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I tried importing float numbers from P-I curve.txt file which contains my data. however i get an error when converting this into float. i used the following code.

with open('C:/Users/Kevin/Documents/4e Jaar/fotonica/Metingen/P-I curve.txt') as csvfile:
    data= csv.reader(csvfile, delimiter = '\t')
    current=[]

    P_15=[]
    P_20=[]
    P_25=[]
    P_30=[]
    P_35=[]
    P_40=[]
    P_45=[]
    P_50=[]

    for row in data:

        current.append(float(row[0].replace(',','.')))  
        P_15.append(float(row[2].replace(',','.')))
        P_20.append(float(row[4].replace(',','.')))
        P_25.append(float(row[6].replace(',','.')))
        P_30.append(float(row[8].replace(',','.')))
        P_35.append(float(row[10].replace(',','.')))
        P_40.append(float(row[12].replace(',','.')))
        P_45.append(float(row[14].replace(',','.')))
        P_50.append(float(row[16].replace(',','.')))

with this code i got the following error which i understand that row 2 is a string but if so then why did this error not occur for row 1. Is there any other data to import float numbers without using csv import? I have copied and pasted the data from excel to a .txt file.

returned error:

  File "C:/Users/Kevin/Documents/Python Scripts/P-I curves.py", line 29, in <module>
    P_15.append(float(row[2].replace(',','.')))

ValueError: could not convert string to float:

I tried another following code:

import pandas as pd

df=pd.read_csv('C:/Users/Kevin/Documents/4e Jaar/fotonica/Metingen/P-I curve.txt', decimal=',', sep='\t',header=0,names=['current','15','20','25','30','35','40','45','50']  )

#curre=df['current']
print(current)

The txt file has a header and looks like this:

1.8   1.9  0.4     1.9  0.4     1.9  0.4     1.9       0.4
3.8   1.9  1.3     1.9  1.3     1.9  1.3     1.9       1.2
5.8   2.0  2.5     2.0  2.4     2.0  2.3     2.0       2.2
7.8   2.0  3.7     2.0  3.6     2.0  3.5     2.0       3.4
9.8   2.1  5.2     2.0  5.1     2.0  4.9     2.0       4.7
11.8  2.1  6.9     2.1  6.7     2.1  6.4     2.1       6.1
13.8  2.1  9.0     2.0  8.6     2.1  8.2     2.1       7.8
15.8  2.1  11.5    2.1  10.8    2.1  10.2    2.1       9.7
17.8  2.2  14.7    2.2  13.7    2.2  12.7    2.2      11.8
19.8  2.2  19.5    2.2  17.5    2.2  15.9    2.2      14.5
21.8  2.2  28.9    2.2  23.6    2.2  20.3    2.2      17.9
23.8  2.3  125.8   2.2  38.4    2.2  27.8    2.2      22.8
25.8  2.3  1669.0  2.3  634.0   2.3  51.7    2.3      31.4
27.8  2.3  3142.0  2.3  2154.0  2.3  982.0   2.3      62.2
29.8  2.3  4560.0  2.3  3594.0  2.3  2460.0  2.3    1075.0
31.8  2.3  5950.0  2.3  5010.0  2.3  3872.0  2.3    2540.0
33.8  2.4  7320.0  2.4  6360.0  2.4  5230.0  2.3    3880.0
35.8  2.4  8670.0  2.4  7700.0  2.4  6550.0  2.4    5210.0
37.8  NaN  NaN     NaN  NaN     2.4  7850.0  2.4    6480.0
39.8  NaN  NaN     NaN  NaN     NaN  NaN     NaN       NaN
41.8  NaN  NaN     NaN  NaN     NaN  NaN     NaN       NaN
Name: current, dtype: float64

python seems to be returning everything instead of just line 1 which i want by printing the header current. I only want to take this line so i can save it as in an array. But How do i specifically draw the line with header current out of the data?.

I am not sure why it returned everything but i think that there is something wrong with encoding because i copied and pasted the data from excel.

