ['148.1', '123.64'], ['148.7', '123.64'], ['149.3', '121.82'], ['149.9', '118.18'], ['150.5', '112.73'], ['151.1', '110.91'], ['151.7', '108.18'], ['152.3', '109.09'], ['152.9', '108.18'], ['153.5', '110.91'], ['154.1', '112.73'], ['154.7', '113.64'], ['155.3', '113.64'], ['155.9', '112.73'], ['156.5', '110.91'], ['157.1', '108.18'], ['157.7', '100.00'], ['158.3', '92.57'], ['158.9', '90.38'], ['159.5', '88.93'], ['160.1', '88.26'], ['160.7', '88.39'], ['161.2', '89.33'], ['161.8', '91.06'], ['162.4', '93.57'], ['163', '92.17'], ['163.6', '92.47'], ['164.2', '89.09'], ['164.8', '87.27'], ['165.4', '86.36'], ['166', '81.82'], ['166.6', '79.09'], ['167.2', '78.18'], ['167.8', '79.09'], ['168.4', '77.27'], ['169', '78.18'], ['169.6', '76.36'], ['170.2', '76.36'], ['170.8', '73.64'], ['171.4', '73.64'], ['172', '76.36'], ['172.6', '71.82'], ['173.2', '70.00'], ['173.8', '70.91'], ['174.4', '70.00'], ['175', '67.27'], ['175.6', '62.73'], ['176.2', '61.82'], ['176.8', '64.55'], ['177.4', '60.91'], ['180.4', '63.93'], ['180.4', '63.93']]

```

Where as I suppose to be,

