Category: Uncategorized

https://media.notthebee.com/articles/6446e41a0c40a6446e41a0c40b.jpg

Ryan Webb is a County Councilman in Delaware County, Indiana and he is making lefties BIG MAD with his recent announcement that he is now a trans-lesbian-woman of color.

Not the Bee

https://s.w.org/images/core/emoji/14.0.0/72×72/1f4a1.png

Setting up the Environment

Before diving into web scraping with Python, set up your environment by installing the necessary libraries.

First, install the following libraries: requests, BeautifulSoup, and pandas. These packages play a crucial role in web scraping, each serving different purposes.

To install these libraries, click on the previously provided links for a full guide (including troubleshooting) or simply run the following commands:

pip install requests
pip install beautifulsoup4
pip install pandas

The requests library will be used to make HTTP requests to websites and download the HTML content. It simplifies the process of fetching web content in Python.

BeautifulSoup is a fantastic library that helps extract data from the HTML content fetched from websites. It makes navigating, searching, and modifying HTML easy, making web scraping straightforward and convenient.

Pandas will be helpful in data manipulation and organizing the scraped data into a CSV file. It provides powerful tools for working with structured data, making it popular among data scientists and web scraping enthusiasts.

Fetching and Parsing URL

Next, you’ll learn how to fetch and parse URLs using Python to scrape data and save it as a CSV file. We will cover sending HTTP requests, handling errors, and utilizing libraries to make the process efficient and smooth.

Sending HTTP Requests

When fetching content from a URL, Python offers a powerful library known as the requests library. It allows users to send HTTP requests, such as GET or POST, to a specific URL, obtain a response, and parse it for information.

We will use the requests library to help us fetch data from our desired URL.

For example:

import requests
response = requests.get('https://example.com/data.csv')

The variable response will store the server’s response, including the data we want to scrape. From here, we can access the content using response.content, which will return the raw data in bytes format.

Handling HTTP Errors

Handling HTTP errors while fetching data from URLs ensures a smooth experience and prevents unexpected issues. The requests library makes error handling easy by providing methods to check whether the request was successful.

Here’s a simple example:

import requests
response = requests.get('https://example.com/data.csv')
response.raise_for_status()

The raise_for_status() method will raise an exception if there’s an HTTP error, such as a 404 Not Found or 500 Internal Server Error. This helps us ensure that our script doesn’t continue to process erroneous data, allowing us to gracefully handle any issues that may arise.

With these tools, you are now better equipped to fetch and parse URLs using Python. This will enable you to effectively scrape data and save it as a CSV file.

Extracting Data from HTML

In this section, we’ll discuss extracting data from HTML using Python. The focus will be on utilizing the BeautifulSoup library, locating elements by their tags, and attributes.

Using BeautifulSoup

BeautifulSoup is a popular Python library that simplifies web scraping tasks by making it easy to parse and navigate through HTML. To get started, import the library and request the page content you want to scrape, then create a BeautifulSoup object to parse the data:

from bs4 import BeautifulSoup
import requests

url = "example_website"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

Now you have a BeautifulSoup object and can start extracting data from the HTML.

Locating Elements by Tags and Attributes

BeautifulSoup provides various methods to locate elements by their tags and attributes. Some common methods include find(), find_all(), select(), and select_one().

Let’s see these methods in action:

# Find the first <span> tag
span_tag = soup.find("span")

# Find all <span> tags
all_span_tags = soup.find_all("span")

# Locate elements using CSS selectors
title = soup.select_one("title")

# Find all <a> tags with the "href" attribute
links = soup.find_all("a", {"href": True})

These methods allow you to easily navigate and extract data from an HTML structure.

Once you have located the HTML elements containing the needed data, you can extract the text and attributes.

Here’s how:

# Extract text from a tag
text = span_tag.text

# Extract an attribute value
url = links[0]["href"]

Finally, to save the extracted data into a CSV file, you can use Python’s built-in csv module.

import csv

# Writing extracted data to a CSV file
with open("output.csv", "w", newline="") as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(["Index", "Title"])
    for index, link in enumerate(links, start=1):
        writer.writerow([index, link.text])

Following these steps, you can successfully extract data from HTML using Python and BeautifulSoup, and save it as a CSV file.

Recommended: Basketball Statistics – Page Scraping Using Python and BeautifulSoup

Organizing Data

This section explains how to create a dictionary to store the scraped data and how to write the organized data into a CSV file.

Creating a Dictionary

Begin by defining an empty dictionary that will store the extracted data elements.

In this case, the focus is on quotes, authors, and any associated tags. Each extracted element should have its key, and the value should be a list that contains individual instances of that element.

For example:

data = {
    "quotes": [],
    "authors": [],
    "tags": []
}

As you scrape the data, append each item to its respective list. This approach makes the information easy to index and retrieve when needed.

