Problems with converting to numeric

[deanhystad]

Split does not take out spaces. Split makes multiple splits a string into multiple substrings using a delimiter.

string = '123-456-7890'
substrings = string.split('-')
print(substrings)

Output:
['123', '456', '7890']

So we now know that this makes no sense and you should never do it:

results_df['MIC/RESULT'].str.split(' ').str[1]

How about this? Does it ever make sense to do this?

results_df['MIC/RESULT'].replace(' ','')

I made a dataframe that has strings that contain numbers and digits and I apply try your code.

import pandas as pd

df = pd.DataFrame({"A": [" 1.23 ", "1 23"]})
df["B"] = df['A'].replace(' ','')
print(df)

Output:
        A       B
0   1.23    1.23 
1    1 23    1 23

As expected, the replace code does nothing because none of the rows in "A" are a single blank. Is that what you wanted to happen? Find rows in MIC/RESULT that are a single space and replace that with an empty string?

And what do you mean by this?

ID MIC1 MIC_FLOAT MIC_FOR_RANGES
1 = 0.0625 0.0625 0.0625
2 =0.5 0.5000 0.5000
3 =0.0625 0.0625 0.0625
4 =0.0625 0.0625 0.0625

Is this a file that you are reading into a dataframe?

I copied the above to a file named text.txt and use pandas.read_csv to load it into a dataframe. This is a little tricky since there two separators in this file: an equal sign that may be preceeded or followed by space, and a space. This requires using a special separator what is a regular expression.

import pandas as pd

df = pd.read_csv("test.txt", sep=r"\s*=\s*|\s+", engine="python")
print(df)
print(df.dtypes)

Output:
   ID    MIC1  MIC_FLOAT  MIC_FOR_RANGES
0   1  0.0625     0.0625          0.0625
1   2  0.5000     0.5000          0.5000
2   3  0.0625     0.0625          0.0625
3   4  0.0625     0.0625          0.0625
ID                  int64
MIC1              float64
MIC_FLOAT         float64
MIC_FOR_RANGES    float64
dtype: object

The sep argument is a regular expression that says the separator might be an equal sign with leading and trailing spaces (\s*=\s*) or it might be a space (\s). Since this appears to be a poorly formatted file (not always using the same formatting) I decided to expand the second separator to one or more spaces (\s+). I had to set the parser engine to "python" so I could use an expression for the separator.

https://pandas.pydata.org/docs/reference...d_csv.html

Notice that the datatypes are all numbers without having to do any processing of the dataframe columns.