Working With Date Column In Pandas Data Frame Using Python

Working Date Column in Data Frame using Python

• Suppressing Warnings
• Importing Pandas and NumPy
• Genrating Data within a given date range
• Extracting Year from Date Column
• Extracting Month From Date Column
• Deriving Month Name Abbrivate From Month Number using calender Module
• Extracting Week Number From Date Column
• Extracting Quarter Number from Date Column
• Deriving Quarter Name from Quarter Number
• Extracting Week Day From date
• Deriving Day Name from Week Day
• Extracting Day of Month From Date Column
• Extracting Hour from the Date column
• Extracting Minute from Date column
• Extraction Seconds from Date column
• Video Explanation and Execution
• You can Subscribe to My channel By clicking on following Button
• If you want to learn Python or Need my help or Want to recive similar posts:
• Contact Details:
• Social Media Links:

Suppressing Warnings¶

In [1]:

import warnings
warnings.filterwarnings('ignore')

Importing Pandas and NumPy¶

In [2]:

import pandas as pd
import numpy as np

Genrating Data within a given date range¶

In [3]:

Start_date = "2020-01-01"

In [4]:

End_date = "2020-12-31"

In [5]:

var = pd.date_range(Start_date,End_date)

In [6]:

data = pd.DataFrame(var)

In [7]:

data.head()

Out[7]:

	0
0	2020-01-01
1	2020-01-02
2	2020-01-03
3	2020-01-04
4	2020-01-05

In [8]:

data.rename(columns={0:"Date"},inplace=True)

In [9]:

data.head()

Out[9]:

	Date
0	2020-01-01
1	2020-01-02
2	2020-01-03
3	2020-01-04
4	2020-01-05

In [10]:

data.size

Out[10]:

366

In [11]:

type(data)

Out[11]:

pandas.core.frame.DataFrame

In [12]:

data.head(10)

Out[12]:

	Date
0	2020-01-01
1	2020-01-02
2	2020-01-03
3	2020-01-04
4	2020-01-05
5	2020-01-06
6	2020-01-07
7	2020-01-08
8	2020-01-09
9	2020-01-10

Extracting Year from Date Column¶

In [13]:

data['Year'] = data['Date'].dt.year

In [14]:

data.head()

Out[14]:

	Date	Year
0	2020-01-01	2020
1	2020-01-02	2020
2	2020-01-03	2020
3	2020-01-04	2020
4	2020-01-05	2020

In [15]:

data.rename(columns={0:"Date"},inplace='True')

Extracting Month From Date Column¶

In [16]:

data['Month'] = data['Date'].dt.month

In [17]:

data.head()

Out[17]:

	Date	Year	Month
0	2020-01-01	2020	1
1	2020-01-02	2020	1
2	2020-01-03	2020	1
3	2020-01-04	2020	1
4	2020-01-05	2020	1

In [18]:

import calendar

In [19]:

# Deriving month name from month number using calender module
data["Month_Name"] = data.Month.apply(lambda x: calendar.month_name[x])

In [20]:

data.head()

Out[20]:

	Date	Year	Month	Month_Name
0	2020-01-01	2020	1	January
1	2020-01-02	2020	1	January
2	2020-01-03	2020	1	January
3	2020-01-04	2020	1	January
4	2020-01-05	2020	1	January

Deriving Month Name Abbrivate From Month Number using calender Module¶

In [21]:

data["Month_Abrr"] = data.Month.apply(lambda x: calendar.month_abbr[x])

In [22]:

data.head()

Out[22]:

	Date	Year	Month	Month_Name	Month_Abrr
0	2020-01-01	2020	1	January	Jan
1	2020-01-02	2020	1	January	Jan
2	2020-01-03	2020	1	January	Jan
3	2020-01-04	2020	1	January	Jan
4	2020-01-05	2020	1	January	Jan

Extracting Week Number From Date Column¶

In [23]:

data['Week_Number'] = data['Date'].dt.week

In [24]:

data.head()

Out[24]:

	Date	Year	Month	Month_Name	Month_Abrr	Week_Number
0	2020-01-01	2020	1	January	Jan	1
1	2020-01-02	2020	1	January	Jan	1
2	2020-01-03	2020	1	January	Jan	1
3	2020-01-04	2020	1	January	Jan	1
4	2020-01-05	2020	1	January	Jan	1

Extracting Quarter Number from Date Column¶

In [25]:

data['Quarter_Number'] = data['Date'].dt.quarter

In [26]:

data.head()

Out[26]:

	Date	Year	Month	Month_Name	Month_Abrr	Week_Number	Quarter_Number
0	2020-01-01	2020	1	January	Jan	1	1
1	2020-01-02	2020	1	January	Jan	1	1
2	2020-01-03	2020	1	January	Jan	1	1
3	2020-01-04	2020	1	January	Jan	1	1
4	2020-01-05	2020	1	January	Jan	1	1

In [27]:

# For converting Number to words
from num2words import num2words

Deriving Quarter Name from Quarter Number¶

In [28]:

data["Quarter_Name"] = data.Quarter_Number.apply(lambda x: num2words(x,to = 'ordinal'))

In [29]:

data.head()

Out[29]:

	Date	Year	Month	Month_Name	Month_Abrr	Week_Number	Quarter_Number	Quarter_Name
0	2020-01-01	2020	1	January	Jan	1	1	first
1	2020-01-02	2020	1	January	Jan	1	1	first
2	2020-01-03	2020	1	January	Jan	1	1	first
3	2020-01-04	2020	1	January	Jan	1	1	first
4	2020-01-05	2020	1	January	Jan	1	1	first

