Convert Integers to Datetime in Pandas DataFrame

To convert integers to datetime in Pandas DataFrame:

df["DataFrame Column"] = pd.to_datetime(df["DataFrame Column"], format=specify format)

Note that the integers must match the format specified.

The Examples

Example 1: Date format of “YYYYMMDD“

To start with a simple example, create a DataFrame with a column called “dates” that contains integers (with a format of YYYYMMDD):

import pandas as pd

data = {"dates": [20240305, 20240316, 20240328], 
        "status": ["open", "open", "close"]
        }

df = pd.DataFrame(data)

print(df)
print(df.dtypes)

As you can see, the data type is integer for the values under the “dates” column:

      dates status
0  20240305   open
1  20240316   open
2  20240328  close
dates      int64
status    object
dtype: object

The date format of YYYYMMDD can be represented as follows (refer to the following source for the different formats):

format='%Y%m%d'

Therefore, the complete code to convert the integers to datetime would be:

import pandas as pd

data = {"dates": [20240305, 20240316, 20240328], 
        "status": ["open", "open", "close"]
        }

df = pd.DataFrame(data)

df["dates"] = pd.to_datetime(df["dates"], format="%Y%m%d")

print(df)
print(df.dtypes)

Run the code in Python, and you’ll see that the data type for the ‘dates‘ column is now datetime:

       dates status
0 2024-03-05   open
1 2024-03-16   open
2 2024-03-28  close
dates     datetime64[ns]
status            object
dtype: object

Example 2: Date format of “YYMMDD“

Now assume that the dates are formatted as YYMMDD:

import pandas as pd

data = {"dates": [240305, 240316, 240328],
        "status": ["open", "open", "close"]
        }

df = pd.DataFrame(data)

print(df)
print(df.dtypes)

As you can see, the data type is integer as well:

    dates status
0  240305   open
1  240316   open
2  240328  close
dates      int64
status    object
dtype: object

The required date format to represent YYMMDD is therefore:

format="%y%m%d"

So the complete Python code is:

import pandas as pd

data = {"dates": [240305, 240316, 240328],
        "status": ["open", "open", "close"]
        }

df = pd.DataFrame(data)

df["dates"] = pd.to_datetime(df["dates"], format="%y%m%d")

print(df)
print(df.dtypes)

As before, the integers would be converted to datetime:

       dates status
0 2024-03-05   open
1 2024-03-16   open
2 2024-03-28  close
dates     datetime64[ns]
status            object
dtype: object

Example 3: Date format of “YYYYMMDDHHMMSS”

In that case, the format that you’ll need to specify is:

format="%Y%m%d%H%M%S"

So the full Python code would be:

import pandas as pd

data = {
    "dates": [20240305093000, 20240316093000, 20240328200000],
    "status": ["open", "open", "closed"],
}

df = pd.DataFrame(data)

df["dates"] = pd.to_datetime(df["dates"], format="%Y%m%d%H%M%S")

print(df)
print(df.dtypes)

You’ll now get the datetime format:

                dates  status
0 2024-03-05 09:30:00    open
1 2024-03-16 09:30:00    open
2 2024-03-28 20:00:00  closed
dates     datetime64[ns]
status            object
dtype: object

Check the following guide for the steps to convert strings to datetime in Pandas DataFrame.