Convert Pandas Column to Datetime

Convert Pandas Column to DateTime

Use the to_datetime function, specifying a format to match your data.

raw_data['Mycol'] =  pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')

Convert DataFrame column type from string to datetime

The easiest way is to use to_datetime:

df['col'] = pd.to_datetime(df['col'])

It also offers a dayfirst argument for European times (but beware this isn't strict).

Here it is in action:

In [11]: pd.to_datetime(pd.Series(['05/23/2005']))
Out[11]:
0   2005-05-23 00:00:00
dtype: datetime64[ns]

You can pass a specific format:

In [12]: pd.to_datetime(pd.Series(['05/23/2005']), format="%m/%d/%Y")
Out[12]:
0   2005-05-23
dtype: datetime64[ns]

Convert Pandas column to datetime for a specific datetime format

You can use pd.to_datetime for this.

import pandas as pd
df = pd.DataFrame({"date": ["2011-06-12T01:17:56"]})

Conversion using map method:

df["date"].map(pd.to_datetime)

or

Conversion using apply method:

df["date"].apply(pd.to_datetime)

or

Conversion using function on column series:

df["date"] = pd.to_datetime(df["date"])

Converting object column in pandas dataframe to datetime

It means you have an extra space. Though pd.to_datetime is very good at parsing dates normally without any format specified, when you actually specify a format, it has to match EXACTLY.

You can likely solve your issue by adding .str.strip() to remove the extra whitespace before converting.

import pandas as pd
df['Time stamp'] = pd.to_datetime(df['Time stamp'].str.strip(), format='%d/%m/%Y')

Alternatively, you can take advantage of its ability to parse various formats of dates by using the dayfirst=True argument

df['Time stamp'] = pd.to_datetime(df['Time stamp'], dayfirst=True)

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Example:

import pandas as pd
df = pd.DataFrame({'Time stamp': ['01/02/1988', '01/02/1988 ']})

pd.to_datetime(df['Time stamp'], format= '%d/%m/%Y')

ValueError: unconverted data remains:

pd.to_datetime(df['Time stamp'].str.strip(), format='%d/%m/%Y')
#0   1988-02-01
#1   1988-02-01
#Name: Time stamp, dtype: datetime64[ns]

pd.to_datetime(df['Time stamp'], dayfirst=True)
#0   1988-02-01
#1   1988-02-01
#Name: Time stamp, dtype: datetime64[ns]

How to convert a pandas datetime column from UTC to EST

tz_localize and tz_convert work on the index of the DataFrame. So you can do the following:

1. convert the "time" to Timestamp format
2. set the "time" column as index and use the conversion functions
3. reset_index()
4. keep only the time

Try:

dataframe["time"] = pd.to_datetime(dataframe["time"],format="%H:%M:%S.%f")
output = (dataframe.set_index("time")
                   .tz_localize("utc")
                   .tz_convert("US/Eastern")
                   .reset_index()
          )
output["time"] = output["time"].dt.time

>>> output
              time val
0  15:13:12.349211   a
1  15:13:13.435233   b
2  15:13:14.345233   c

Convert dataframe column to datetime only if length of string is not zero

Two steps,

first lets create a series with your datetimes and coerce the bad values into NaTs

s = pd.to_datetime(data['etime'],errors='coerce',format='%Y%m%d%H%M%S')

second, lets find any values that aren't NaT and replace them with your target formatting.

data.loc[~s.isna(),'etime'] = s.dt.strftime('%Y-%m-%d %H:%M')

   day             etime
0   15  2020-08-11 23:52
1   17                  
2   20  2020-08-11 21:52
3   14    20200811265205
4   25  2020-08-11 23:52

• assuming 26 is a typo in your hour column at index 3.

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