熊猫数据框输出格式

Question

I'm importing a trade list and trying to consolidate it into a position file with summed quantities and average prices. I'm grouping based on (ticker, type, expiration and strike). Two questions:

1. Output has the index group (ticker, type, expiration and strike) in the first column. How can I change this so that each index column outputs to its own column so the output csv is formatted the same way as the input data?
2. I currently force the stock trades to have values ("1") because leaving the cells blank will cause an error, but this adds bad data since "1" is not meaningful. Is there a way to preserve "" without causing a problem?

Dataframe:

    GM      stock   1           1       32      100
    AAPL    call    201612      120     3.5     1000
    AAPL    call    201612      120     3.25    1000
    AAPL    call    201611      120     2.5     2000
    AAPL    put     201612      115     2.5     500
    AAPL    stock   1            1      117     100

Code:

    import pandas as pd
    import numpy as np

    df = pd.read_csv(input_file, index_col=['ticker', 'type', 'expiration', 'strike'], names=['ticker', 'type', 'expiration', 'strike', 'price', 'quantity'])
    df_output = df.groupy(df.index).agg({'price':np.mean, 'quantity':np.sum})
    df_output.to_csv(output_file, sep=',')

csv output comes out in this format:

(ticker, type, expiration, strike), price, quantity

desired format:

ticker, type, expiration, strike, price, quantity

Answer 1

For the first question, you should use groupby(df.index_col) instead of groupby(df.index)

For the second, I am not sure why you couldn't preserve "", is that numeric?

I mock some data like below:

import pandas as pd                                                                                                 
import numpy as np                                                                                                  

d = [                                                                                                               
    {'ticker':'A', 'type':'M', 'strike':'','price':32},                                                             
    {'ticker':'B', 'type':'F', 'strike':100,'price':3.5},                                                           
    {'ticker':'C', 'type':'F', 'strike':'', 'price':2.5}                                                            

]                                                                                                                   
df = pd.DataFrame(d)                                                                                                
print df                                                                                                            

#dgroup = df.groupby(['ticker', 'type']).agg({'price':np.mean})                                                     
df.index_col = ['ticker', 'type', 'strike']                                                                         
dgroup = df.groupby(df.index_col).agg({'price':np.mean})   
#dgroup = df.groupby(df.index).agg({'price':np.mean})                                                 
print dgroup                                                                                                        
print type(dgroup)                                                                                                  
dgroup.to_csv('check.csv') 

output in check.csv:

ticker,type,strike,price                                                                                            
A,M,,32.0                                                                                                           
B,F,100,3.5                                                                                                         
C,F,,2.5