Reputation: 151
python: import data from text
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.
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
Upvotes: 2
Views: 264
Answers (2)
Reputation: 863246
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]
Upvotes: 1
Reputation: 151
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.
Upvotes: 0
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