```auto
['9.4,-104.55', '10,-89.09', '10.6,-74.55', '11.2,-62.73', '11.8,-50.00', '12.4,-37.27', '13,-20.91', '13.6,-7.27', '14.2,7.27', '14.8,20.91', '15.4,41.82', '16,58.18', '16.6,78.18', '17.2,100.00', '17.8,121.82', '18.4,141.82', '19,158.18', '19.6,174.55', '20.2,184.55', '20.8,193.21', '21.4,198.08', '22,203.66', '22.6,208.94', '23.2,213.01', '23.7,215.82', '24.3,217.35', '24.9,217.59', '25.5,216.56', '26.1,218.91', '26.7,219.09', '27.3,219.09', '27.9,217.27', '28.5,213.64', '29.1,213.64', '29.7,217.27', '30.3,219.09', '30.9,219.09', '31.5,217.27', '32.1,213.64', '32.7,218.18', '33.3,220.91', '33.9,221.82', '34.5,220.91', '35.1,218.18', '35.7,218.18', '36.3,220.91', '36.9,221.82', '37.5,220.91', '38.1,222.73', '38.7,221.82', '39.3,223.64', '39.9,223.64', '40.5,223.53', '41.1,225.77', '41.7,224.94', '42.3,226.59', '42.9,230.68', '43.4,231.62', '44,229.39', '44.6,234.26', '45.2,235.92', '45.8,234.46', '46.4,234.55', '47,232.73', '47.6,232.73', '48.2,234.55', '48.8,232.73', '49.4,232.73', '50,234.55', '50.6,232.73', '51.2,237.27', '51.8,238.18', '52.4,235.45', '53,239.09', '53.6,239.09', '54.2,240.00', '54.8,241.82', '55.4,244.55', '56,242.73', '56.6,246.36', '57.2,245.45', '57.8,250.00', '58.4,250.00', '59,250.00', '59.6,250.00', '60.2,250.00', '60.8,250.00', '61.4,250.00', '62,250.00', '62.6,250.00', '63.2,251.90', '63.8,253.40', '64.4,259.20', '65,259.08', '65.6,263.23', '66.1,266.04', '66.7,267.48', '67.3,267.59', '67.9,270.96', '68.5,267.48', '69.1,267.27', '69.7,267.27', '70.3,270.00', '70.9,275.45', '71.5,273.64', '72.1,274.55', '72.7,278.18', '73.3,279.09', '73.9,281.82', '74.5,286.36', '75.1,287.27', '75.7,289.09', '76.3,291.82', '76.9,295.45', '77.5,300.00', '78.1,300.00', '78.7,300.00', '79.3,300.00', '79.9,300.00', '80.5,300.00', '81.1,300.00', '81.7,300.00', '82.3,300.00', '82.9,304.55', '83.5,310.42', '84.1,315.06', '84.7,318.39', '85.3,320.33', '85.9,320.84', '86.4,319.90', '87,317.52', '87.6,318.41', '88.2,316.97', '88.8,313.27', '89.4,312.73', '90,313.64', '90.6,313.64', '91.2,312.73', '91.8,310.91', '92.4,308.18', '93,304.55', '93.6,300.00', '94.2,300.00', '94.8,300.00', '95.4,300.00', '96,300.00', '96.6,300.00', '97.2,300.00', '97.8,300.00', '98.4,295.45', '99,291.82', '99.6,289.09', '100.2,287.27', '100.8,286.36', '101.4,286.36', '102,282.73', '102.6,280.91', '103.2,280.91', '103.8,278.18', '104.4,278.18', '105,275.45', '105.6,270.00', '106.2,269.27', '106.8,266.33', '107.4,266.63', '108,265.62', '108.6,263.22', '109.1,259.47', '109.7,260.00', '110.3,254.54', '110.9,253.39', '111.5,251.83', '112.1,245.45', '112.7,246.36', '113.3,242.73', '113.9,240.00', '114.5,238.18', '115.1,237.27', '115.7,232.73', '116.3,230.00', '116.9,224.55', '117.5,226.36', '118.1,225.45', '118.7,221.82', '119.3,220.91', '119.9,218.18', '120.5,213.64', '121.1,212.73', '121.7,210.91', '122.3,208.18', '122.9,204.55', '123.5,200.00', '124.1,195.45', '124.7,191.82', '125.3,189.09', '125.9,187.27', '126.5,186.36', '127.1,186.36', '127.7,182.73', '128.3,180.91', '128.9,180.91', '129.5,178.18', '130.1,178.18', '130.7,175.45', '131.3,170.00', '131.9,167.27', '132.5,167.27', '133.1,165.45', '133.7,167.27', '134.3,162.73', '134.9,161.82', '135.5,160.00', '136.1,157.27', '136.7,153.64', '137.3,154.55', '137.9,150.00', '138.5,145.45', '139.1,146.36', '139.7,142.73', '140.3,144.55', '140.9,141.82', '141.5,140.00', '142.1,139.09', '142.7,139.09', '143.3,135.45', '143.9,133.64', '144.5,133.64', '145.1,130.00', '145.7,128.18', '146.3,123.64', '146.9,126.36', '147.5,126.36', '148.1,123.64', '148.7,123.64', '149.3,121.82', '149.9,118.18', '150.5,112.73', '151.1,110.91', '151.7,108.18', '152.3,109.09', '152.9,108.18', '153.5,110.91', '154.1,112.73', '154.7,113.64', '155.3,113.64', '155.9,112.73', '156.5,110.91', '157.1,108.18', '157.7,100.00', '158.3,92.57', '158.9,90.38', '159.5,88.93', '160.1,88.26', '160.7,88.39', '161.2,89.33', '161.8,91.06', '162.4,93.57', '163,92.17', '163.6,92.47', '164.2,89.09', '164.8,87.27', '165.4,86.36', '166,81.82', '166.6,79.09', '167.2,78.18', '167.8,79.09', '168.4,77.27', '169,78.18', '169.6,76.36', '170.2,76.36', '170.8,73.64', '171.4,73.64', '172,76.36', '172.6,71.82', '173.2,70.00', '173.8,70.91', '174.4,70.00', '175,67.27', '175.6,62.73', '176.2,61.82', '176.8,64.55', '177.4,60.91', '180.4,63.93', '180.4,63.93']

```

Note the single quote difference. Just don’t know how do i not have that appended.

Thanks a lot @raymond1982

---

<div class="post-metadata">

**Author:** ![raymond1982](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/raymond1982/32/6490_2.png) [@raymond1982](https://forum.inductiveautomation.com/u/raymond1982)\
**Post date:** [November 1, 2018, 5:59pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/11 "2018-11-01T17:59:54Z")

</div>

Ok this should work and help out a little. I read the code you posted a couple of times and i think i captured everything you were doing. If not let me know and ill be happy to help. You will need to put your component references back in where appropriate.

