Python + Pandas：为csv文件中的特定行添加值

Question

I have a csvfile which looks like this

Date,A,B,C,...,X
2016/04,ColA,ColB,ColC,...,ColX
2016/05,ColA,ColB,ColC,...,ColX
2016/06,ColA,ColB,ColC,...,ColX
2016/07,ColA,ColB,ColC,...,ColX
2016/08,ColA,ColB,ColC,...,ColX
2016/09,ColA,ColB,ColC,...,ColX
2016/10,ColA,ColB,ColC,...,ColX
2016/11,ColA,ColB,ColC,...,ColX
2016/12,ColA,ColB,ColC,...,ColX

and a function which creates lists like this:

['2016/08', 67287800000.00001, '2016/09', -22714300000.0, '2016/10', 97335100000.0, '2016/11', 97579300000.0, '2016/12', 97723900000.0]

These lists get passed to following function

def write_graph_data(cat, data_list, column):
    if len(data_list) > 0:
        row_num = 0

        for row in pd.read_csv('./data/%s_f.csv' % cat, sep=',', error_bad_lines=False, chunksize=1, header=0):
            date = row.at[row_num,'Date']

            if date == data_list[0]:
                row[column] = data_list[1]
                data_list.pop(0)
                data_list.pop(0)

            row_num += 1

This function reads the csv file row-wise and for every row checks the value in the 'Date' column. If the value is the same as the first list entry, the second list entry will be added to the row in a new column like this

2016/08,ColA,ColB,ColC,...,ColX,67287800000.00001

How can i save my csvfile with the changed row(s)?

Answer 1

you could try concat in pandas

lst = ['2016/08', 67287800000.00001, '2016/09', -22714300000.0, '2016/10', 97335100000.0, '2016/11', 97579300000.0, '2016/12', 97723900000.0]
df1 = pd.read_csv('./data/%s_f.csv' % cat, sep=',', error_bad_lines=False, chunksize=1, header=0)
df2 = pd.DataFrame(zip(lst[::2], lst[1::2]),columns=['Date','val'])

new_df = pd.concat([df1.set_index('Date'),df2.set_index('Date')], axis =1)

new_df.to_csv('new_file.csv')

output:

        A     B     C  ...     X           val
2016/04  ColA  ColB  ColC  ...  ColX           NaN
2016/05  ColA  ColB  ColC  ...  ColX           NaN
2016/06  ColA  ColB  ColC  ...  ColX           NaN
2016/07  ColA  ColB  ColC  ...  ColX           NaN
2016/08  ColA  ColB  ColC  ...  ColX  6.728780e+10
2016/09  ColA  ColB  ColC  ...  ColX -2.271430e+10
2016/10  ColA  ColB  ColC  ...  ColX  9.733510e+10
2016/11  ColA  ColB  ColC  ...  ColX  9.757930e+10
2016/12  ColA  ColB  ColC  ...  ColX  9.772390e+10