Python | Working with date and time using Pandas

While working with data, encountering time series data is very usual. Pandas is a very useful tool while working with time series data.

Pandas provide a different set of tools using which we can perform all the necessary tasks on date-time data. Let’s try to understand with the examples discussed below.

Code #1: Create a dates dataframe

import pandas as pd 
  
# Create dates dataframe with frequency   
data = pd.date_range('1/1/2011', periods = 10, freq ='H') 
  
data 

Output:

Code #2: Create range of dates and show basic features

# Create date and time with dataframe 
data = pd.date_range('1/1/2011', periods = 10, freq ='H') 
  
x = datetime.now() 
x.month, x.year 

Output:

(9, 2018)

Datetime features can be divided into two categories.The first one time moments in a period and second the time passed since a particular period. These features can be very useful to understand the patterns in the data.

Divide a given date into features –

pandas.Series.dt.year returns the year of the date time.
pandas.Series.dt.month returns the month of the date time.
pandas.Series.dt.day returns the day of the date time.
pandas.Series.dt.hour returns the hour of the date time.
pandas.Series.dt.minute returns the minute of the date time.

Refer all datatime properties from here.

Code #3: Break data and time into separate features

# Create date and time with dataframe 
rng = pd.DataFrame() 
rng['date'] = pd.date_range('1/1/2011', periods = 72, freq ='H') 
  
# Print the dates in dd-mm-yy format 
rng[:5] 
  
# Create features for year, month, day, hour, and minute 
rng['year'] = rng['date'].dt.year 
rng['month'] = rng['date'].dt.month 
rng['day'] = rng['date'].dt.day 
rng['hour'] = rng['date'].dt.hour 
rng['minute'] = rng['date'].dt.minute 
  
# Print the dates divided into features 
rng.head(3) 

Output:

Code #4: To get the present time, use Timestamp.now() and then convert timestamp to datetime and directly access year, month or day.

# Input present datetime using Timestamp 
t = pandas.tslib.Timestamp.now() 
t 

Timestamp('2018-09-18 17:18:49.101496')

# Convert timestamp to datetime 
t.to_datetime() 

datetime.datetime(2018, 9, 18, 17, 18, 49, 101496)

# Directly access and print the features 
t.year 
t.month 
t.day 
t.hour 
t.minute 
t.second 

Let’s analyze this problem on a real dataset uforeports.

import pandas as pd 
  
url = 'http://bit.ly/uforeports'
  
# read csv file 
df = pd.read_csv(url)            
df.head() 

Output:

# Convert the Time column to datatime format 
df['Time'] = pd.to_datetime(df.Time) 
  
df.head() 

# shows the type of each column data 
df.dtypes 

City                       object
Colors Reported            object
Shape Reported             object
State                      object
Time               datetime64[ns]
dtype: object

# Get hour detail from time data 
df.Time.dt.hour.head() 

0    22
1    20
2    14
3    13
4    19
Name: Time, dtype: int64

# Get name of each date 
df.Time.dt.weekday_name.head() 

0     Sunday
1     Monday
2     Sunday
3     Monday
4    Tuesday
Name: Time, dtype: object

# Get ordinal day of the year 
df.Time.dt.dayofyear.head() 

0    152
1    181
2     46
3    152
4    108
Name: Time, dtype: int64

My Personal Notes

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