```auto

CURVE = "CURVE"
DATAPOINT = "DATAPOINT"

def getClassToTypeMatrix():
	return {CURVE: CurveHandler, DATAPOINT:DatapointHandler}

def getClassForType(type_):
	return getClassToTypeMatrix().get(type_)

def groomDataBlockName(string):
	"""Groom brackets and spaces from type name"""
	return string.strip().replace('[', '').replace(']', '')

def assertAndCastType(value):
	"""Coerce string to appropriate python type."""
	value = str(value).lower().strip()
	try:
   		return float(value)
	except ValueError:
	  pass
	if value.isnumeric():
		return int(value)
	elif value in ['false', 'true']:
		return True if value == 'true' else False
	return value

class Coordinates:
	"""Basic Coordinates class."""
	
	def __init__ (self, x, y):
		self._x = x
		self._y = y
	
	@property
	def x(self):
		return self._x
	
	@property
	def y(self):
		return self._y

	def __call__ (self):
		return (self.x, self.y)

	def __repr__ (self):
		return '< Coordinates [x=%s, y=%s] >' % (self.x, self.y)

	def points(self):
		return [self.x, self.y]

class GenericDataBlock:
	
	def __init__ (self, name, type_):
		self.name = name
		self.type_ = type_
		self.coords = []
	
	def __repr__ (self):
		return '< Handler name=%s, type=%s, order=%s >' % (self.name, self.type_, self.order)

	def load(self, chunk):
		pass

	def getHeader(self):
		return ['x', 'y']

	def getCoordsArray(self):
		return []
	
	@property
	def order(self):
		order = [i for i in self.name if i.isnumeric()]
		if len(order) > 0:
			return int(''.join(order))
		return -99

	def getResult(self):
		return []

class CurveHandler(GenericDataBlock):
	
	def load(self, chunk):
		key_value_groups = chunk.split('\n')
		for key_value_pair in key_value_groups:
			#print key_value_pair
			# Key value pair is only valid if the line contains a '='
			if '=' in key_value_pair:
				# lets get rid of spaces and split on the '='
				key_value_pair = key_value_pair.replace(' ','').split('=')
				#the split must be equal otherwise it is invalid.
				if len(key_value_pair) == 2:
					# using spread operator for speed
					key, value = key_value_pair
					# converting the periods to underscores so we can set the
					# key as the attribute.
					if '.' in key:
						key = key.replace('.', '_')
						# setting the key value as class attributes.
						# since thay aren coords we will use them as instance vars.
						setattr(self, key, assertAndCastType(value))
					else:
						# Test for x y seperator in string
						if ',' in value:
							# split on seperator
							coords = value.split(',')
							if len(coords) == 2:
								x, y = coords
								self.coords.append(
									Coordinates(assertAndCastType(x), assertAndCastType(y))
								)

	def getCoordsArray(self):
		return [data() for data in self.coords]

	def getDataset(self):
		return system.dataset.toDataSet(self.getHeader(), self.getCoordsArray())

class DatapointHandler(GenericDataBlock):

	def load(self, chunk):
		key_value_groups = chunk.split('\n')
		for key_value_pair in key_value_groups:
			# Key value pair is only valid if the line contains a '='
			if '=' in key_value_pair:
				# lets get rid of spaces and split on the '='
				key_value_pair = key_value_pair.replace(' ','').split('=')
				# the split must be equal other wis it is invalid.
				if len(key_value_pair) == 2:
					# using spread operator for speed
					key, value = key_value_pair
					# converting the periods to underscores so we can set the
					# key as the attribute.
					key = key.replace('.', '_')
					# setting the key value as class attributes.
					setattr(self, key, assertAndCastType(value))
		