Working with DataFrames and Pandas

Once the data is stored in a dictionary, it’s time to convert it into a dataframe. Using the Pandas library, it’s easy to transform the dictionary into a dataframe where the keys become the column names and the respective lists become the rows.

Simply use the following command:

import pandas as pd

df = pd.DataFrame(data)

Exporting Data to a CSV File

With the dataframe prepared, it’s time to write it to a CSV file. Thankfully, Pandas comes to the rescue once again. Using the dataframe’s built-in .to_csv() method, it’s possible to create a CSV file from the dataframe, like this:

df.to_csv('scraped_data.csv', index=False)

This command will generate a CSV file called 'scraped_data.csv' containing the organized data with columns for quotes, authors, and tags. The index=False parameter ensures that the dataframe’s index isn’t added as an additional column.

Recommended: 17 Ways to Read a CSV File to a Pandas DataFrame

And there you have it—a neat, organized CSV file containing your scraped data!

Handling Pagination

This section will discuss how to handle pagination while scraping data from multiple URLs using Python to save the extracted content in a CSV format. It is essential to manage pagination effectively because most websites display their content across several pages.

Looping Through Web Pages

Looping through web pages requires the developer to identify a pattern in the URLs, which can assist in iterating over them seamlessly. Typically, this pattern would include the page number as a variable, making it easy to adjust during the scraping process.

Once the pattern is identified, you can use a for loop to iterate over a range of page numbers. For each iteration, update the URL with the page number and then proceed with the scraping process. This method allows you to extract data from multiple pages systematically.

For instance, let’s consider that the base URL for every page is "https://www.example.com/listing?page=", where the page number is appended to the end.

Here is a Python example that demonstrates handling pagination when working with such URLs:

import requests
from bs4 import BeautifulSoup
import csv

base_url = "https://www.example.com/listing?page="

with open("scraped_data.csv", "w", newline="") as csvfile:
    csv_writer = csv.writer(csvfile)
    csv_writer.writerow(["Data_Title", "Data_Content"])  # Header row

    for page_number in range(1, 6):  # Loop through page numbers 1 to 5
        url = base_url + str(page_number)
        response = requests.get(url)
        soup = BeautifulSoup(response.text, "html.parser")
        
        # TODO: Add scraping logic here and write the content to CSV file.

In this example, the script iterates through the first five pages of the website and writes the scraped content to a CSV file. Note that you will need to implement the actual scraping logic (e.g., extracting the desired content using Beautiful Soup) based on the website’s structure.

Handling pagination with Python allows you to collect more comprehensive data sets, improving the overall success of your web scraping efforts. Make sure to respect the website’s robots.txt rules and rate limits to ensure responsible data collection.

Exporting Data to CSV

You can export web scraping data to a CSV file in Python using the Python CSV module and the Pandas to_csv function. Both approaches are widely used and efficiently handle large amounts of data.

Python CSV Module

The Python CSV module is a built-in library that offers functionalities to read from and write to CSV files. It is simple and easy to use. To begin with, first, import the csv module.

import csv

To write the scraped data to a CSV file, open the file in write mode ('w') with a specified file name, create a CSV writer object, and write the data using the writerow() or writerows() methods as required.

with open('data.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(["header1", "header2", "header3"])
    writer.writerows(scraped_data)

In this example, the header row is written first, followed by the rows of data obtained through web scraping.

Using Pandas to_csv()

Another alternative is the powerful library Pandas, often used in data manipulation and analysis. To use it, start by importing the Pandas library.

import pandas as pd

Pandas offers the to_csv() method, which can be applied to a DataFrame. If you have web-scraped data and stored it in a DataFrame, you can easily export it to a CSV file with the to_csv() method, as shown below:

dataframe.to_csv('data.csv', index=False)

In this example, the index parameter is set to False to exclude the DataFrame index from the CSV file.

The Pandas library also provides options for handling missing values, date formatting, and customizing separators and delimiters, making it a versatile choice for data export.

10 Minutes to Pandas in 5 Minutes

If you’re just getting started with Pandas, I’d recommend you check out our free blog guide (it’s only 5 minutes!):

Recommended: 5 Minutes to Pandas — A Simple Helpful Guide to the Most Important Pandas Concepts (+ Cheat Sheet)

Be on the Right Side of Change

https://static1.makeuseofimages.com/wordpress/wp-content/uploads/2023/03/chatgpt-openai-logo-on-green-background-feature.jpg

 import openai

openai.api_key = "YOUR_API_KEY"

completion = openai.ChatCompletion.create(
  model = "gpt-3.5-turbo",
  temperature = 0.8,
  max_tokens = 2000,
  messages = [
    {"role": "system", "content": "You are a funny comedian who tells dad jokes."},
    {"role": "user", "content": "Write a dad joke related to numbers."},
    {"role": "assistant", "content": "Q: How do you make 7 even? A: Take away the s."},
    {"role": "user", "content": "Write one related to programmers."}
  ]
)

print(completion.choices[0].message) 

 import openai

openai.api_key = "YOUR_API_KEY"