Please look at the image of how the .txt looks like when copied from excel.

enter image description here

i tried out another short code (i also deleted the header manually for the .txt file!!), see description below:

data=np.loadtxt('C:/Users/Kevin/Documents/4e Jaar/fotonica/Metingen/ttest.txt',delimiter='\t')

data=float(data.replace(',','.'))


print(data[0])

with this code, i get the followin error.

ValueError: could not convert string to float: b'1,8'

I find this weird to occur. Is floating and replacing not enough for this

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3
  • @usr2564301 i copied the data from excel and pasted into a txt file. This would save time. Commented Jan 11, 2018 at 10:34
  • Much better approach would be to use Pandas: import pandas as pd; df=pd.read_csv(filename, decimal=',', sep='\t') Commented Jan 11, 2018 at 10:51
  • @Bartłomiej i approached your suggestions. But this did not return sufficient results, please see the code above Commented Jan 11, 2018 at 11:59

2 Answers 2

Reset to default
1

I think you need omit header=0:

df=pd.read_csv('C:/Users/Kevin/Documents/4e Jaar/fotonica/Metingen/P-I curve.txt', 
                decimal=',', 
                sep='\t',
                names=['current','15','20','25','30','35','40','45','50'])

EDIT:

df=pd.read_csv('ttest.txt', 
                decimal=',', 
                sep='\t',
                names=['current','15','20','25','30','35','40','45','50'])
print (df)
    current      15      20      25      30      35      40      45     50
0       1.8     0.4     0.4     0.4     0.4     0.4     0.4     0.3    0.3
1       3.8     1.3     1.3     1.3     1.2     1.2     1.1     1.1    1.1
2       5.8     2.5     2.4     2.3     2.2     2.2     2.1     2.0    1.9
3       7.8     3.7     3.6     3.5     3.4     3.3     3.1     3.0    2.9
4       9.8     5.2     5.1     4.9     4.7     4.5     4.3     4.1    4.0
5      11.8     6.9     6.7     6.4     6.1     5.9     5.6     5.3    5.1
6      13.8     9.0     8.6     8.2     7.8     7.4     7.0     6.6    6.3
7      15.8    11.5    10.8    10.2     9.7     9.1     8.6     8.0    7.6
8      17.8    14.7    13.7    12.7    11.8    11.0    10.3     9.6    9.0
9      19.8    19.5    17.5    15.9    14.5    13.3    12.2    11.3   10.5
10     21.8    28.9    23.6    20.3    17.9    16.0    14.5    13.2   12.2
11     23.8   125.8    38.4    27.8    22.8    19.6    17.2    15.4   14.1
12     25.8  1669.0   634.0    51.7    31.4    24.5    20.6    17.9   16.2
13     27.8  3142.0  2154.0   982.0    62.2    33.1    25.3    21.0   18.5
14     29.8  4560.0  3594.0  2460.0  1075.0    60.0    32.6    25.0   21.3
15     31.8  5950.0  5010.0  3872.0  2540.0   903.0    49.9    30.8   24.6
16     33.8  7320.0  6360.0  5230.0  3880.0  2294.0   387.0    40.9   28.8
17     35.8  8670.0  7700.0  6550.0  5210.0  3621.0  1733.0    71.0   34.8
18     37.8     NaN     NaN  7850.0  6480.0  4880.0  3026.0   751.0   44.6
19     39.8     NaN     NaN     NaN     NaN  6100.0  4240.0  1998.0   70.2
20     41.8     NaN     NaN     NaN     NaN     NaN     NaN  3161.0  650.0

#list from column 15 with all values include NaNs
L1 = df['15'].tolist()
print (L1)
[0.4, 1.3, 2.5, 3.7, 5.2, 6.9, 9.0, 11.5, 14.7, 19.5, 28.9, 125.8, 1669.0, 
 3142.0, 4560.0, 5950.0, 7320.0, 8670.0, nan, nan, nan]

