How to extract and group values from a Pandas Dataframe column based on the spread of the values

Question

I have a pandas column containing some numbers for example:

A
1.10
1.11
1.00
2.10
2.11
2.10
3.10
3.11
3.12

I want to group the numbers in groups of 1's 2's and 3's how can I do that? so basically I want to specify the spread so if the spread is for example 1.00+-0.5 then I group all numbers falling within that range and then take the average of them by putting them in an array. I have tried the groupby pd.cut() but did not achieve the expected results. Please help!

Answer 1

Your data.

    d = {"A" : [1.10, 1.11, 1.00, 2.10, 2.11, 2.10, 3.10, 3.11, 3.12]}
    df = pd.DataFrame.from_dict(d)

To group your data into 1, 2 and 3 - you can simply take the first int of the float and convert it into a string in a new column (here: grouping_of_A).

    df["grouping_of_A"] = df["A"].map(lambda x: str(x)[0])

    print(df)
       A          grouping_of_A
    0  1.10             1
    1  1.11             1
    2  1.00             1
    3  2.10             2
    4  2.11             2
    5  2.10             2
    6  3.10             3
    7  3.11             3
    8  3.12             3

    df.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 9 entries, 0 to 8
    Data columns (total 2 columns):
    #   Column         Non-Null Count  Dtype  
    ---  ------         --------------  -----  
    0   A              9 non-null      float64
    1   grouping_of_A  9 non-null      object 
    dtypes: float64(1), object(1)
    memory usage: 272.0+ bytes