completion = openai.ChatCompletion.create(
  model = "gpt-3.5-turbo",
  temperature = 0.8,
  max_tokens = 2000,
  messages = [
    {"role": "system", "content": "You are a poet who creates poems that evoke emotions."},
    {"role": "user", "content": "Write a short poem for programmers."}
  ]
)

print(completion.choices[0].message.content) 

 messages = [
  {"role": "user", "content": "Write a short poem for programmers."}
] 

 import requests

openai.api_key = "YOUR_API_KEY"
URL = "https://api.openai.com/v1/chat/completions"

payload = {
  "model": "gpt-3.5-turbo",
  "temperature" : 1.0,
  "messages" : [
    {"role": "system", "content": f"You are an assistant who tells any random and very short fun fact about this world."},
    {"role": "user", "content": f"Write a fun fact about programmers."},
    {"role": "assistant", "content": f"Programmers drink a lot of coffee!"},
    {"role": "user", "content": f"Write one related to the Python programming language."}
  ]
}

headers = {
  "Content-Type": "application/json",
  "Authorization": f"Bearer {openai.api_key}"
}

response = requests.post(URL, headers=headers, json=payload)
response = response.json()

print(response['choices'][0]['message']['content']) 

 pip install openai 

 import openai

openai.api_key = "YOUR_API_KEY"

response = openai.ChatCompletion.create(
  model = "gpt-3.5-turbo",
  temperature = 0.2,
  max_tokens = 1000,
  messages = [
    {"role": "user", "content": "Who won the 2018 FIFA world cup?"}
  ]
)

print(response['choices'][0]['message']['content']) 

MakeUseOf

https://s.w.org/images/core/emoji/14.0.0/72×72/2b50.png

To convert a list of tuples to a Pandas DataFrame, import the pandas library, call the DataFrame constructor, and pass the list of tuples as the data argument such as in pd.DataFrame(tuples_list, columns=['Number', 'Letter']).

Here’s a minimal example:

import pandas as pd
tuples_list = [(1, 'A'), (2, 'B'), (3, 'C')]
df = pd.DataFrame(tuples_list, columns=['Number', 'Letter'])

The output of the given code will be a Pandas DataFrame with two columns, 'Number' and 'Letter', as follows:

   Number Letter
0       1      A
1       2      B
2       3      C

After the Panda image, let’s dive deeper into this conversion technique so you can improve your skills and learn more on Pandas’ assume capabilities!

I’ll also show you how to convert a list of named tuples — and how to convert the DataFrame back to a list of tuples (key-value pairs).

Converting a List of Tuples to DataFrame

First, let’s explore how to convert a list of tuples into a DataFrame using Python’s Pandas library.

Using DataFrame Constructor

The simplest way to convert a list of tuples into a DataFrame is by using the DataFrame() constructor provided by the Pandas library. This method is straightforward and can be achieved in just a few lines of code.

Here’s an example:

import pandas as pd
tuple_list = [('A', 1), ('B', 2), ('C', 3)]
df = pd.DataFrame(tuple_list)
print(df)

Executing this code will create a DataFrame with the following structure:

Handling Data with Column Names

When converting a list of tuples to a DataFrame, it’s often useful to include column names to make the data more readable and understandable. To do this, you can add the columns parameter when calling the DataFrame() constructor.

Here’s an example:

import pandas as pd
tuple_list = [('A', 1), ('B', 2), ('C', 3)]
column_names = ['Letter', 'Number']
df = pd.DataFrame(tuple_list, columns=column_names)
print(df)

With the column names specified, the resulting DataFrame will look like this:

By using the DataFrame constructor and handling data with column names, you can easily convert a list of tuples into a DataFrame that is more organized and easier to understand. Keep working with these techniques, and soon enough, you’ll be a master of DataFrames!

Examples and Use Cases

When working with Python, one often encounters data stored in lists of tuples. These data structures are lightweight and easy to use, but sometimes, it’s beneficial to convert them into a more structured format, such as a DataFrame . In this section, we will explore some examples and use cases for converting a list of tuples into a DataFrame in Python, using the pandas library.

Here’s a simple example that demonstrates how to create a DataFrame from a list of tuples:

import pandas as pd

data = [('Peter', 18, 7), ('Riff', 15, 6), ('John', 17, 8), ('Michel', 18, 7), ('Sheli', 17, 5)]
df = pd.DataFrame(data, columns=['Name', 'Age', 'Score'])

In this example, we have a list of tuples representing student data, with each tuple containing a name, age, and score. By passing this list to the DataFrame constructor along with the column names, we can easily convert it into a DataFrame .