Extracting Week Day From date¶

In [30]:

data['Week_day'] = data['Date'].dt.weekday

In [31]:

data.head()

Out[31]:

	Date	Year	Month	Month_Name	Month_Abrr	Week_Number	Quarter_Number	Quarter_Name	Week_day
0	2020-01-01	2020	1	January	Jan	1	1	first	2
1	2020-01-02	2020	1	January	Jan	1	1	first	3
2	2020-01-03	2020	1	January	Jan	1	1	first	4
3	2020-01-04	2020	1	January	Jan	1	1	first	5
4	2020-01-05	2020	1	January	Jan	1	1	first	6

Deriving Day Name from Week Day¶

In [32]:

data["Week_day_name"] = data.Week_day.apply(lambda x: calendar.day_name[x])

In [33]:

data.head(10)

Out[33]:

	Date	Year	Month	Month_Name	Month_Abrr	Week_Number	Quarter_Number	Quarter_Name	Week_day	Week_day_name
0	2020-01-01	2020	1	January	Jan	1	1	first	2	Wednesday
1	2020-01-02	2020	1	January	Jan	1	1	first	3	Thursday
2	2020-01-03	2020	1	January	Jan	1	1	first	4	Friday
3	2020-01-04	2020	1	January	Jan	1	1	first	5	Saturday
4	2020-01-05	2020	1	January	Jan	1	1	first	6	Sunday
5	2020-01-06	2020	1	January	Jan	2	1	first	0	Monday
6	2020-01-07	2020	1	January	Jan	2	1	first	1	Tuesday
7	2020-01-08	2020	1	January	Jan	2	1	first	2	Wednesday
8	2020-01-09	2020	1	January	Jan	2	1	first	3	Thursday
9	2020-01-10	2020	1	January	Jan	2	1	first	4	Friday

Extracting Day of Month From Date Column¶

In [34]:

data['Day_of_month'] = data['Date'].dt.strftime("%d")

In [35]:

data.head()

Out[35]:

	Date	Year	Month	Month_Name	Month_Abrr	Week_Number	Quarter_Number	Quarter_Name	Week_day	Week_day_name	Day_of_month
0	2020-01-01	2020	1	January	Jan	1	1	first	2	Wednesday	01
1	2020-01-02	2020	1	January	Jan	1	1	first	3	Thursday	02
2	2020-01-03	2020	1	January	Jan	1	1	first	4	Friday	03
3	2020-01-04	2020	1	January	Jan	1	1	first	5	Saturday	04
4	2020-01-05	2020	1	January	Jan	1	1	first	6	Sunday	05

In [36]:

#creating a new dataframe
df = pd.DataFrame()

In [37]:

df['date'] = pd.date_range('1/1/2022', periods = 10, freq ='2H')
df

Out[37]:

	date
0	2022-01-01 00:00:00
1	2022-01-01 02:00:00
2	2022-01-01 04:00:00
3	2022-01-01 06:00:00
4	2022-01-01 08:00:00
5	2022-01-01 10:00:00
6	2022-01-01 12:00:00
7	2022-01-01 14:00:00
8	2022-01-01 16:00:00
9	2022-01-01 18:00:00

Extracting Hour from the Date column¶

In [38]:

df['Hour'] = df['date'].dt.hour

In [39]:

df

Out[39]:

	date	Hour
0	2022-01-01 00:00:00	0
1	2022-01-01 02:00:00	2
2	2022-01-01 04:00:00	4
3	2022-01-01 06:00:00	6
4	2022-01-01 08:00:00	8
5	2022-01-01 10:00:00	10
6	2022-01-01 12:00:00	12
7	2022-01-01 14:00:00	14
8	2022-01-01 16:00:00	16
9	2022-01-01 18:00:00	18

Extracting Minute from Date column¶

In [40]:

df['Minute'] = df['date'].dt.minute

In [41]:

df

Out[41]:

	date	Hour	Minute
0	2022-01-01 00:00:00	0	0
1	2022-01-01 02:00:00	2	0
2	2022-01-01 04:00:00	4	0
3	2022-01-01 06:00:00	6	0
4	2022-01-01 08:00:00	8	0
5	2022-01-01 10:00:00	10	0
6	2022-01-01 12:00:00	12	0
7	2022-01-01 14:00:00	14	0
8	2022-01-01 16:00:00	16	0
9	2022-01-01 18:00:00	18	0

Extraction Seconds from Date column¶

In [42]:

df['seconds'] = df['date'].dt.second

In [43]:

df

Out[43]:

	date	Hour	Minute	seconds
0	2022-01-01 00:00:00	0	0	0
1	2022-01-01 02:00:00	2	0	0
2	2022-01-01 04:00:00	4	0	0
3	2022-01-01 06:00:00	6	0	0
4	2022-01-01 08:00:00	8	0	0
5	2022-01-01 10:00:00	10	0	0
6	2022-01-01 12:00:00	12	0	0
7	2022-01-01 14:00:00	14	0	0
8	2022-01-01 16:00:00	16	0	0
9	2022-01-01 18:00:00	18	0	0

Video Explanation and Execution¶

You can Subscribe to My channel By clicking on following Button¶

If you want to learn Python or Need my help or Want to recive similar posts:¶

Contact Details:¶

Email : dhawanppd@gmail.com

Whatsapp : 8010152597

Social Media Links:¶

Linkedin:- https://www.linkedin.com/in/parag-dhawan

Youtube:- https://www.youtube.com/c/ParagDhawan

Facebook Page:- http://fb.me/dhawanparag

Instagram: - https://www.instagram.com/paragdhawan/

Twitter:- https://twitter.com/dhawan_parag

GitHub:- https://github.com/paragdhawan/