Pandas column to datetime

Pandas column to datetime DEFAULT

Convert the column type from string to datetime format in Pandas dataframe

While working with data in Pandas, it is not an unusual thing to encounter time series data, and we know Pandas is a very useful tool for working with time-series data in python.
Let&#;s see how we can convert a dataframe column of strings (in dd/mm/yyyy format) to datetime format. We cannot perform any time series based operation on the dates if they are not in the right format. In order to be able to work with it, we are required to convert the dates into the datetime format.

Code #1 : Convert Pandas dataframe column type from string to datetime format using pd.to_datetime() function.

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Output: 




As we can see in the output, the data type of the &#;Date&#; column is object i.e. string. Now we will convert it to datetime format using pd.to_datetime() function. 

Python3

Output: 

As we can see in the output, the format of the &#;Date&#; column has been changed to the datetime format. 
  
Code #2: Convert Pandas dataframe column type from string to datetime format using DataFrame.astype() function.

Python3

Output : 



As we can see in the output, the data type of the &#;Date&#; column is object i.e. string. Now we will convert it to datetime format using DataFrame.astype() function. 

Python3

Output : 

As we can see in the output, the format of the &#;Date&#; column has been changed to the datetime format.

Code #3: If the data frame column is in &#;yymmdd&#; format and we have to convert it to &#;yyyymmdd&#; format 

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In the above example, we change the data type of column &#;Dates&#; from &#;object&#; to &#;datetime64[ns]&#; and format from &#;yymmdd&#; to &#;yyyymmdd&#;.

Code #4: Converting multiple columns from string to &#;yyyymmdd&#; format using pandas.to_datetime()

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In the above example, we change the data type of columns &#;Treatment_start&#; and &#;Treatment_end&#; from &#;object&#; to &#;datetime64[ns]&#; type.




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Sours: https://www.geeksforgeeks.org/convert-the-column-type-from-string-to-datetime-format-in-pandas-dataframe/

You may use this template in order to convert strings to datetime in Pandas DataFrame:

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

Note that the strings must match the format specified. Later, you&#;ll see several scenarios for different formats.

Steps to Convert Strings to Datetime in Pandas DataFrame

Step 1: Collect the Data to be Converted

To begin, collect the data that you&#;d like to convert to datetime.

For example, here is a simple dataset about 3 different dates (with a format of yyyymmdd), when a store might be opened or closed:

DatesStatus
Opened
Opened
Closed

Step 2: Create a DataFrame

Next, create a DataFrame to capture the above data in Python. You can capture the dates as strings by placing quotes around the values under the &#;dates&#; column:

import pandas as pd values = {'dates': ['','',''], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) print (df) print (df.dtypes)

Run the code in Python, and you&#;ll get this DataFrame:

Notice that the &#;dates&#; were indeed stored as strings (represented by object).

Step 3: Convert the Strings to Datetime in the DataFrame

You may then use the template below in order to convert the strings to datetime in Pandas DataFrame:

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

Recall that for our example, the date format is yyyymmdd.

This date format can be represented as:

format='%Y%m%d'

Note that the strings data (yyyymmdd) must match the format specified (%Y%m%d). You may refer to the following source for the different formats that you may apply.

For our example, the complete Python code to convert the strings to datetime would be:

import pandas as pd values = {'dates': ['','',''], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) df['dates'] = pd.to_datetime(df['dates'], format='%Y%m%d') print (df) print (df.dtypes)

You&#;ll see that the data type for the &#;dates&#; column is now datetime:

Note that when applying pd.to_datetime, the default format is yyyymmdd. So in the above particular example, you could remove the format =&#;%Y%m%d&#; from the code. However, in other scenarios, as you&#;ll see below, you must specify the correct format to match with the strings data.

Converting Additional Formats

Let&#;s say that the dates are now formatted as ddmmyyyy:

DatesStatus
Opened
Opened
Closed

In that case, you&#;ll need to apply the format below (for reference, check the following table to identify the correct format that you should apply):

format='%d%m%Y'

Here is the complete Python code:

import pandas as pd values = {'dates': ['','',''], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) df['dates'] = pd.to_datetime(df['dates'], format='%d%m%Y') print (df) print (df.dtypes)

As before, your strings will now get converted to datetime:

What if your dates have a ddmmmyyyy format (e.g., 05Mar)?

You&#;ll then need to apply the format below (by changing the &#;m&#; to &#;b&#;):

format='%d%b%Y'

So your complete Python code would look like this:

import pandas as pd values = {'dates': ['05Mar','16Mar','28Mar'], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) df['dates'] = pd.to_datetime(df['dates'], format='%d%b%Y') print (df) print (df.dtypes)

You&#;ll now get the datetime format:

Let&#;s say that your dates now contain dashes (e.g., &#;Mar&#;) .

