Convert a series of date strings to a time series in Pandas Dataframe

During the analysis of a dataset, oftentimes it happens that the dates are not represented in proper type and are rather present as simple strings which makes it difficult to process them and perform standard date-time operations on them. 

pandas.to_datetime() Function helps in converting a date string to a python date object. So, it can be utilized for converting a series of date strings to a time series.

Let’s see some examples:
Example 1: 

Python3

# import pandas library
import pandas as pd

# create a series of date strings
dt_series = pd.Series(['28 July 2020', '16 January 2013',
                       '29 February 2016 18:14'])

# display the series initially
print("Series of date strings:")
print(dt_series)

# display the series after being
# converted to a time series
print("\nSeries of date strings after" +
       " being converted to a timeseries:")

print(pd.to_datetime(dt_series))

Output:

Example 2: 

Python3

# import pandas library
import pandas as pd

# create a series of date strings
dt_series = pd.Series(['2020/07/28', '2013/01/16',
                       '2016/02/29 18:14'])

# display the series initially
print("Series of date strings:")
print(dt_series)

# display the series after being
# converted to a time series
print("\nSeries of date strings after " +
      "being converted to a timeseries:")

print(pd.to_datetime(dt_series))

Output:

Example 3: 

Python3

# import pandas library
import pandas as pd

# create a series of date strings
dt_series = pd.Series(['2020-07-28', '2013-01-16',
                       '2016-02-29 18:14'])

# display the series initially
print("Series of date strings:")
print(dt_series)

# display the series after
# being converted to a time series
print("\nSeries of date strings after " +
      "being converted to a timeseries:")

print(pd.to_datetime(dt_series))

Output:

Example 4: 

Python3

# import pandas library
import pandas as pd

# create a series of date strings
dt_series = pd.Series(['28/07/2020', '01/16/2013',
                       '29/02/2016 18:14'])

# display the series initially
print("Series of date strings:")
print(dt_series)

# display the series after being
# converted to a time series
print("\nSeries of date strings after " +
      "being converted to a timeseries:")

print(pd.to_datetime(dt_series))

Output:

Example 5: 

Python3

# import pandas library
import pandas as pd

# create a series of date strings
dt_series = pd.Series(['20200728', '20130116',
                       '20160229 181431'])

# display the series initially
print("Series of date strings:")
print(dt_series)

# display the series after
# being converted to a time series
print("\nSeries of date strings after " +
      "being converted to a timeseries:")

print(pd.to_datetime(dt_series))

Output:

Example 6: 

Python3

# import pandas library
import pandas as pd

# create a series of date strings
dt_series = pd.Series(['28 July 2020', '2013-01-16',
                       '20160229 18:14', '5/03/2019 2215',
                       '20151204 09:23'])

# display the series initially
print("Series of date strings:")
print(dt_series)

# display the series after
# being converted to a time series
print("\nSeries of date strings after " +
      "being converted to a timeseries:")

print(pd.to_datetime(dt_series))

Output: