熊猫获得列平均值/平均值

I can’t get the average or mean of a column in pandas. A have a dataframe. Neither of things I tried below gives me the average of the column weight

>>> allDF 
         ID           birthyear  weight
0        619040       1962       0.1231231
1        600161       1963       0.981742
2      25602033       1963       1.3123124     
3        624870       1987       0.94212

The following returns several values, not one:

allDF[['weight']].mean(axis=1)

So does this:

allDF.groupby('weight').mean()

If you only want the mean of the weight column, select the column (which is a Series) and call .mean():

In [479]: df
Out[479]: 
         ID  birthyear    weight
0    619040       1962  0.123123
1    600161       1963  0.981742
2  25602033       1963  1.312312
3    624870       1987  0.942120

In [480]: df["weight"].mean()
Out[480]: 0.83982437500000007

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.

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()

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

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 use either of the two statements below:

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

You can easily followthe 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())

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