Impossibile convertire l'ora della data in formato leggibile in python: python, datetime

Ho un intervallo di date come segue:

Date
2017-04-02 12:00 AM
2017-04-02 01:00 AM
2017-04-02 02:00 AM
2017-04-02 03:00 AM
2017-04-02 04:00 AM
2017-04-02 05:00 AM
2017-04-02 06:00 AM
2017-04-02 07:00 AM
2017-04-02 08:00 AM
2017-04-02 09:00 AM
2017-04-02 10:00 AM

Voglio convertirli tutti in python data time per tracciarlo in asse x.ho usato il seguente codice:

dates=fire["Date"]#copy date value in dataframe
x = dt.datetime.strptime("dates","%Y-%m-%d %I:%M %p").date()

Ma mi dà errore come:

time data "d" does not match format "%Y-%m-%d %I:%M %p"

Come posso convertire tutte queste date in date leggibili con Python?

risposte:

3 per risposta № 1

d è una variabile, quindi è necessario rimuovere le virgolette:

x = [dt.datetime.strptime(d,"%Y-%m-%d %I:%M %p").date() for d in dates]

Modifica: esempio riproducibile

Seguendo la richiesta OP, ecco un esempio riproducibile:

import pandas as pd
import datetime as dt
from StringIO import StringIO

data="""
Date
2017-04-02 12:00 AM
2017-04-02 01:00 AM
2017-04-02 02:00 AM
2017-04-02 03:00 AM
2017-04-02 04:00 AM
2017-04-02 05:00 AM
2017-04-02 06:00 AM
2017-04-02 07:00 AM
2017-04-02 08:00 AM
2017-04-02 09:00 AM
2017-04-02 10:00 AM
"""
# StringIO is used to simulate reading from a csv file
df = pd.read_csv(StringIO(data))
print(df.head())

dates = df["Date"]
x = [dt.datetime.strptime(d,"%Y-%m-%d %I:%M %p").date() for d in dates]
print(x)

L'output:

[datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2), datetime.date(2017, 4, 2)]

Una nota a margine, i panda possono analizzare le date per te come segue:

df2 = pd.read_csv(StringIO(data), parse_dates=["Date"])

L'output:

                 Date
0 2017-04-02 00:00:00
1 2017-04-02 01:00:00
2 2017-04-02 02:00:00
3 2017-04-02 03:00:00
4 2017-04-02 04:00:00

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