1595772895

This is a step by step tutorial of the Numpy module in python for Beginners. Learn about Numpy arrays and setting up Numpy on our system to get started.

An array is a variable that can store multiple values in itself. An array can hold a large number of values so they can be accessed using index numbers. The index always begins with 0 being the first element of the array and moving to the nth element, which will be the last. These values are of the same data type, which can be one of these strings, integer or float and also many others. Arrays can be of three types such as indexed arrays, associative arrays and multidimensional arrays.

NumPy is a python based library that helps in the manipulation of the multidimensional arrays with regards to various factors. As a result, this helps in maintaining large and multidimensional arrays and matrics. NumPy stands for Numerical Python. So this open-source help in performing various mathematical functions because it is capable of solving complex problems in arrays. A NumPy array is a group of values with the same data type, and these are given their index number with only non-negative integer numbers. The dimension form up the rank of the array and also integer which gives the size of the array along with dimension form the shape of the array.

In the early years, the Python was not supposed to be used for the numerical reason; as a result, it was not so common among the developers. In the year of 2005, Travis Oliphant was trying to develop a unified array package, and the next year it came in to use known with the name of NumPy 1.0 in the year 2006. Everyone can use it as it is open source and free to use.

I am going to discuss a few reasons why we should use NumPy:

- NumPy helps in the faster processing of the array.
- NumPy is preferred because it can perform operations like Searching and Sorting.
- It can perform all sorts of mathematical and logical operations.
- Arrays can operate fast. As a result, they are better than lists.
- It is good for data analysis.
- It is a good replacement for MATLAB and OCTAVE.
- Supports the use of vector operations.
- It is convenient to perform operations on arrays.
- NumPy is very much helpful in machine learning and data science.
- It helps in high-performance computing and simulations.

- It can handle on multidimensional arrays.
- NumPy refers to array objects as
`ndarray`

as a result, it provides various supporting functions. - It can work with all the latest CPU architectures.
- It has tools for using C/C++ and Fortran Code.

- NumPy deals with the problem of missing values, but it supports ‘NAN’ which creates confusion among the users.
- Also, it creates issues while comparing values with the help of a python interpreter.
- It requires the allocation of memory for performing functions like addition and insertion, which makes it difficult to manage the space.
- This memory allocation also makes it costly to use.
- Also, it requires shifting in order to use various memory allocations.
- It focuses on working with only numerical data.

The location for the source code of the NumPy is as follows: https://github.com/numpy/numpy

And also GitHub allows any number of users to use their codebase and they can work on the same codebase as it is enabled by the GitHub.

#programming #python #installation #numpy #python tutorial #setup

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

1595664780

Python is an open-source object-oriented language. It has many features of which one is the wide range of external packages. There are a lot of packages for installation and use for expanding functionalities. These packages are a repository of functions in python script. NumPy is one such package to ease array computations. To install all these python packages we use the pip- package installer. Pip is automatically installed along with Python. We can then use pip in the command line to install packages from PyPI.

**_Keeping you updated with latest technology trends, _***Join DataFlair on Telegram*

Python comes pre-installed on Mac OS. However, it has an old system version the newer versions can be downloaded alongside.

1. Open the terminal in your MacBook.

2. In the terminal, we use the pip command to install the package

- pip install numpy

3. If you use Python3, enter the pip3 command.

- pip3 install numpy

#numpy tutorials #install numpy #installing numpy #numpy installation

1595772895

This is a step by step tutorial of the Numpy module in python for Beginners. Learn about Numpy arrays and setting up Numpy on our system to get started.

An array is a variable that can store multiple values in itself. An array can hold a large number of values so they can be accessed using index numbers. The index always begins with 0 being the first element of the array and moving to the nth element, which will be the last. These values are of the same data type, which can be one of these strings, integer or float and also many others. Arrays can be of three types such as indexed arrays, associative arrays and multidimensional arrays.

NumPy is a python based library that helps in the manipulation of the multidimensional arrays with regards to various factors. As a result, this helps in maintaining large and multidimensional arrays and matrics. NumPy stands for Numerical Python. So this open-source help in performing various mathematical functions because it is capable of solving complex problems in arrays. A NumPy array is a group of values with the same data type, and these are given their index number with only non-negative integer numbers. The dimension form up the rank of the array and also integer which gives the size of the array along with dimension form the shape of the array.

In the early years, the Python was not supposed to be used for the numerical reason; as a result, it was not so common among the developers. In the year of 2005, Travis Oliphant was trying to develop a unified array package, and the next year it came in to use known with the name of NumPy 1.0 in the year 2006. Everyone can use it as it is open source and free to use.

I am going to discuss a few reasons why we should use NumPy:

- NumPy helps in the faster processing of the array.
- NumPy is preferred because it can perform operations like Searching and Sorting.
- It can perform all sorts of mathematical and logical operations.
- Arrays can operate fast. As a result, they are better than lists.
- It is good for data analysis.
- It is a good replacement for MATLAB and OCTAVE.
- Supports the use of vector operations.
- It is convenient to perform operations on arrays.
- NumPy is very much helpful in machine learning and data science.
- It helps in high-performance computing and simulations.

- It can handle on multidimensional arrays.
- NumPy refers to array objects as
`ndarray`

as a result, it provides various supporting functions. - It can work with all the latest CPU architectures.
- It has tools for using C/C++ and Fortran Code.

- NumPy deals with the problem of missing values, but it supports ‘NAN’ which creates confusion among the users.
- Also, it creates issues while comparing values with the help of a python interpreter.
- It requires the allocation of memory for performing functions like addition and insertion, which makes it difficult to manage the space.
- This memory allocation also makes it costly to use.
- Also, it requires shifting in order to use various memory allocations.
- It focuses on working with only numerical data.

The location for the source code of the NumPy is as follows: https://github.com/numpy/numpy

And also GitHub allows any number of users to use their codebase and they can work on the same codebase as it is enabled by the GitHub.

#programming #python #installation #numpy #python tutorial #setup

1619510796

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

**Lambda function in python**: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

**Syntax: x = lambda arguments : expression**

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

1626775355

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

**Robust frameworks **

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

**Simple to read and compose **

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

**Utilized by the best **

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

**Massive community support **

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

**Progressive applications **

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development