csv文件中多列的python pandas parse_dates

Question

I'm working on Python pandas for two csv files comparison but in csv files having 5 date columns i've used parse_date=['dateofbirth','lastupdates','dateofjoin','dateofresign,'endoftrade'] in read_csv method but it is only parsing dateofbirth not all the columns in csv file.

code:

csv_pandas=pd.read_csv("path of the csv file",parse_date=['dateofbirth','lastupdates','dateofjoin','dateofresign,'endoftrade'])
print(csv_pandas)

CSV File:

dateofbirth             lastupdates       dateofjoin          dateofresign 
05/06/2021 00:00:00PM 12/13/2021 12:00:00PM 12/13/2021 12:00:00PM 12/13/2021 12:00:00PM

column        non-null count   Dtype
------        -------------    ------
dateofbirth    non-null         object
dateofbirth    non-null         datetime64[ns]
dateofbirth    non-null         datetime64[ns]
dateofbirth    non-null         datetime64[ns]

I can able to convert only object Dtype column,remaining datetime64[ns] not parsing

Around i've 160 csv files ,each csv file have different column names ,Can any one plz suggest

Answer 1

• you have two date formats that require different strptime() format instructions.
• not all of the columns you are trying to convert exist in data frame, so testing that column exists in dict comprehension that is pass as ** kwargs to assign()

csv_pandas = csv_pandas.assign(
    **{
        c: pd.to_datetime(csv_pandas[c], format="%Y-%m-%d %H:%M:%S:%f", errors="ignore")
        for c in parse_date
        if c in csv_pandas.select_dtypes("object").columns
    }
).pipe(
    lambda d: d.assign(
        **{
            c: pd.to_datetime(d[c], format="%m/%d/%Y %H:%M:%S%p", errors="ignore")
            for c in parse_date
            if c in d.select_dtypes("object").columns
        }
    )
)

output

csv_pandas.dtypes
dateofbirth     datetime64[ns]
lastupdates     datetime64[ns]
dateofjoin      datetime64[ns]
dateofresign    datetime64[ns]
dtype: object