从现有的 csv 文件 python pandas 创建多个 csv 文件

Question

I'm trying to take a large csv file and write a csv file for the sort of two columns. I was able to get the two individual unique values from the file to be able to know which csv files need to be created.
Ex Data:

1,224939.203,1243008.651,1326.774,F,C-GRAD-FILL,09/22/18 07:24:34,
1,225994.242,1243021.426,1301.772,BS,C-GRAD-FILL,09/24/18 08:24:18,
451,225530.332,1243016.186,1316.173,GRD,C-TOE,10/02/18 11:49:13,
452,225522.429,1242996.017,1319.168,GRD,C-TOE KEY,10/02/18 11:49:46,

I would like to create a csv file "C-GRAD-FILL 09-22-18.csv" with all of the data that matches the two values. I cannot decide how to iterate through the data for both values.

def readData(fileName):
    df = pd.read_csv(fileName,index_col=False, names+['Number','Northing','Easting','Elevation','Description','Layer','Date'],parse_dates=['Date'] )
    ##Layers here!!!
    layers = df['Layer'].unique()
    ##Dates here!!! AS DATETIME OBJECTS!!!!
    dates = df['Date'].map(lambda t: t.date()).unique()
    ##Sorted in order
    sortedList = df.sort_values(by=['Layer','Date'])

Answer 1

You can use a GroupBy object. First ensure your date is in the correct string format:

df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%m-%d-%y')

To output all files, iterate a GroupBy object:

for (layer, date), group in df.groupby(['Layer', 'Date']):
    group.to_csv(f'{layer} {date}.csv', index=False)

Or, for one specific combination:

layer = 'C-GRAD-FILL'
date = '09-22-18'
g = df.groupby(['Layer', 'Date'])

g.get_group((layer, date)).to_csv(f'{layer} {date}.csv', index=False)