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1657081614

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

**Workbook:** A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

**Sheet:** A sheet is a single page composed of cells for organizing data.

**Cell:** The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

**Row:** A row is a horizontal line represented by a number (1,2, etc.).

**Column:** A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

`pip `

**install** openpyxl

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the

which creates a new workbook.**function** **Workbook**()

```
from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method
ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position
#Renaming the sheet
ws.title = "Example"
#save the workbook
wb.save(filename = "example.xlsx")
```

We load the file using the

which takes the filename as an argument. The file must be saved in the same working directory.**function** **load_Workbook**()

```
#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")
```

```
#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']
#getting a particular sheet
sheet1 = wb["sheet2"]
#getting sheet title
sheet1.title
result = 'sheet2'
#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'
```

```
#get a cell from the sheet
sheet1["A1"] <
Cell 'Sheet1'.A1 >
#get the cell value
ws["A1"].value 'Segment'
#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10
```

```
#looping through each row and column
for x in range(1, 5):
for y in range(1, 5):
print(x, y, ws.cell(row = x, column = y)
.value)
#getting the highest row number
ws.max_row
701
#getting the highest column number
ws.max_column
19
```

There are two functions for iterating through rows and columns.

```
Iter_rows() => returns the rows
Iter_cols() => returns the columns {
min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.
```

**Example:**

```
#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
for cell in row:
print(cell) <
Cell 'Sheet1'.A2 >
<
Cell 'Sheet1'.B2 >
<
Cell 'Sheet1'.C2 >
<
Cell 'Sheet1'.A3 >
<
Cell 'Sheet1'.B3 >
<
Cell 'Sheet1'.C3 >
#iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
for cell in col:
print(cell) <
Cell 'Sheet1'.A2 >
<
Cell 'Sheet1'.A3 >
<
Cell 'Sheet1'.B2 >
<
Cell 'Sheet1'.B3 >
<
Cell 'Sheet1'.C2 >
<
Cell 'Sheet1'.C3 >
```

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.

**Example:**

```
for row in ws.values:
for value in row:
print(value)
```

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

```
#creates a new workbook
wb = openpyxl.Workbook()
#saving the workbook
wb.save("new.xlsx")
```

```
#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")
#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")
#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']
#deleting a sheet
del wb['sheet 0']
#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']
```

```
#checking the sheet value
ws['B2'].value
null
#adding value to cell
ws['B2'] = 367
#checking value
ws['B2'].value
367
```

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.

**For example:**

```
import openpyxl
from openpyxl
import Workbook
wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
ws['A9'] = '=SUM(A2:A8)'
wb.save("new2.xlsx")
```

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

**For example:****Merge cells**

```
#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"
```

Adding the above code to the previous example will merge cells as below.

```
#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')
```

The above code will unmerge cells from B2 to C9.

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

**Example:**

```
import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image
wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3
ws.add_image(img, 'A3')
wb.save("new2.xlsx")
```

**Result:**

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

**Example**

```
import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series
wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active
values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")
```

**Result**

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️

00:00 | Introduction

02:14 | Installing openpyxl

03:19 | Testing Installation

04:25 | Loading an Existing Workbook

06:46 | Accessing Worksheets

07:37 | Accessing Cell Values

08:58 | Saving Workbooks

09:52 | Creating, Listing and Changing Sheets

11:50 | Creating a New Workbook

12:39 | Adding/Appending Rows

14:26 | Accessing Multiple Cells

20:46 | Merging Cells

22:27 | Inserting and Deleting Rows

23:35 | Inserting and Deleting Columns

24:48 | Copying and Moving Cells

26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄

OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/

Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial

Subscribe: https://www.youtube.com/c/TechWithTim/featured

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1603753200

So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some **regression** algorithms, **classification** algorithms and algorithms that can be used for both types of problems (**SVM****, ****Decision Trees** and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for **clustering** and learned about several clustering techniques.

We also talked about how to quantify machine learning model **performance** and how to improve it with **regularization**. In all these articles, we used Python for “from the scratch” implementations and libraries like **TensorFlow**, **Pytorch** and SciKit Learn. The word optimization popped out more than once in these articles, so in this and next article, we focus on optimization techniques which are an important part of the machine learning process.

In general, every machine learning algorithm is composed of three integral parts:

- A
**loss**function. - Optimization criteria based on the loss function, like a
**cost**function. **Optimization**technique – this process leverages training data to find a solution for optimization criteria (cost function).

As you were able to see in previous articles, some algorithms were created intuitively and didn’t have optimization criteria in mind. In fact, mathematical **explanations** of why and how these algorithms work were done later. Some of these algorithms are **Decision Trees** and **kNN**. Other algorithms, which were developed later had this thing in mind beforehand. **SVM**is one example.

During the training, we change the parameters of our machine learning model to try and **minimize** the loss function. However, the question of how do you change those parameters arises. Also, by how much should we change them during training and when. To answer all these questions we use **optimizers**. They put all different parts of the machine learning algorithm together. So far we mentioned **Gradient Decent** as an optimization technique, but we haven’t explored it in more detail. In this article, we focus on that and we cover the **grandfather** of all optimization techniques and its variation. Note that these techniques are **not** machine learning algorithms. They are solvers of **minimization** problems in which the function to minimize has a gradient in most points of its domain.

Data that we use in this article is the famous *Boston Housing Dataset* . This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small **dataset** with only 506 samples.

For the purpose of this article, make sure that you have installed the following _Python _libraries:

- **NumPy **– Follow
**this guide**if you need help with installation. - **SciKit Learn **– Follow
**this guide**if you need help with installation. **Pandas**– Follow**this guide**if you need help with installation.

Once installed make sure that you have imported all the necessary modules that are used in this tutorial.

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor
```

Apart from that, it would be good to be at least familiar with the basics of **linear algebra**, **calculus** and **probability**.

Note that we also use simple **Linear Regression** in all examples. Due to the fact that we explore **optimization**techniques, we picked the easiest machine learning algorithm. You can see more details about Linear regression **here**. As a quick reminder the formula for linear regression goes like this:

where *w* and *b* are parameters of the machine learning algorithm. The entire point of the training process is to set the correct values to the *w* and *b*, so we get the desired output from the machine learning model. This means that we are trying to make the value of our **error vector** as small as possible, i.e. to find a **global minimum of the cost function**.

One way of solving this problem is to use calculus. We could compute derivatives and then use them to find places where is an extrema of the cost function. However, the cost function is not a function of one or a few variables; it is a function of all parameters of a machine learning algorithm, so these calculations will quickly grow into a monster. That is why we use these optimizers.

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