Pandas issue pulling a number from a string input from a CSV file

I have a csv file with the contents:

StrainMax 0.125
StrainMin -0.125
cycles_sec 10
time_percent 50

I read it in with:

df = pd.read_csv('Strains_SI.csv', index_col=None, header=None)

Then this:

StrainMax = df.iat[0,0]
StrainMin = df.iat[1,0]
cycles_sec = df.iat[2,0]
time_percent = df.iat[3,0]
print(StrainMax)
print(StrainMin)
print(cycles_sec)
print(time_percent)

And what I get is this (I believe these are strings):

StrainMax 0.125
StrainMin -0.125
cycles_sec 10
time_percent 50

But what I want is this:

0.125 (I want an float here)
-0.125 (I want an float here)
10 (I want an integer here)
50 (I want an integer here)

Is this possible?

I tried numerous Pandas commands and can't get it. I am expecting floats and integers of the numbers only.

I notice there are no commas in your CSV file. If you check the documentation for the .read_csv method, you'll see that it does by default expect a comma separator. If you don't actually have comma separation, it can be useful to specify the actual separator to save effort later. Here we can read in the file using a space as the separator:

I saved your data to data.csv (I could have done data.txt just as easily.)

StrainMax 0.125
StrainMin -0.125
cycles_sec 10
time_percent 50

import pandas as pd

df = pd.read_csv('data.csv',sep=' ',header=None)

Note that I also set header=None so that the first row of the file would be treated as data and not column names. In this case, pandas makes up column names 0 and 1.

You can see that it separates the label from the numerical value right away so you don't have to do that manually with each string. The extra convenient thing about that is that pandas now sees an entirely numeric column. If we look at df.dtypes we now have:

The column labeled 1 is now of type float64. Your four float values are then accessible by df[column][row] as in: df[1][0] df[1][1] df[1][2] df[1][3]

To be a little slicker in your usage, we can go a bit further. Instead of just telling pandas to not use the first row as headers, we can instead tell it what each column is and even set the name of the measurement as the index for convenience:

import pandas as pd

df = pd.read_csv('data.csv',sep=' ',names=['measurement','value'])
df.set_index('measurement',inplace=True)

Now you can get at your data quite nicely with df.loc['StrainMax']['value'] to get 0.125 (which is indeed a float).

PS. If you ever find that pandas didn't set the data type of a column correctly, you can force the type. In this example, it would be

df['value'] = df['value'].astype(float)