如何使用 Pandas 在 CSV 文件中识别过去 10 天的数据行？

Question

I'm new to Python and currently seeking help with the following:

How can I identify data rows for the last 10 days in CVS file with Pandas? My first column (report_date) in CSV file has data values (yyyy-mm-dd) I have hundreds of records for each day, but I need to get only last 10 days from this file, based on the date in report_date column and ideally save output to a new CSV file.

My code so far:

import pandas as pd

data = pd.read_csv("path/to/my/file/myfile.csv")    

df = pd.DataFrame(report_date) 

days=10    
cutoff_date = df["report_date"].dt.date.iloc[-1] - pd.Timedelta(days=days)

Would someone be able to help? Thanks in advance!

Answer 1

Create DatetimeIndex first with index_col and parse_dates parameters in read_csv :

df = pd.read_csv("path/to/my/file/myfile.csv", 
                 index_col=['report_date'], 
                 parse_dates=['report_date'])   

And then is possible use DataFrame.last :

df1 = df.last('10d')

And last save to file by DataFrame.to_csv :

df1.to_csv('new.csv')

---

Your solution should be changed with convert column to datetimes in read_csv :

df = pd.read_csv("path/to/my/file/myfile.csv", parse_dates=['report_date'])    

days=10    
cutoff_date = df["report_date"].dt.date.iloc[-1] - pd.Timedelta(days=days)

Then compare dates by Series.dt.date in boolean indexing :

df1 = df[df["report_date"].dt.date > cutoff_date]

Last save to file with removed default index by DataFrame.to_csv :

df1.to_csv('new.csv', index=False)

EDIT: I believe you need:

df = pd.DataFrame({'data': range(30)}, index= pd.date_range('2020-01-25', periods=30))  
print (df)
            data
2020-01-25     0
2020-01-26     1
2020-01-27     2
2020-01-28     3
2020-01-29     4
2020-01-30     5
2020-01-31     6
2020-02-01     7
2020-02-02     8
2020-02-03     9
2020-02-04    10
2020-02-05    11
2020-02-06    12
2020-02-07    13
2020-02-08    14
2020-02-09    15
2020-02-10    16
2020-02-11    17
2020-02-12    18
2020-02-13    19
2020-02-14    20
2020-02-15    21
2020-02-16    22
2020-02-17    23
2020-02-18    24
2020-02-19    25
2020-02-20    26
2020-02-21    27
2020-02-22    28
2020-02-23    29

---

today = pd.Timestamp('today').floor('d')
df1 = df[df.index > today].first('10d')
print (df1)
            data
2020-02-11    17
2020-02-12    18
2020-02-13    19
2020-02-14    20
2020-02-15    21
2020-02-16    22
2020-02-17    23
2020-02-18    24
2020-02-19    25
2020-02-20    26