Consider another use case, where we need to filter and manipulate data before converting it into a DataFrame. For instance, let’s imagine we have a list of sales data, with each tuple representing an item, its price, and the number of sales:

data = [('Item A', 35, 20), ('Item B', 45, 15), ('Item C', 50, 30), ('Item D', 25, 10)]

In this case, we can use list comprehensions to filter items with sales greater than 20 and update the price by applying a 10% discount:

filtered_data = [(item, price * 0.9, sales) for item, price, sales in data if sales > 20]
df = pd.DataFrame(filtered_data, columns=['Item', 'Discounted Price', 'Sales'])

Now, our DataFrame contains only the filtered items with the discounted prices .

Python List of Named Tuples to DataFrame

Converting a list of named tuples to a DataFrame in Python can be done efficiently using the pandas library’s default functions as well.

Info: A named tuple is a subclass of a tuple, which allows you to access elements by name, making it highly readable and practical for data manipulation.

First, create a list of named tuples using Python’s built-in collections module.

Let’s assume we have a list of students with their names, ages, and test scores:

from collections import namedtuple

Student = namedtuple('Student', ['name', 'age', 'score'])
students = [
    Student('Alice', 23, 89),
    Student('Bob', 22, 92),
    Student('Charlie', 24, 85)
]

With the list of named tuples prepared, proceed to import the pandas library and use the pd.DataFrame() method to convert the list to a DataFrame:

import pandas as pd

dataframe = pd.DataFrame(students, columns=Student._fields)

This process creates a DataFrame with columns corresponding to the named tuple fields. The final result appears as follows:

      name  age  score
0    Alice   23     89
1      Bob   22     92
2  Charlie   24     85

In summary, simply define the list with the named tuple structure, and then call the pd.DataFrame() method to create the DataFrame.

Create a List of Tuples From a DataFrame

When working with data in Python, you may need to convert a DataFrame back into a list of tuples.

To begin, import the library in your Python code using import pandas as pd.

Now, let’s say you have a DataFrame, and you want to extract its data as a list of tuples. The simplest approach is to use the itertuples() function, which is a built-in method in Pandas (source).

To use this method, call the itertuples() function on the DataFrame object, and then pass the output to the list() function to convert it into a list:

python import pandas as pd

# Sample DataFrame data = {'Name': ['John', 'Alice', 'Tim'],
                           'Age': [28, 22, 27]}
df = pd.DataFrame(data)

# Convert DataFrame to list of tuples 
list_of_tuples = list(df.itertuples(index=False, name=None))
print(list_of_tuples) 

This code will output:

 [('John', 28), ('Alice', 22), ('Tim', 27)] 

The itertuples() method has two optional parameters: index and name. Setting index=False excludes the DataFrame index from the tuples, and setting name=None returns regular tuples instead of named tuples.

So there you go! You now know how to convert a DataFrame into a list of tuples using the Pandas library in Python . To keep learning and improving your Python skills, feel free to download our cheat sheets and visit the recommended Pandas tutorial:

Recommended: 10 Minutes to Pandas (in 5 Minutes)

Be on the Right Side of Change

While working with pandas dataframe, we often filter data using different conditions. In this article, we will discuss how we can use the pandas where method to filter and replace data from a series or dataframe.

The Pandas where() Method

We use the pandas where() method to replace a value based on a condition. The where() method has the following syntax.

DataFrame.where(cond, other=_NoDefault.no_default, *, inplace=False, axis=None, level=None)

Here, 

• The cond parameter takes a condition or multiple conditional statements as input arguments. The conditional statements must evaluate to a series of True and False values. If the cond parameter is True for a row, the data is preserved in that row. All the values are set to None for the rows where the cond parameter evaluates to False. 
• The other parameter takes a function, series, dataframe, or scaler value as its input argument. All the entries where the cond parameter evaluates to False are replaced with the corresponding value from the other parameter. If we pass a function to the other parameter, it is computed on the DataFrame and should return scalar or Series/DataFrame. The function must not change the input DataFrame. If we don’t specify the other parameter, all the values are set to None for the rows where the cond parameter evaluates to False. 
• By default, the where() method returns a new dataframe after execution. If you want to modify the existing dataframe using the where() method, you can set the inplace parameter to True. After this, the original dataframe will be modified to store the output.
• The axis parameter is used to set the alignment axis if needed. For Series, the axis parameter is unused. For dataframes, it has a default value of 0.
• The level parameter is used to set the alignment level if required.

Now, let us discuss how we can use the where() method with a series or a dataframe.

Pandas Where() Method With Series in Python

When we invoke the where() method on a pandas series, it takes a condition as its input argument. After execution, it returns a new series. In the output series, the values that fulfill the condition in the input argument and unchanged while the rest of the values are set to None. You can observe this in the following example.

import pandas as pd
series=pd.Series([1,23,12,423,4,53,231,234,1])
print("The input series is:")
print(series)
output=series.where(series>50)
print("The output series is:")
print(output)

Output:

The input series is:
0      1
1     23
2     12
3    423
4      4
5     53
6    231
7    234
8      1
dtype: int64
The output series is:
0      NaN
1      NaN
2      NaN
3    423.0
4      NaN
5     53.0
6    231.0
7    234.0
8      NaN
dtype: float64

In the above example, we passed the condition series>50 to the where() method. In the output series, you can observe that the where() method preserves the numbers greater than 50. On the other hand, values less than 50 are set to None.

