How to Format Datetime Column In Pandas?

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To format a datetime column in pandas, you can use the strftime method from the datetime module to specify the format you want. For example, you can convert a datetime column to a string with a specific format like this:

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df['datetime_column'] = pd.to_datetime(df['datetime_column']).dt.strftime('%Y-%m-%d %H:%M:%S')


In this example, '%Y-%m-%d %H:%M:%S' is the format string that specifies the year, month, day, hour, minute, and second in the desired order. You can customize this format string to display the datetime column in the format you prefer.

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How to filter datetime column in pandas based on specific conditions?

To filter a datetime column in pandas based on specific conditions, you can use the pd.to_datetime() function to convert the datetime column to a datetime object, and then use boolean indexing to filter the rows based on the conditions.


Here is an example on how to filter a datetime column named timestamp based on a specific condition:

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import pandas as pd

# Sample dataframe with datetime column
data = {'timestamp': ['2022-01-01 08:00:00', '2022-01-02 09:00:00', '2022-01-03 10:00:00']}
df = pd.DataFrame(data)

# Convert the timestamp column to datetime object
df['timestamp'] = pd.to_datetime(df['timestamp'])

# Filter rows based on specific conditions
filtered_df = df[(df['timestamp'] < pd.Timestamp('2022-01-02'))]

# Print the filtered dataframe
print(filtered_df)


In this example, the code filters the rows where the timestamp column is before '2022-01-02'.


You can adjust the condition inside the brackets to filter the datetime column based on your specific criteria.


What is the default format of datetime column in pandas?

The default format of datetime column in pandas is "YYYY-MM-DD HH:MM:SS" (Year-Month-Day Hour:Minute:Second).


How to add hours to datetime column in pandas?

To add hours to a DateTime column in Pandas, you can use the pd.to_timedelta function to create a TimeDelta object representing the number of hours you want to add, and then add it to the DateTime column.


Here's an example of how you can add 3 hours to a DateTime column named 'date_time':

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import pandas as pd

# Sample DataFrame with a DateTime column
df = pd.DataFrame({'date_time': ['2022-01-01 12:00:00', '2022-01-02 14:00:00']})

# Convert the 'date_time' column to datetime format
df['date_time'] = pd.to_datetime(df['date_time'])

# Add 3 hours to the 'date_time' column
df['date_time'] = df['date_time'] + pd.to_timedelta(3, unit='h')

print(df)


This code snippet will output the DataFrame with the 'date_time' column increased by 3 hours.


How to subtract two datetime columns in pandas?

You can subtract two datetime columns in pandas by using the pd.to_datetime() function to convert the columns to datetime objects and then subtracting them using the - operator. Here is an example code snippet:

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import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'start_date': ['2021-01-01', '2021-02-01', '2021-03-01'],
                   'end_date': ['2021-01-10', '2021-02-15', '2021-03-20']})

# Convert the datetime columns to datetime objects
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])

# Subtract the end date from the start date to get the difference in days
df['date_diff'] = df['end_date'] - df['start_date']

print(df)


This will output a DataFrame with a new column date_diff that contains the difference between the end_date and start_date columns in days.

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