查找包含最大数据的重复行

Question

我有一个这样的csv文件：

Date of event       Name        Date of birth
06.01.1986          John Smit   23.08.1996
18.12.1996          Barbara D   01.08.1965
12.12.2001          Barbara D   01.08.1965
17.10.1994          John Snow   20.07.1965

我必须按“姓名”和“出生日期”（可能与其他列一起）查找唯一的行，但必须按最大日期查找。

因此，我必须获得如下csv文件：

Date of event       Name        Date of birth
06.01.1986          John Smit   23.08.1996
12.12.2001          Barbara D   01.08.1965
17.10.1994          John Snow   20.07.1965

怎么做？我没有任何想法。。

Answer 1

import pandas as pd

# read the csv in with pandas module

df = pd.read_csv('pathToCsv.csv', header=0, parse_dates=[0, 2])

# set the column names as more programming friendly  i.e. no whitespace

df.columns = ['dateOfEvent','name','DOB'] # and probably some other columns ..

# keep row only with max (Date of event) per group ( name, Date of Birth )

yourwish = =df.groupby(['Name','DOB'])['dateOfEvent'].max()

Answer 2

因为列名有空格，所以最好用逗号分隔。

您可以使用pandas库执行以下操作：

import tempfile
import pandas

# create a temporary csv file with your data (comma delimited)
temp_file_name = None
with tempfile.NamedTemporaryFile('w', delete=False) as f:
    f.write("""Date of event,Name,Date of birth
06.01.1986,John Smit,23.08.1996
18.12.1996,Barbara D,01.08.1965
12.12.2001,Barbara D,01.08.1965
17.10.1994,John Snow,20.07.1965""")
    temp_file_name = f.name

# read the csv data using the pandas library, specify columns with dates
data_frame = pandas.read_csv(
    temp_file_name,
    parse_dates=[0,2],
    dayfirst=True,
    delimiter=','
)

# use groupby and max to do the magic
unique_rows = data_frame.groupby(['Name','Date of birth']).max()

# write the results
result_csv_file_name = None
with tempfile.NamedTemporaryFile('w', delete=False) as f:
    result_csv_file_name = f.name
    unique_rows.to_csv(f)

# read and show the results
with open(result_csv_file_name, 'r') as f:
    print(f.read())

这导致：

Name,Date of birth,Date of event
Barbara D,1965-08-01,2001-12-12
John Smit,1996-08-23,1986-01-06
John Snow,1965-07-20,1994-10-17