Replace a Value Based on a Condition Using The where() Method

Instead of None, we can also set a replacement value for the values in the series that don’t fulfill the condition given in the input to the where() method. For this, we will pass the replacement value as the second input argument to the where() method. After execution, it returns a series in which the values that fulfill the condition remain unchanged while the other values are replaced using the replacement value. You can observe this in the following example.

import pandas as pd
series=pd.Series([1,23,12,423,4,53,231,234,1])
print("The input series is:")
print(series)
output=series.where(series>50,-1)
print("The output series is:")
print(output)

Output:

The input series is:
0      1
1     23
2     12
3    423
4      4
5     53
6    231
7    234
8      1
dtype: int64
The output series is:
0     -1
1     -1
2     -1
3    423
4     -1
5     53
6    231
7    234
8     -1
dtype: int64

In the above example, we have set the other parameter to -1. Hence, the numbers less than 50 are set to -1 in the output dataframe.

Replace a Value Using a Function Based on a Condition Using The where() Method

Instead of a value, we can also pass a function for replacing the values in the series using the where() method. For instance, consider the following example.

def myFun(x):
    return x**2
import pandas as pd
series=pd.Series([1,23,12,423,4,53,231,234,1])
print("The input series is:")
print(series)
output=series.where(series>50,other=myFun)
print("The output series is:")
print(output)

Output:

The input series is:
0      1
1     23
2     12
3    423
4      4
5     53
6    231
7    234
8      1
dtype: int64
The output series is:
0      1
1    529
2    144
3    423
4     16
5     53
6    231
7    234
8      1
dtype: int64

In the above code, we have defined a function myFun() that takes a number and returns its square. Then, we passed the function to the other parameter in the where() method. After this, the values less than 50 are first passed to the function myFun(). The where() method then gives the output of myFun() function in the output series in all the positions where the cond parameter is False.

Pandas Where Method With DataFrame

Instead of a series, we can also use the where() method on a dataframe. When we invoke the where() method on a dataframe, it takes a condition as its input argument. After execution, it returns a dataframe created from the input dataframe.

Here, the rows that fulfill the condition given as input to the where() method remain unchanged. All the other rows are filled with a None value. You can observe this in the following example.

import pandas as pd
myDicts=[{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90},
        {"Roll":2,"Maths":80, "Physics":100, "Chemistry": 90},
        {"Roll":3,"Maths":90, "Physics":80, "Chemistry": 70},
        {"Roll":4,"Maths":100, "Physics":100, "Chemistry": 90},
        {"Roll":5,"Maths":90, "Physics":90, "Chemistry": 80},
        {"Roll":6,"Maths":80, "Physics":70, "Chemistry": 70}]
df=pd.DataFrame(myDicts)
print("The input dataframe is:")
print(df)
df1=df.where(df["Maths"]>80)
print("The output dataframe is:")
print(df1)

Output:

The input dataframe is:
   Roll  Maths  Physics  Chemistry
0     1    100       80         90
1     2     80      100         90
2     3     90       80         70
3     4    100      100         90
4     5     90       90         80
5     6     80       70         70
The output dataframe is:
   Roll  Maths  Physics  Chemistry
0   1.0  100.0     80.0       90.0
1   NaN    NaN      NaN        NaN
2   3.0   90.0     80.0       70.0
3   4.0  100.0    100.0       90.0
4   5.0   90.0     90.0       80.0
5   NaN    NaN      NaN        NaN

Instead of the None value, we can also give a replacement value to the where() method as shown below.

import pandas as pd
myDicts=[{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90},
        {"Roll":2,"Maths":80, "Physics":100, "Chemistry": 90},
        {"Roll":3,"Maths":90, "Physics":80, "Chemistry": 70},
        {"Roll":4,"Maths":100, "Physics":100, "Chemistry": 90},
        {"Roll":5,"Maths":90, "Physics":90, "Chemistry": 80},
        {"Roll":6,"Maths":80, "Physics":70, "Chemistry": 70}]
df=pd.DataFrame(myDicts)
print("The input dataframe is:")
print(df)
df1=df.where(df["Maths"]>80,"LOW")
print("The output dataframe is:")
print(df1)

Output:

The input dataframe is:
   Roll  Maths  Physics  Chemistry
0     1    100       80         90
1     2     80      100         90
2     3     90       80         70
3     4    100      100         90
4     5     90       90         80
5     6     80       70         70
The output dataframe is:
  Roll Maths Physics Chemistry
0    1   100      80        90
1  LOW   LOW     LOW       LOW
2    3    90      80        70
3    4   100     100        90
4    5    90      90        80
5  LOW   LOW     LOW       LOW

In the above examples, you can observe that the where() method works in a similar manner it works with a series. The only difference is that the results are applied to the entire row instead of a single value.

