How to Use Python to Automate Excel Tasks

How to Use Python to Automate Excel Tasks

Surprisingly, Excel is a widely used tool for data analysis.

    With Excel, data analysts can easily alter, evaluate, and present enormous amounts of data, making it easier to get insights and make informed decisions.

Excel's adaptability enables users to do a range of data analysis tasks, from simple mathematical operations to complex statistical analysis. Moreover, Excel has automation capabilities through the use of external programs like Python or the built-in programming language VBA.

Due to its adaptability and usability, Excel is frequently used for data analysis across a variety of industries, including banking, healthcare, and marketing.   

But if you work with Excel as a data analyst, you might frequently discover that you repeat the same boring tasks on a daily basis.

Data copying and pasting, cell formatting, and chart creation are a few examples of these operations. This can seem boring and time-consuming over time, leaving you with less time to concentrate on the most crucial parts of data analysis, including finding trends, outliers, and insights.

In order to optimize your workflows and free up time for more insightful analysis, automating Excel with Python can be a game-changer.

In this lesson, I'll demonstrate some practical Python programming techniques for creating, editing, and analyzing Excel spreadsheets. Let's start now.

How to Use Python to Combine Two Different Spreadsheets

    Data analysts frequently work with numerous spreadsheets, which can be challenging when you need to combine such files.

    With the help of the code provided below, you can combine two separate files.

import pandas as pd
                  
	# Read in the two Excel files
	file1 = pd.read_excel('file1.xlsx')file2 = pd.read_excel('file2.xlsx')
                  
	# Merge the two files using the concat() method
	merged_file = pd.concat([file1, file2], ignore_index=True)
                  
	# Write the merged file to a new Excel file
	merged_file.to_excel('merged_file.xlsx', index=False)
                    

    The Pandas library, which we'll need to read in and work with the Excel files, is initially imported in this code.

    The  read excel()  method is then used to read in files  file1.xlsx  and  file2.xlsx . The two files are then combined using the concat()  technique. To avoid having duplicate index values in the merged file, the ignore  index=True  option makes sure that the index values from both files are reset.

Data Import and Export Methods in Python

    With Python programs like Pandas, this job requires translating XLS files into a DataFrame object. Afterward, it may be modified and examined using Python.

You may export data from Python back into an XLS file using the same libraries.

import pandas as pd
            
	# Import Excel file
	df = pd.read_excel('filename.xlsx', sheet_name='Sheet1')
            
	# Export to Excel file
	df.to_excel('new_filename.xlsx', index=False)
    

    The preceding code imports the Pandas library and reads an Excel file named "filename.xlsx" from Sheet1 of the workbook, saving the data in a Pandas dataframe named "df". The dataframe is then exported to a brand-new Excel file with the name "new filename.xlsx" using the "to excel" technique. When "index=False" is used, row indexing is not included in the output file.

In essence, the code copies the data from the original Excel file to a new file using Pandas.

Methods for Python Data Cleansing and Transformation

    The task at hand is to clean and alter data in Excel using Python modules like Pandas.

It is possible to remove duplicate data, filter data based on predetermined criteria, and do computations on data.

import pandas as pd
        
	# Remove duplicates
	df = df.drop_duplicates()
    
	# Filter data
	df = df[df['column_name'] > 10]
        
	# Perform calculations
	df['new_column'] = df['column1'] + df['column2']
    

    The aforementioned code piece performs data cleansing and transformation operations on a Pandas dataframe called "df".

To start, it removes duplicate rows from the "df" table using the "drop duplicates" method. A new dataframe called "data df" is formed by filtering the "df" dataframe by choosing rows where the value in the "column name" column is larger than 10.

The last addition adds the values from "column1" and "column2" to "df" in a new column called "new column".

The code effectively cleans and manipulates the data by removing duplicates, filtering specific rows, and adding a new calculated column to the original dataframe.

How to Analyze Data Using Python

    This task comprises using Python tools like Pandas and NumPy to perform data analysis on Excel data.

This could require creating specialized reports by compiling data in accordance with established standards or computing summary statistics like mean and standard deviation.

import pandas as pd
import numpy as np
    
	# Calculate summary statistics
	df.describe()
            
	# Create custom reports
 	df.pivot_table(values='column_name', index='category_name', columns='date')
    

The code works with a Pandas dataframe called "df" to execute data analysis and reporting tasks using the NumPy and Pandas libraries.

Then, it uses the "describe" method to compute summary statistics for the dataframe's numerical data. The distribution, central tendency, and dispersion of the data are all insightfully revealed by this method.

Second, the code generates customized reports from the dataframe using the "pivot table" technique. This technique can create tables in a variety of formats while summarising and aggregating the data in the dataframe.

The "column name" values in this code are grouped by the "category name" and "date" columns to create a new dataframe.

In order to extract insights from the data, the code performs statistical analysis and reporting activities on the dataframe.

Steps for Creating Charts in Python

    Creating charts and graphs from Excel data using Python tools like  matplotlib  or seaborn is required for this assignment.

These charts can be customized to display specific data and formatted to meet certain requirements.

import pandas as pd
import matplotlib.pyplot as plt
  
	# Create a bar chart
	df.plot(kind='bar', x='category_name', y='sales')
	plt.show()
  
	# Create a scatter plot
	df.plot(kind='scatter', x='column1', y='column2')plt.show()

    The code imports two libraries,  Pandas  and  matplotlib.pyplot  using the aliases 'pd' and 'plt', respectively.

The Pandas "plot" method is then used to create two types of plots. The first type of plot is a bar chart that shows the relationship between the 'category_name' and 'sales' columns in the "df" dataframe.

The second type of plot is a scatter plot that shows the relationship between the 'column1' and 'column2' columns in the same dataframe. The code uses the parameters "kind='bar'" for the bar chart and "kind='scatter'" for the scatter plot to create the respective plots.

Lastly, the "show" method is called to display the plots on the screen. In summary, the code utilizes Pandas and matplotlib to create a bar chart and a scatter plot to visualize the data in the "df" dataframe.

Python Data Visualization Techniques

    Using Python tools like Plotly and bokeh, this endeavor comprises creating dynamic data visualizations from Excel data.

Users can study data in unique ways with the use of these representations, for as by concentrating on certain data points or filtering it in accordance with predetermined criteria.

  import pandas as pd
  import plotly.express as px
  
      # Create a heatmap
      fig = px.imshow(df.corr())
      fig.show()
  
      # Create a line chart
      fig = px.line(df, x='date', y='sales', color='category')
      fig.show()

The code produces two distinct types of visualizations using the Pandas and plotly.express libraries. The "imshow" method of plotly.express is then used to create a heatmap plot that shows the relationship between the columns in the "df" dataframe.

The "line" method of plotly.express is then used to create a line chart showing the relationship between the "date" and "sales" columns of the dataframe. Depending on the "category" column in the dataframe, this line chart makes a distinction between several categories. The two graphs are shown using the "show" function.

Conclusion

    Python is a flexible programming language that may be used to automate many Excel tasks. With a number of libraries, such as Pandas, openpyxl, xlwings, and pyautogui, you may also alter data, extract information, make reports, and automate repetitive tasks.

Automation may boost output, reduce mistakes, and save time and labour. Being skilled in Python could be a beneficial skill, regardless of whether you work as a data analyst or financial analyst. Studying Python could help you achieve greater things with your work.