	def getResult(self):
		return self.Variable_Caption 

	def getVariableCaption(self):
		return getattr(self, 'Variable_Caption', 'Empty')
	
	def getVariableValue(self):
		return getattr(self, 'Variable_Value', 'Empty')

	def getResult(self):
		return (self.getVariableCaption(), self.getVariableValue())

def ProcessASCList(asc_list):		

 		data_block_owner = None
 		block_data = ''
 		block_start = False
 		data_object_array = []
 		
 		for line in asc_list:
 			# testing for the '[' indicates we have hit another group.
 			# if black_start is True that means we are at the end of this block.
			if '[' in line and block_start:
				# Clear block start.
				block_start = False
				# verify that we have a obj assigned as the block owner.
				if data_block_owner:
					# load block to handler.
					data_block_owner.load(block_data)
					# append handler to array.
					data_object_array.append(data_block_owner)
					# clear block data.
					block_data = ''
 			
 			elif block_start:
 				# We are in the block here. Append line and start the loop over
 				# The continue statment cuts the loop short saving us from 
 				# testing conditions that are irrelevant at this stage.
 				block_data = block_data + line
 				continue
			# clear type_
			type_ = None
			if CURVE in line:
				type_ = CURVE
		
 			elif DATAPOINT in line:
 				type_ = DATAPOINT
 			
 			if type_ and not block_start:
				handler = getClassForType(type_)
				if handler:
					data_block_owner = handler(groomDataBlockName(line), type_)
					block_start = True

		return data_object_array

def getListFromFile(path):
	# verify file is present.
	if system.file.fileExists(path):
		# open file
		asc_file = open(path, "r")
		# read the contents to a list in memory
		asc_list = [line for line in asc_file.readlines()]
		# close file object.
		asc_file.close()
		# return the list for iteration
		return asc_list
	return ''

def getHandlerByName(name, handler_array):
	for handler in handler_array:
		if handler.name == name:
			return handler
	raise Exception('Unknown Handler Name: %s' % name)
	return GenericDataBlock('Uknown', 'Uknown')

def filterHandlerListOnType(type_, handler_array):
	return [handler for handler in handler_list if handler.type_ == type_]

def orderHandlerList(handler_array):
	return sorted(handler_array, key=lambda x: x.order)

path = "C:\\Users\\kshah2\\Desktop\\MDR1.asc"
asc_list = getListFromFile(path)
handler_list = ProcessASCList(asc_list)

# Curve Example
handler_name = 'CURVE1'
handler = getHandlerByName(handler_name, handler_list)
print handler.getCoordsArray()
print handler.getDataset() # <-- What you asked for.

# Datapoint example
handler_name = 'DATAPOINT1'
handler = getHandlerByName(handler_name, handler_list)
print handler.getResult()

# Example
# print list of the datapoint names
print [handler.name for handler in handler_list]

# Example
# Filter handler list by type
type_0 = CURVE
filtered_handler_list = filterHandlerListOnType(type_0, handler_list)
print filtered_handler_list

# Example
# Combine result values for multiple datapoints.
results_array = [handler.getResult() for handler in filterHandlerListOnType(DATAPOINT, handler_list)]
print results_array

# Example
# Merge Results caption and value
results_as_dict = dict(results_array)
print results_as_dict

# Example 
# Order DATAPOINT handler list
dphl = filterHandlerListOnType(DATAPOINT, handler_list)
dphl_ordered = orderHandlerList(dphl)
print dphl_ordered

#event.source.parent.getComponent('Text Area').text = ''.join(asc_list)
#event.source.running = 0
#event.source.value = 0

```

---

<div class="post-metadata">

**Author:** ![kaushik.shah](https://avatars.discourse-cdn.com/v4/letter/k/34f0e0/32.png) [@kaushik.shah](https://forum.inductiveautomation.com/u/kaushik.shah)\
**Post date:** [November 1, 2018, 6:18pm UTC](https://forum.inductiveautomation.com/t/python-lists-to-dataset/20473/12 "2018-11-01T18:18:29Z")

</div>

Holy Smokes…  
could never have thought doing something like this.  
Thanks a lot sir.  
I got that working but this is Great…