Pandas where() Method With Multiple Conditions

We can also use multiple conditions in a single where method. For this, we will operate all the conditions with AND/OR logical operator. After the execution of each condition, the logical operations are performed and we get a mask containing True and False values. The mask is then used to create the output dataframe. You can observe this in the following example.

import pandas as pd
myDicts=[{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90},
        {"Roll":2,"Maths":80, "Physics":100, "Chemistry": 90},
        {"Roll":3,"Maths":90, "Physics":80, "Chemistry": 70},
        {"Roll":4,"Maths":100, "Physics":100, "Chemistry": 90},
        {"Roll":5,"Maths":90, "Physics":90, "Chemistry": 80},
        {"Roll":6,"Maths":80, "Physics":70, "Chemistry": 70}]
df=pd.DataFrame(myDicts)
print("The input dataframe is:")
print(df)
df1=df.where((df["Maths"]>80) & (df["Chemistry"]>80))
print("The output dataframe is:")
print(df1)

Output:

The input dataframe is:
   Roll  Maths  Physics  Chemistry
0     1    100       80         90
1     2     80      100         90
2     3     90       80         70
3     4    100      100         90
4     5     90       90         80
5     6     80       70         70
The output dataframe is:
   Roll  Maths  Physics  Chemistry
0   1.0  100.0     80.0       90.0
1   NaN    NaN      NaN        NaN
2   NaN    NaN      NaN        NaN
3   4.0  100.0    100.0       90.0
4   NaN    NaN      NaN        NaN
5   NaN    NaN      NaN        NaN

Conclusion

In this article, we discussed different ways to use the pandas where method with a series or dataframe in Python. To learn more about Python programming, you can read this article on how to read excel into pandas dataframe. You might also like this article on how to map functions to a pandas series in Python.

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy Learning!

The post Pandas Where Method With Series and DataFrame appeared first on PythonForBeginners.com.

Planet Python

https://theawesomer.com/photos/2023/04/cruise_ship_time_lapse_t.jpg

Link

MK Timelapse GmbH recorded the entire construction process of the AIDAnova, an 1105-foot-long cruise ship that carries 6600 passengers. The footage from Germany’s Meyer Neptun and Finland’s Meyer Turku shipyards starts out with massive engines being built and takes us through the fascinating modular assembly process.

The Awesomer

https://static1.makeuseofimages.com/wordpress/wp-content/uploads/2023/04/python-logo-with-scribbled-python-functions-3.jpg

 

def calculate_sum(a, b, c):
    return a+b+c

print(calculate_sum(1,2,3)) 
print(calculate_sum(1000, 300,44)) 
print(calculate_sum(12, 4,78)) 
 

 def function_name()
 

 

def calculate_triangle_area(base, height):
    area = (base * height)/2
    return area

print(calculate_triangle_area(12, 3))
 

 

def calculate_sum(a, b, c):
    return a+b+c

print(calculate_sum(1,2,3)) 
print(calculate_sum(1000, 300,44)) 
print(calculate_sum(12, 4,78)) 
 

 def calculate_sum(a, b, c):
    return a+b+c

print(calculate_sum(1,2)) 
 

 def calculate_sum(a, b, c=3):
    return a+b+c

print(calculate_sum(1,2)) 
 

MakeUseOf

http://img.youtube.com/vi/tZ99AEyHKm4/0.jpgThere’s a common myth or misconception that Laravel uses Tailwind by default. You still can use Bootstrap, let me show you how.Laravel News Links

https://cdn.hibit.dev/images/social/2023/preview/laravel_pagination.png

composer create-project --prefer-dist laravel/laravel hibit-pagination

php artisan vendor:publish --tag=laravel-pagination

Laravel News Links

https://s.w.org/images/core/emoji/14.0.0/72×72/1f680.png

Boolean indexing in Pandas filters DataFrame rows using conditions. Example: df[df['column'] > 5] returns rows where 'column' values exceed 5. Efficiently manage and manipulate data with this method.

Here’s an easy example:

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'San Francisco', 'Los Angeles', 'Seattle']}
df = pd.DataFrame(data)

# Perform boolean indexing to filter rows with age greater than 30
age_filter = df['Age'] > 30
filtered_df = df[age_filter]

# Display the filtered DataFrame
print(filtered_df)

This code creates a DataFrame with data for four people, then uses boolean indexing to filter out the rows with an age greater than 30. The filtered DataFrame is then printed.

Let’s dive slowly into Boolean Indexing in Pandas:

Understanding Boolean Indexing

Boolean indexing is a powerful feature in pandas that allows filtering and selecting data from DataFrames using a boolean vector. It’s particularly effective when applying complex filtering rules to large datasets .

To use boolean indexing, a DataFrame, along with a boolean index that matches the DataFrame’s index or columns, must be present.

To start, there are different ways to apply boolean indexing in pandas. One can access a DataFrame with a boolean index, apply a boolean mask, or filter data based on column or index values .

