pandas get column average/mean
Question:
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()
Answers:
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.loc[:, 'weight'].mean()
Out[480]: 0.83982437500000007
Do try to give print (df.describe())
a shot. I hope it will be very helpful to get an overall description of your dataframe.
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.
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
You can use either of the two statements below:
numpy.mean(df['col_name'])
# or
df['col_name'].mean()
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())
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 use the method agg
(aggregate
):
df.agg('mean')
It’s possible to apply multiple statistics:
df.agg(['mean', 'max', 'min'])
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.loc[:, 'weight'].mean()
Out[480]: 0.83982437500000007
Do try to give print (df.describe())
a shot. I hope it will be very helpful to get an overall description of your dataframe.
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.
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
You can use either of the two statements below:
numpy.mean(df['col_name'])
# or
df['col_name'].mean()
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())
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 use the method agg
(aggregate
):
df.agg('mean')
It’s possible to apply multiple statistics:
df.agg(['mean', 'max', 'min'])