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How to extract date from Excel file using Pandas?
  • Last Updated : 29 Dec, 2020
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Prerequisite: Regular Expressions in Python

In this article, Let’s see how to extract date from the Excel file. Suppose our Excel file looks like below given image then we have to extract the date from the string and store it into a new Dataframe column.

date_sample_data.xlsx

For viewing the Excel file Click Here.

Approach :

  • Import required module.
  • Import data from Excel file.
  • Make an extra column for a new date.
  • Set Index for searching.
  • Define the pattern of date format.
  • Search Date and assigning to the respective column in Dataframe.

Let’s see Step-By-Step-Implementation:



Step 1: Import the required module and read data from the Excel file.

Python3




# import required module
import pandas as pd;
import re;
  
# Read excel file and store in to DataFrame
data = pd.read_excel("date_sample_data.xlsx");
  
print("Original DataFrame")
data

Output:

Step 2: Make an extra column for a new date.

Python3




# Create column for Date
data['new_Date']= None
data

Output:



Step 3: Set Index for searching.

Python3




# set required index
index_set = data.columns.get_loc('Description')
index_date = data.columns.get_loc('new_Date')
  
print(index_set, index_date)

Output:

1 2

Step 4: Defining the Pattern of the date format.

We need to create a Regular expression for date pattern in DD/MM/YY format. Use the [0-9] expression to find any character between the brackets that is a digit. Use escape sequence “\” for escaping “/” a special symbol and {2}, {4} is used to denote no of times a character belongs to the given string. So the expression become ‘[0-9]{2}\/[0-9]{2}\/[0-9]{4}’.

Example:

02/04/2020
02 -----> [0 to 9] --> [0-9]
number of character inside the string {2} ( i.e DD)

04- ----> [0 to 9] --> [0-9]
number of character inside the string {2} ( i.e MM)

2020 -->[0 to 9] -->[0-9]
number of character inside the sting {4} ( i.e YYYY)

Python3




# In DD/MM/YYYY
date_pattern = r'([0-9]{2}\/[0-9]{2}\/[0-9]{4})'

Step 5: Search Date and assigning to the respective column in Dataframe.

For searching the Date using regex in a string we are using re.search() function of re library.

Python3




for row in range(0, len(data)):
    Date = re.search(date_pattern,data.iat[row,index_set]).group()
    data.iat[row, index_date] = Date
      
# show the Dataframe
data

Output:

Complete Code:

Python3




# importing required module
import pandas as pd;
import re;
  
data = pd.read_excel("date_sample_data.xlsx");
  
print("Original data : \n",
      data)
  
# Create column for Date
data['new_Date'] = None
  
# set index
index_set = data.columns.get_loc('Description')
index_date = data.columns.get_loc('new_Date')
print(index_set, index_date)
  
# define pattern for date
# in DD/MM/YYYY
date_pattern = r'([0-9]{2}\/[0-9]{2}\/[0-9]{4})'
  
# searching pattern
# And storing in to DataFrame
for row in range(0, len(data)):
    Date = re.search(date_pattern, 
                     data.iat[row,index_set]).group()
    data.iat[row, index_date] = Date
  
# show the Dataframe
data

Output:

Note: Before running this program, make sure you have already installed xlrd library in your Python environment.

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