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() 11 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["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.
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()) 