#list from column 15 with removing NaNs
L2 = df['15'].dropna().tolist()
print (L2)
[0.4, 1.3, 2.5, 3.7, 5.2, 6.9, 9.0, 11.5, 14.7, 19.5, 28.9, 125.8, 1669.0, 
 3142.0, 4560.0, 5950.0, 7320.0, 8670.0]

#convert all NaNs in all columns to 0
df = df.fillna(0)
print (df)
    current      15      20      25      30      35      40      45     50
0       1.8     0.4     0.4     0.4     0.4     0.4     0.4     0.3    0.3
1       3.8     1.3     1.3     1.3     1.2     1.2     1.1     1.1    1.1
2       5.8     2.5     2.4     2.3     2.2     2.2     2.1     2.0    1.9
3       7.8     3.7     3.6     3.5     3.4     3.3     3.1     3.0    2.9
4       9.8     5.2     5.1     4.9     4.7     4.5     4.3     4.1    4.0
5      11.8     6.9     6.7     6.4     6.1     5.9     5.6     5.3    5.1
6      13.8     9.0     8.6     8.2     7.8     7.4     7.0     6.6    6.3
7      15.8    11.5    10.8    10.2     9.7     9.1     8.6     8.0    7.6
8      17.8    14.7    13.7    12.7    11.8    11.0    10.3     9.6    9.0
9      19.8    19.5    17.5    15.9    14.5    13.3    12.2    11.3   10.5
10     21.8    28.9    23.6    20.3    17.9    16.0    14.5    13.2   12.2
11     23.8   125.8    38.4    27.8    22.8    19.6    17.2    15.4   14.1
12     25.8  1669.0   634.0    51.7    31.4    24.5    20.6    17.9   16.2
13     27.8  3142.0  2154.0   982.0    62.2    33.1    25.3    21.0   18.5
14     29.8  4560.0  3594.0  2460.0  1075.0    60.0    32.6    25.0   21.3
15     31.8  5950.0  5010.0  3872.0  2540.0   903.0    49.9    30.8   24.6
16     33.8  7320.0  6360.0  5230.0  3880.0  2294.0   387.0    40.9   28.8
17     35.8  8670.0  7700.0  6550.0  5210.0  3621.0  1733.0    71.0   34.8
18     37.8     0.0     0.0  7850.0  6480.0  4880.0  3026.0   751.0   44.6
19     39.8     0.0     0.0     0.0     0.0  6100.0  4240.0  1998.0   70.2
20     41.8     0.0     0.0     0.0     0.0     0.0     0.0  3161.0  650.0

#list from column 15
L3 = df['15'].tolist()
print (L3)
[0.4, 1.3, 2.5, 3.7, 5.2, 6.9, 9.0, 11.5, 14.7, 19.5, 28.9, 125.8, 1669.0, 
 3142.0, 4560.0, 5950.0, 7320.0, 8670.0, 0.0, 0.0, 0.0]
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10 Comments

python must have picked up something else when copying and pasting this data
If need no data like excel screen, it is in pandas not possible, for it is always used NaN. Check missing data in docs
Thank you for your reply. I understand that no data is present, a value NaN is returned. That is correct as NaN is present in the .txt and no data is given. My goal here however is to return any row from the data imported by panda. The problem is that when i try to print out any desired row, it returns all rows.
Do you try loc? Like for row with index=4 df = df.loc[4] ?
I still did not get line 4 when using this code df = df.loc[4] . It prints out exactly as shown in the problem above
|
0

if importing data from .txt file as csv, the missing data should be added. So in this by manually adding 0 to the .txt file and retrying this code with open('C:/Users/Kevin/Documents/4e Jaar/fotonica/Metingen/P-I curve.txt') as csvfile: data= csv.reader(csvfile, delimiter = '\t') current=[]

P_15=[]
P_20=[]
P_25=[]
P_30=[]
P_35=[]
P_40=[]
P_45=[]
P_50=[]

for row in data:

    current.append(float(row[0].replace(',','.')))  
    P_15.append(float(row[2].replace(',','.')))

 print(P_15)

it works for any row to print out.

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