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 

Python3

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 

Python3

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

x = pd.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 datetime properties from here.

Code #3: Break date and time into separate features  

Python3

# 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.

Python3

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

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

Python3

# Convert timestamp to datetime
t.to_datetime()

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

Python3

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

2018
8
25
15
53

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

Python3

import pandas as pd

url = 'http://bit.ly/uforeports'

# read csv file
df = pd.read_csv(url)
df.head()

Output:

Python3

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

df.head()

Python3

# shows the type of each column data
df.dtypes

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

Python3

# 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

Python3

# 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

Python3

# 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