Automatically Extracting the Datetime Format from a Pandas Series [duplicate] - python-3.x

Question

I am trying to format the column 'Data' to make a pattern with dates.
The formats I have are:
1/30/20 16:00
1/31/2020 23:59
2020-02-02T23:43:02
Here is the code for the dataframe.
import requests
import pandas as pd
import numpy as np
url = "https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports"
csv_only = [i.split("=")[1][1:-1] for i in requests.get(url).text.split(" ") if '.csv' in i and 'title' in i]
combo = [pd.read_csv(url.replace("github","raw.githubusercontent").replace("/tree/","/")+"/"+f) for f in csv_only]
one_df = pd.concat(combo,ignore_index=True)
one_df["País"] = one_df["Country/Region"].fillna(one_df["Country_Region"])
one_df["Data"] = one_df["Last Update"].fillna(one_df["Last_Update"])
I tried adding the code bellow but it doesn't bring the result I wanted
pd.to_datetime(one_df['Data'])
one_df.style.format({"Data": lambda t: t.strftime("%m/%d/%Y")})
Any help?
UPDATE
This is the complete code, but it doesn't work. Many exceptions printed with different date formats.
import requests
import pandas as pd
import numpy as np
from datetime import datetime
url = "https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports"
csv_only = [i.split("=")[1][1:-1] for i in requests.get(url).text.split(" ") if '.csv' in i and 'title' in i]
combo = [pd.read_csv(url.replace("github","raw.githubusercontent").replace("/tree/","/")+"/"+f) for f in csv_only]
one_df = pd.concat(combo,ignore_index=True)
df = pd.DataFrame()
DATE_FORMATS = ["%m/%d/%y %H:%M", "%m/%d/%Y %H:%M", "%Y-%m-%dT%H:%M:%S", "%Y-%m-%d %H:%M:%S", "%Y-%m-%d %H:%M:%S", "%Y-%m-%d %H:%M:%S"]
df["Região"] = one_df["Province/State"].fillna(one_df["Admin2"])
df["País"] = one_df["Country/Region"].fillna(one_df["Country_Region"])
df["Data"] = one_df["Last Update"].fillna(one_df["Last_Update"])
df["Confirmados"] = one_df["Confirmed"]
df["Mortes"] = one_df["Deaths"]
df["Recuperados"] = one_df["Recovered"]
def parse(x_):
for fmt in DATE_FORMATS :
try:
tmp = datetime.strptime(x_, fmt).strftime("%m/%d/%Y")
return tmp
except ValueError:
print(x_)
pd.to_datetime(df['Data'])
df['Data'] = df['Data'].apply(lambda x: parse(x))
#df['Data'].strftime('%m/%d/%Y')
#df['Data'] = df['Data'].map(lambda x: x.strftime('%m/%d/%Y') if x else '')
df.to_excel(r'C:\Users\guilh\Downloads\Covid2\Covid-19.xlsx', index=False, encoding="utf8")
print(df)

Answer 1

from datetime import datetime
import pandas as pd
You could save all possible formats in a list as -
DATE_FORMATS = ["%Y-%m-%d %H:%M:%S", "%Y-%m-%dT%H:%M:%S", "%m/%d/%y %H:%M", "%m/%d/%Y %H:%M"]
Define a function that loops through the formats and tries to parse it.
(Fixed a bug, where the print statement should have been outside the for loop)
issues = set()
def parse(x_):
for fmt in DATE_FORMATS:
try:
return datetime.strptime(x_, fmt).strftime("%m/%d/%Y")
except ValueError:
pass
issues.add(x_)
sample = ["1/30/20 16:00", "1/31/2020 23:59", "2020-02-02T23:43:02"]
df = pd.DataFrame({'data': sample})
df['data'] = df['data'].apply(lambda x: parse(x))
assert df['Data'].isna().sum() == len(issues) == 0, "Issues observed, nulls observed in dataframe"
print("Done")
Output
data
0 01/30/2020
1 01/31/2020
2 02/02/2020
If df.apply() comes across a particular date format that hasn't been defined in the list, it would simply print None since nothing would be returned by the function parse()

Answer 2

also here, letting pd.to_datetime infer the format does the trick:
import pandas as pd
s = pd.to_datetime(["1/30/20 16:00", "1/31/2020 23:59", "2020-02-02T23:43:02"])
print(s)
# DatetimeIndex(['2020-01-30 16:00:00', '2020-01-31 23:59:00',
# '2020-02-02 23:43:02'],
# dtype='datetime64[ns]', freq=None)
Note that if your date/time format generally provides the day first (e.g. 30.1.2021 for Jan 30th 2021), set keyword dayfirst=True.