In that case, simply add those dashes as follows:

format='%d-%b-%Y'

Here is the full Python code:

import pandas as pd values = {'dates': ['Mar','Mar','Mar'], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) df['dates'] = pd.to_datetime(df['dates'], format='%d-%b-%Y') print (df) print (df.dtypes)

And the result:

Formats with Timestamps

Suppose that your strings contain both the dates and times:

DatesStatus
Opened
Opened
Closed

In that case, the format that should be specified is:

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

So the full Python code would be:

import pandas as pd values = {'dates': ['','',''], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) df['dates'] = pd.to_datetime(df['dates'], format='%Y%m%d%H%M%S') print (df) print (df.dtypes)

You&#;ll now see the datetime format:

Now let&#;s say that the strings contain characters, such as the dash character (&#;-&#;) to separate between the date and the time:

DatesStatus
Opened
Opened
Closed

In that scenario, the format should include the dash as well:

format='%Y%m%d-%H%M%S'

Here is the full Python code:

import pandas as pd values = {'dates': ['','',''], 'status': ['Opened','Opened','Closed'] } df = pd.DataFrame(values) df['dates'] = pd.to_datetime(df['dates'], format='%Y%m%d-%H%M%S') print (df) print (df.dtypes)

And the result:

Categories PythonSours: https://datatofish.com/strings-to-datetime-pandas/
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Pandas Convert Column to datetime – object/string, integer, CSV & Excel

In Pandas, you can convert a column (string/object or integer type) to datetime using the and methods. Furthermore, you can also specify the data type (e.g., datetime) when reading your data from an external source, such as CSV or Excel. 

In this Pandas tutorial, we are going to learn how to convert a column, containing dates in string format, to datetime. First, we are going to have a look at converting objects (i.e., strings) to datetime using the method. One neat thing when working with to_datetime() is that we can work with the format parameter. That is, we will also have a look at how to get the correct format when converting. After that, we will go on and carry out this conversion task with the method.

In the two last sections, we will import data from the disk. First, we will look at how to work with datetime when reading .csv files. Second, we will import data from an Excel file. Now, depending on how we want our dataframe, we can either parse the dates in our data files as indexes or specify the column(s). Furthermore, in both examples, we will work with the argument. 

Example Data

First, before going on to the two examples, we are going to create a Pandas dataframe from a dictionary. Here, we are going to create a dictionary:

Code language:Python(python)

Notice how we now have a dictionary with strings containing datetime (i.e., the values found if using the ‘Date’ key). Creating a dataframe is the next step, then. First, we import pandas and then we use the pd.DataFrame class with the dictionary as input:

Code language:Python(python)

In the image above, we can see that we have four columns and the last most contains the datetime strings that we want to convert. First, however, we can have a look at the data types of the dataframe. This can be done using the info() method:

Code language:CSS(css)

As evident in the output, the data types of the ‘Date’ column is object (i.e., a string) and the ‘Date2’ is integer. Note, you can convert a NumPy array to a Pandas dataframe, as well, if needed. In the next section, we will use the method to convert both these data types to datetime.

Pandas Convert Column with the to_datetime() Method

In this section, we are going to work with the to_datetime() to 1) convert strings, and 2) convert integers. Both to datetime, of course.

1 Convert an Object (string) Column:

Now, here’s how to convert a column to datetime:

Code language:Python(python)

In the code chunk above, we used our dataframe (i.e., df) and the brackets. Furthermore, within the brackets we put a string with the column that we wanted to convert. Note, if you want this to be a new column its just to change ‘Date’ to i.e. ‘Datetime’. That would add a new column to the dataframe. On the right side of the equal sign (“=”) we used the method. As we’re not working with any formatting we just use the column here, again, that we wanted to convert. Here’s the column converted to datetime:

2 Convert an Integer Column:

Here’s how to convert integer to datetime in the dataframe:

Code language:Python(python)

As we can see in the output above, the type of the ‘Date2’ column has been converted to datetime.

Note, if your dates are formatted differently and in these examples you can use the format parameter as well. For more information about the method check out the documentation. Now that you have changed the data type in the dataframe, you can, for example, use Pandas value_count() method to count occurrences in a column. In the next section, we will carry out the same conversion task but using the method. 

Pandas Convert Column with the astype() Method

In this section, we are going to use the method. Here’s how to convert a column, containing strings, to datetime with the method:

Code language:PHP(php)

Notice how we put datetime[ns] as the only argument. This will produce the following output, similar to what we have seen earlier.