For instance, boolean indexing can filter entries in a dataset with specific criteria, such as data points above or below a certain threshold or specific ranges .

Working with boolean indexes is pretty straightforward. First, create a condition based on which data will be selected. This condition will generate a boolean array, which will then be used in conjunction with the pandas DataFrame to select only the desired data .

Here’s a table with examples of boolean indexing in pandas:

How Boolean Indexing Works in Pandas

Boolean indexing in Pandas is a technique used to filter data based on actual values in the DataFrame, rather than row/column labels or integer locations. This allows for a more intuitive and efficient way to select subsets of data based on specific conditions. Let’s dive into the steps on how boolean indexing works in Pandas:

Creating Boolean Arrays

Before applying boolean indexing, you first need to create a boolean array. This array contains True and False values corresponding to whether a specific condition is met in the DataFrame.

Consider the following example:

import pandas as pd

data = {'A': [1, 2, 3, 4],
        'B': [5, 6, 7, 8]}

df = pd.DataFrame(data)
bool_array = df['A'] > 2

In this example, we create a boolean array by checking which elements in column 'A' are greater than 2. The resulting boolean array would be:

[False, False, True, True]

Applying Boolean Arrays to DataFrames

Once you have a boolean array, you can use it to filter the DataFrame based on the conditions you set. To do so, simply pass the boolean array as an index to the DataFrame.

Let’s apply the boolean array we created in the previous step:

filtered_df = df[bool_array]

This will produce a new DataFrame containing only the rows where the condition was met, in this case, the row that had values greater than 2:

   A  B
2  3  7
3  4  8

To provide more examples, let’s consider the following table:

Filtering Data with Boolean Indexing

Boolean indexing is also a powerful technique to filter data in Pandas DataFrames based on the actual values of the data, rather than row or column labels . In this section, you’ll learn how to harness the power of boolean indexing to filter your data efficiently and effectively.

Selecting Rows Based on Condition

To select rows based on a condition, you can create a boolean mask by applying a logical condition to a column or dataframe. Then, use this mask to index your DataFrame and extract the rows that meet your condition . For example:

import pandas as pd

data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)

mask = df['A'] > 2
filtered_data = df[mask]

In this example, the mask is a boolean Series with True values for rows with A > 2, and filtered_data is the filtered DataFrame containing only the rows that meet the condition.

Combining Conditions with Logical Operators

For more complex filtering, you can combine multiple conditions using logical operators like & (AND), | (OR), and ~ (NOT). Just remember to use parentheses to separate your conditions:

Example:

mask2 = (df['A'] > 2) & (df['B'] < 8)
filtered_data2 = df[mask2]

This filters the data for rows where both A > 2 and B < 8.

Using Query Method for Complex Filtering

For even more complex filtering conditions, you can use the query method. This method allows you to write your conditions using column names, making it more readable and intuitive:

Example:

filtered_data3 = df.query('A > 2 and B < 8')

This achieves the same result as the masked2 example, but with a more readable syntax.

Pandas Boolean Indexing Multiple Conditions

Here is a table summarizing the examples of boolean indexing with multiple conditions in Pandas:

With these techniques at your disposal, you’ll be able to use boolean indexing effectively to filter your Pandas DataFrames, whether you’re working with simple or complex conditions .

Modifying Data Using Boolean Indexing

Boolean indexing is also great to modify data within a DataFrame or Series by specifying conditions that return a boolean array. These boolean arrays are then used to index the original DataFrame or Series, making it easy to modify selected rows or columns based on specific criteria.

In essence, it allows you to manipulate and clean data according to various conditions. It’s perfect for tasks like replacing missing or erroneous values, transforming data, or selecting specific data based on the criteria you set. This process is efficient and versatile, allowing for greater control when working with large datasets.

Now, let’s take a look at some examples of Boolean indexing in pandas to get a better understanding of how it works. The table below demonstrates various ways of modifying data using Boolean indexing:

These examples showcase some basic boolean indexing operations in pandas, but it’s worth noting that more complex operations can be achieved using boolean indexing too. The key takeaway is that this powerful technique can quickly and efficiently modify your data, making your data processing tasks simpler and more effective.

So, next time you’re working with data in pandas, don’t forget to employ this nifty technique to make your data wrangling tasks more manageable and efficient. Happy data cleaning!

Advanced Applications

Boolean indexing in Pandas has a wide range of advanced applications, allowing users to harness its power in complex scenarios. In this section, we will dive into a few of these applications, exploring their usefulness and demonstrating practical examples.

Using Indexers with Boolean Indexing

Combining indexers like iloc and loc with boolean indexing enhances the ability to select specific data subsets. Utilizing indexers in conjunction with boolean indexing allows you to specify both rows and columns, maintaining that sweet balance of performance and flexibility.

