Pandas Get Column Average/Mean

Try df.mean(axis=0) , axis=0 argument calculates the column wise mean of the dataframe so the result will be axis=1 is row wise mean so you are getting multiple values.

Do try to give print (df.describe()) a shot. I hope it will be very helpful to get an overall description of your dataframe.

Mean for each column in df :

    A   B   C
0   5   3   8
1   5   3   9
2   8   4   9

df.mean()

A    6.000000
B    3.333333
C    8.666667
dtype: float64

and if you want average of all columns:

df.stack().mean()
6.0

you can use

df.describe() 

you will get basic statistics of the dataframe and to get mean of specific column you can use

df["columnname"].mean()

You can also access a column using the dot notation (also called attribute access) and then calculate its mean:

df.your_column_name.mean()

You can use either of the two statements below:

numpy.mean(df['col_name'])
# or
df['col_name'].mean()

Additionally if you want to get the round value after finding the mean.

#Create a DataFrame
df1 = {
    'Subject':['semester1','semester2','semester3','semester4','semester1',
               'semester2','semester3'],
   'Score':[62.73,47.76,55.61,74.67,31.55,77.31,85.47]}
df1 = pd.DataFrame(df1,columns=['Subject','Score'])

rounded_mean = round(df1['Score'].mean()) # specified nothing as decimal place
print(rounded_mean) # 62

rounded_mean_decimal_0 = round(df1['Score'].mean(), 0) # specified decimal place as 0
print(rounded_mean_decimal_0) # 62.0

rounded_mean_decimal_1 = round(df1['Score'].mean(), 1) # specified decimal place as 1
print(rounded_mean_decimal_1) # 62.2

You can simply go for: df.describe() that will provide you with all the relevant details you need, but to find the min, max or average value of a particular column (say 'weights' in your case), use:

    df['weights'].mean(): For average value
    df['weights'].max(): For maximum value
    df['weights'].min(): For minimum value

Do note that it needs to be in the numeric data type in the first place.

 import pandas as pd
 df['column'] = pd.to_numeric(df['column'], errors='coerce')

Next find the mean on one column or for all numeric columns using describe().

df['column'].mean()
df.describe()

Example of result from describe:

          column 
count    62.000000 
mean     84.678548 
std     216.694615 
min      13.100000 
25%      27.012500 
50%      41.220000 
75%      70.817500 
max    1666.860000

You can easily follow the following code

import pandas as pd 
import numpy as np 
        
classxii = {'Name':['Karan','Ishan','Aditya','Anant','Ronit'],
            'Subject':['Accounts','Economics','Accounts','Economics','Accounts'],
            'Score':[87,64,58,74,87],
            'Grade':['A1','B2','C1','B1','A2']}

df = pd.DataFrame(classxii,index = ['a','b','c','d','e'],columns=['Name','Subject','Score','Grade'])
print(df)

#use the below for mean if you already have a dataframe
print('mean of score is:')
print(df[['Score']].mean())