As a final note, it is not possible to change the type from integer to datetime with the same code above. In the next two sections, we are going to convert to datetime when reading data from disk.

Convert Column to datetime when Reading a CSV File

Here’s how to change a column to datetime while using Pandas read_csv method:

Code language:Python(python)

As can be seen in the example code above, we used the parameter and set the column number in which our dates are stored. As usual, when working with Python the indexes start at 0. Noteworthy, here, is that we also used the parameter and set this to the first column (0) in the datafile. If we, on the other hand, would have had the dates in the first column (i.e., in the .csv file) we can set the dates as index:

Code language:Python(python)

Finally, if you want the date column to be index, this can be done after reading the .csv file as well. Here, you will just make the column index in the Pandas dataframe with the method.

Convert Column to datetime when Reading an Excel File

Here’s how to change a column to datetime when importing data using Pandas read_excel:

Code language:PHP(php)

As you can see, in the code chunk above, we used the same parameter as when reading a CSV file (i.e., parse_date). Note, here we set the date column, in the Excel file, as indexes. If you want this to be a column, change to .

Now that you have converted your dates to datetime object, you can start working with other date-related methods such as TimeDelta. For example, you can now calculate the difference between two dates. Furthermore, you can also plot your data in a time-series plot using e.g. Seaborn line plot.

As a final note, on both methods above, is that if you have many columns that need to be converted to datetime you can add each index of these columns in the list. Here’s some example code:

Conclusion

In this post, you have converted strings and integers (in dataframe columns) to datetime. First, you have learned how to use the method. Second, you learned how to use the method. Remember, it is not possible to change the data type from integer to datetime if you use the later method. Finally, you have also learned how to specify which columns that are of datetime type when reading a CSV and Excel file.

Sours: https://www.marsja.se/pandas-convert-column-to-datetime/
Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

Convert Pandas Column to Datetime

Created: December, | Updated: December,

  1. Pandas () Function to Convert DataFrame Column to Pandas Datetime
  2. Use the Method to Convert Pandas DataFrame Column to Datetime
  3. Use the Method to Convert Pandas Multiple Columns to Datetime
  4. Use Method to Convert Pandas DataFrame Column to Datetime

We will introduce methods to convert a Pandas column to . We use the same DataFrame below in the following examples.

Output:

Pandas () Function to Convert DataFrame Column to Pandas Datetime

Pandas function converts the given argument to .

The parameter in the Pandas function specifies the pattern of the string. It is the same with the in or in Python module.

Example of Converting Pandas Column to

Output:

Convert Pandas Column to Datetime_example

function doesn’t modify the in-place; therefore we need to assign the returned Pandas to the specific column.

Pandas () Function Is Smart to Convert to Datetime

function could do the conversion to in a smart way without being given the format string. It will find the string pattern automatically and smartly.

Warning

Although could do its job without given the smartly, the conversion speed is much lower than when the is given.

We could set the option of to be to switch the conversion to a faster mode if the format of the string could be inferred without giving the string.

It could increase the parsing speed by 5~6 times.

Options When the Input Argument Is Not a Valid String

has the parameter to specify the behavior if the given input is not a valid string to be parsed.

OptionBehaviour
An exception will be raised. Default option
is set
invalid parsing returns the input

It raises an exception when the option is or is omitted because is the default option.

Output:

The invalid item is set to be , and others are converted correctly.

If is set to be , when any of the column items is not valid, then the input column will be returned, even other items are valid string.

Output:

As shown above, the whole column is not converted or is ignored.

Use the Method to Convert Pandas DataFrame Column to Datetime

method of Pandas applies the function to each column or row.

We could use the function in the place of for simplicity.

Use the Method to Convert Pandas Multiple Columns to Datetime

If we need to convert Pandas DataFrame multiple columns to , we can still use the method as shown above.

Suppose we have two columns and that are strings.

pandas convert multiple columns to datetime_original dataframe

The function passed to the method is the function introduced in the first section.

Example code:

Output:

pandas convert multiple columns to datetime

Use Method to Convert Pandas DataFrame Column to Datetime

method of the Pandas converts the column to another data type. The data type of the in Pandas is ; therefore, shall be given as the parameter in the method to convert the DataFrame column to .

Output:

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  • Sours: https://www.delftstack.com/howto/python-pandas/how-to-convert-dataframe-column-to-datetime-in-pandas/

    To datetime column pandas

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    Pandas Datetime Tutorial - Working with Date and Time in Pandas

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