Returning the max and min values and indexes with Numpy Python

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I am trying to write a function that prints out all the max and min values and their indexes for the specified columns in the input.csv file. The columns I want to return the max values of are referenced in the max_columns variable and the one to return min values are in the min_columns variable. However it doesnt go through the whole array values as intended

input.csv file:

element,LNPT,SNPT,NLP,NSP,TNT,TPnL,MxPnL,MnPnL,MxU,MxD
[ 2.  2. 30.],0,0,4,4,8,-0.1,-0.0,-0.1,17127,-3
[ 2.  2. 40.],0,0,2,2,4,0.0,-0.0,-0.0,17141,-3
[ 2.  2. 50.],0,0,2,2,4,0.0,-0.0,-0.0,17139,-3
[ 2.  2. 60.],2,0,6,6,12,0.5,2.3,-1.9,17015,-3
[ 2.  2. 70.],1,0,4,4,8,0.3,0.3,-0.0,17011,-3

Code:

my_data = pd.read_csv('input.csv').to_numpy()
max_columns= np.array([1,2,3,7,8,10])
min_columns = np.array([4,5,6,9])

def max_vals():
    results = np.max(my_data[:,max_columns])
    index = np.argmax(results)
    return results, index
    
def min_vals():
    results = np.min(my_data[:,min_columns])
    index = np.argmin(results)
    return results, index

max_values, max_index= max_vals()
min_values, min_index= min_vals()
pandas numpy function arrays python
1 Answers

Since you already have data frame in pandas , let us try pandas way

df = pd.read_csv('input.csv')
max_columns= np.array([1,2,3,7,8,10])
min_columns = np.array([4,5,6,9])

# max 
index = df[max_columns].idxmax()
values = df[max_columns].max()

# min 
index = df[min_columns].idxmin()
values = df[min_columns].min()
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