Pandas Dataframe 分组/组合列？

Question

I'm new to Pandas, and I'm having a horrible time figuring out datasets. I have a csv file I've read in using pandas.read_csv, dogData , that looks as follows:

The column names are dog breeds, the first line [0] refers to the size of the dogs, and beyond that there's a bunch of numerical values. The very first column has string description that I need to keep, but isn't relevant to the question. The last column for each size category contains separate "Average" values. (Note that it changed the "Average" columns to "Average.1", "Average.2" and so on, to take care of them not being unique)

Basically, I want to "group" by the first row - so all "small" dog values will be averaged except the "small" average column, and so on. The result would look like something like this:

The existing "Average" columns should not be included in the new average being calculated. The existing "Average" columns for each size don't need to be altered at all. All "small" breed values should be averaged, all "medium" breed values should be averaged, and so on (actual file is much larger then the sample I showed here).

There's no guarantee the breeds won't be altered, and no guarantee the "sizes" will remain the same / always be included ("Small" could be left out, for example).

EDIT:: After Joe Ferndz's comment, I've updated my code and have something slightly closer to working, but the actual adding-the-columns is giving me trouble still.

dogData = pd.read_csv("dogdata.csv", header=[0,1])
dogData.columns = dogData.columns.map("_".join)

totalVal = ""
count = 0

for col in dogData:
    if "Unnamed" in col:
        continue  # to skip starting columns
    if "Average" not in col:
        totalVal += dogData[col]
        count += 1
    else:
        # this is where I'd calculate average, then reset count and totalVal
        # right now, because the addition isn't working, I'm haven't figured that out
        break

print(totalVal)

Now, this code is getting the correct values technically... but it won't let me numerically add them (hence why totalVal is a string right now). It gives me a string of concatenated numbers, the correct concatenated numbers, but won't let me convert them to floats to actually add.

I've tried doing float(dogData[col]) for the totalVal addition line - it gives me a TypeError: cannot convert the series to <class float>

I've tried keeping it as a string, putting in "," between the numbers, then doing totalVal.split(",") to separate them, then convert and add... but obviously that doesn't work either, because AttributeError: 'Series' has no attribute 'split'

These errors make sense to me and I understand why it's happening, but I don't know what the correct method for doing this is. dogData[col] gives me all the values for every row at once, which is what I want, but I don't know how to then store that and add it in the next iteration of the loop.

Here's a copy/pastable sample of data:

,Corgi,Yorkie,Pug,Average,Average,Dalmation,German Shepherd,Average,Great Dane,Average  
,Small,Small,Small,Small,Medium,Large,Large,Large,Very Large,Very Large  
Words,1,3,3,3,2.4,3,5,7,7,7  
Words1,2,2,4,4,2.2,4,4,6,8,8  
Words2,2,1,5,3,2.5,5,3,8,9,6  
Words3,1,4,4,2,2.7,6,6,5,6,9  

Answer 1

You have to do a few tricks to get this to work. Step 1: You need to read the csv file and use first two rows as header. It will create a MultiIndex column list.

Step 2: You need to join them together with say an _.

Step 3: Then rename the specific columns as per your requirement like S-Average, M-Average, ....

Step 4: find out how many columns have dog name + small

Step 5: Compute value for Small. Per your req, sum (columns with Small) / count (columns with Small)

Step 6,7: do same for Large

Step 8,9: do same for Very Large

This will give you the final list. If you want the columns to be in specific order, then you can change the order.

Step 10: Change the order for the dataframe

import pandas as pd
df = pd.read_csv('abc.txt',header=[0,1], index_col=0)
df.columns = df.columns.map('_'.join)
df.rename(columns={'Average_Small': 'S-Average',
                   'Average_Medium': 'M-Average',
                   'Average_Large': 'L-Average',
                   'Average_Very Large': 'Very L-Average'}, inplace = True)

idx = [i for i,x in enumerate(df.columns) if x.endswith('_Small')]
if idx:
    df['Small']= ((df.iloc[:, idx].sum(axis=1))/len(idx)).round(2)
    df.drop(df.columns[idx], axis = 1, inplace = True)

idx = [i for i,x in enumerate(df.columns) if x.endswith('_Large')]
if idx:
    df['Large']= ((df.iloc[:, idx].sum(axis=1))/len(idx)).round(2)
    df.drop(df.columns[idx], axis = 1, inplace = True)

idx = [i for i,x in enumerate(df.columns) if x.endswith('_Very Large')]
if idx:
    df['Very_Large']= ((df.iloc[:, idx].sum(axis=1))/len(idx)).round(2)
    df.drop(df.columns[idx], axis = 1, inplace = True)

df = df[['Small', 'S-Average', 'M-Average', 'L-Average', 'Very L-Average', 'Large', 'Very_Large', ]]

print (df)

The output of this will be:

        Small  S-Average  M-Average  ...  Very L-Average  Large  Very_Large
Words    2.33          3        2.4  ...               7    4.0         7.0
Words1   2.67          4        2.2  ...               8    4.0         8.0
Words2   2.67          3        2.5  ...               6    4.0         9.0
Words3   3.00          2        2.7  ...               9    6.0         6.0