Pandas Datetime

The Pandas can provide the features to work with time-series data for all domains. It also consolidates a large number of features from other Python libraries like scikits.timeseries by using the NumPy datetime64 and timedelta64 dtypes. It provides new functionalities for manipulating the time series data. The manipulations performed in Pandas Datetime can be listed as:

DatetimeIndex
Range Generation of Dates
Localization and Conversions
Time zone Handling
Time Series Operations

The time series tools are most useful for data science applications and deals with other packages used in Python.

Example 1: DatetimeIndex

The below code generates a sequence of eight dates starting from '5/4/2013' with a frequency of one second.

Code

# Python Program for Pandas datetime Library
import pandas as pd     
# Create the dates with frequency     
info = pd.date_range('5/4/2013', periods = 8, freq ='S')     
# Display the generated sequence of dates  
info                                   

Output:

DatetimeIndex(['2013-05-04 00:00:00', '2013-05-04 00:00:01',
               '2013-05-04 00:00:02', '2013-05-04 00:00:03',
               '2013-05-04 00:00:04', '2013-05-04 00:00:05',
               '2013-05-04 00:00:06', '2013-05-04 00:00:07'],
              dtype='datetime64[ns]', freq='S')

Example 2: Conversion

The below code converts a DataFrame with separate columns for year, month, and day into a single datetime format using the pd.to_datetime() function in Pandas.

Code

# Python Program for Pandas datetime Library
import pandas as pd
# Create a DataFrame with 'year', 'month', and 'day' columns
info = pd.DataFrame({'year': [2014, 2012],  
'month': [5, 7],  
'day': [20, 17]})  
# Convert the DataFrame columns to datetime format
pd.to_datetime(info)                           

Output:

0   2014-05-20
1   2012-07-17
dtype: datetime64[ns]

Example 3: Range Generation

This below code generates a sequence of five dates starting from '2017-06-04' with a frequency of one second.

Code

Example3:

# Python Program for Pandas datetime Library
import pandas as pd  
start_date = '2017-06-04'
num_periods = 5
frequency = 'S'
dates = pd.date_range(start=start_date, periods=num_periods, freq=frequency)
# Display the generated sequence of dates  
dates                          

Output:

DatetimeIndex(['2017-06-04 00:00:00', 
               '2017-06-04 00:00:01',
               '2017-06-04 00:00:02',
               '2017-06-04 00:00:03',
               '2017-06-04 00:00:04'],
               dtype='datetime64[ns]', freq='S')

Example 4: Localization

The below python datetime code localizes the timezone of a datetime sequence represented by the variable 'dmy' to UTC using the tz_localize() function in Pandas.

Code

# Python Program for Pandas datetime Library
import pandas as pd  
# Localize the timezone of the 'dmy' datetime sequence to UTC
info = dmy.tz_localize('UTC')   
# Display the updated datetime sequence
info            

Output:

DatetimeIndex(['2017-06-04 00:00:00+00:00', '2017-06-04 00:00:01+00:00',
               '2017-06-04 00:00:02+00:00', 
               '2017-06-04 00:00:03+00:00',
               '2017-06-04 00:00:04+00:00'],
              dtype='datetime64[ns, UTC]', freq='S')

Example 5: Time Series Operations

The below code is written to perform time series operations like rolling mean.

Code

# Python Program for Pandas datetime Library
import pandas as pd  
# Create a sample DataFrame with datetime index
data = {'Value': [10, 15, 8, 12, 9]}
index = pd.date_range('2022-01-01', periods=5, freq='D')
df = pd.DataFrame(data, index=index)

# Perform rolling window calculation on the 'Value' column
rolling_mean = df['Value'].rolling(window=3).mean()

# Print the DataFrame and rolling mean values
print("DataFrame:")
print(df)
print("Rolling Mean:")
print(rolling_mean) 

Output:

DataFrame:
            Value
2022-01-01     10
2022-01-02     15
2022-01-03      8
2022-01-04     12
2022-01-05      9

Rolling Mean:
2022-01-01          NaN
2022-01-02          NaN
2022-01-03     11.000000
2022-01-04     11.666667
2022-01-05      9.666667
Freq: D, Name: Value, dtype: float64

Conclusion:

The Pandas library gives strong usefulness to working with datetime information in Python. It offers different elements to deal with, control, and break down dates and times productively.

Next TopicPandas Time Offset

← prev next →