Adding values to existing columns in pandas

Question

I loop into csv files in a directory and read them with pandas. For each csv files I have a category and a marketplace. Then I need to get the id of the category and the id of the marketplace from the database which will be valid for this csv file.

the finalDf is a dataframe containing all the products for all the csv files and I need to append it with data fron the current csv.

The list of the products of the current CSV are retrived using:

df['PRODUCT']

I need to append them to the finalDf and I used:

finalDf['PRODUCT'] =  finalDf['PRODUCT'].append(df['PRODUCT'],ignore_index=True)

This seems to work fine, and I now have to insert catid and marketid to the corresponding columns of the finalDf. because catid and marketid are consitent accross the current csv file I just need to add them as much time as there are rows in the df dataframe, this is what I'm trying to accomplish in the code below.

finalDf = pd.DataFrame(columns=['PRODUCT', 'CAT_ID', 'MARKET_ID'])
finalDf['PRODUCT'] = finalDf.PRODUCT.astype('category')

df = pd.read_csv(filename, header=None,
                             names=['PRODUCT', 'URL_PRODUCT', 'RANK', 'URL_IMAGE', 'STARS', 'PRICE', 'NAME', 'SNAPDATE',
                                    'CATEGORY', 'MARKETPLACE', 'PARENTCAT', 'LISTTYPE', 'VERSION', 'LEVEL'], sep='\t')

finalDf['PRODUCT'] = finalDf['PRODUCT'].append(df['PRODUCT'],ignore_index=True)
# Here I have a single value to add n times, n corresponding to the number of rows in the dataframe df
catid = 2113
marketid = 13
catids = pd.Series([catid]*len(df.index))
marketids = pd.Series([marketid]*len(df.index))
finalDf['CAT_ID'] = finalDf['CAT_ID'].append(catids, ignore_index=True)
finalDf['MARKET_ID'] = finalDf['MARKET_ID'].append(marketids, ignore_index=True)

print finalDf.head()

        PRODUCT  CAT_ID  MARKET_ID
    0    ABC       NaN    NaN
    1    ABB       NaN    NaN
    2    ABE       NaN    NaN
    3    DCB       NaN    NaN
    4    EFT       NaN    NaN

As you can see, I just have NaN values instead of the actual values. expected output:

        PRODUCT  CAT_ID  MARKET_ID
    0    ABC       2113    13
    1    ABB       2113    13
    2    ABE       2113    13
    3    DCB       2113    13
    4    EFT       2113    13

finalDF containing several csv would look like:

        PRODUCT  CAT_ID  MARKET_ID
    0    ABC       2113    13
    1    ABB       2113    13
    2    ABE       2113    13
    3    DCB       2113    13
    4    EFT       2113    13
    5    SDD       2114    13
    6    ERT       2114    13
    7    GHJ       2114    13
    8    MOD       2114    13
    9    GTR       2114    13
   10    WLY       2114    13
   11    WLO       2115    13
   12    KOP       2115    13

Any idea?

Thanks

Answer 1

I finally found the solution, don't know why the other one didn't work though. But this one is simpler:

tempDf = pd.DataFrame(columns=['PRODUCT','CAT_ID','MARKET_ID'])
tempDf['PRODUCT'] = df['PRODUCT']
tempDf['CAT_ID'] = catid
tempDf['MARKET_ID'] = 13

finalDf = pd.concat([finalDf,tempDf])