Handling Missing Data with Boolean Indexing

Dealing with missing data can be quite challenging. However, boolean indexing in Pandas comes to the rescue. With boolean indexing, users can quickly filter out missing data by applying boolean masks. This makes data cleaning and preprocessing a breeze. No more headaches navigating through messy data!

Pandas Boolean Indexing MultiIndex

MultiIndex, also known as a hierarchical index, adds another layer of depth to boolean indexing. By incorporating boolean indexing with MultiIndex DataFrames, you can access and manipulate data across multiple levels, enhancing your data exploration capabilities.

Here’s an example demonstrating the use of a MultiIndex in combination with boolean indexing in Pandas:

import pandas as pd

# Create a sample DataFrame with MultiIndex
index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)],
                                  names=['Category', 'Subcategory'])
data = {'Value': [10, 15, 20, 25]}
df = pd.DataFrame(data, index=index)

# Perform boolean indexing to filter rows where 'Category' is 'A' and 'Value' is greater than 12
category_filter = df.index.get_level_values('Category') == 'A'
value_filter = df['Value'] > 12
filtered_df = df[category_filter & value_filter]

# Display the filtered DataFrame
print(filtered_df)

This code creates a DataFrame with a MultiIndex consisting of two levels: 'Category' and 'Subcategory'. Then, it uses boolean indexing to filter the rows where the 'Category' is 'A' and the 'Value' column is greater than 12. The filtered DataFrame is then printed.

The output of the provided code is:

                   Value
Category Subcategory       
A        2             15

The filtered DataFrame contains only one row where the 'Category' is 'A', the 'Subcategory' is 2, and the 'Value' is 15, as this row meets both conditions specified in the boolean indexing.

Talk about leveling up your data analysis game!

Pandas Boolean Indexing DateTime

Time series data often requires efficient filtering and slicing. With boolean indexing applied to DateTime data, users can effortlessly filter their data based on specific date ranges, time periods, or even individual timestamps. You’ll never lose track of time with this powerful feature!

Examples of Boolean Indexing in Pandas

Below is a table showcasing a few examples of boolean indexing in action:

Now you have a better understanding of advanced applications with boolean indexing in Pandas! Happy data wrangling!

Pandas Boolean Indexing “OR”

In Pandas, Boolean indexing is a powerful way to filter and manipulate data using logical conditions . The “OR” operator, denoted by the symbol “|“, allows users to select rows that satisfy at least one of the specified conditions . In this section, let’s explore how the “OR” operator works with Boolean indexing in details, along with some examples .

With Pandas, users can combine multiple logical conditions using the “OR” operator by simply providing them with a “|“. This can be especially useful when working on complex data filtering tasks . Normally, the conditions are enclosed in parentheses to maintain order and group them correctly. Just remember to use the proper Boolean operator carefully!

For a better understanding, let’s take a look at the following example on how the “OR” operator works with Boolean indexing in Pandas:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}
df = pd.DataFrame(data)

# Boolean indexing using "OR" operator
result = df[(df['A'] > 3) | (df['B'] <= 7)]

In this example, we have a DataFrame with two columns ‘A’ and ‘B’, and the goal is to filter rows where the value of ‘A’ is greater than 3 or the value of ‘B’ is less than or equal to 7. The resulting DataFrame will include rows that meet either condition .

Pandas Boolean Indexing “NOT”

Pandas boolean indexing is a powerful tool used for selecting subsets of data based on the actual values of the data in a DataFrame, which can make filtering data more intuitive . In this section, we’ll focus on the “NOT” operation and its usage in pandas boolean indexing.

The “NOT” operation is primarily used to reverse the selection made by the given condition, meaning if the condition is initially true, it will turn false, and vice versa. In pandas, the “not” operation can be performed using the tilde operator (~) . It can be particularly helpful when filtering the data that does not meet specific criteria.

Let’s consider some examples to understand better how “NOT” operation works in pandas boolean indexing:

In these examples, the tilde operator (~) is utilized to perform the “NOT” operation, which helps to refine the selection criteria to better suit our needs. We can also combine the “NOT” operation with other boolean indexing operations like “AND” (&) and “OR” (|) to create more complex filtering conditions .

Remember, when working with pandas boolean indexing, it’s essential to use parentheses to group conditions properly, as it ensures the correct precedence of operations and avoids ambiguity when combining them .

Boolean indexing in pandas provides an efficient and easy way to filter your data based on specific conditions, and mastering the different operations, such as “NOT”, allows you to craft precise and powerful selections in your DataFrames .

Pandas Boolean Indexing in List

Pandas Boolean indexing is a powerful technique that allows you to select subsets of data in a DataFrame based on actual values rather than row or column labels . This technique is perfect for filtering data based on specific conditions .

When using Boolean indexing, you can apply logical conditions using comparison operators or combination operators like & (and) and | (or) . Keep in mind that when applying multiple conditions, you must wrap each condition in parentheses for proper evaluation .

Let’s go through a few examples to better understand how Boolean indexing with lists works!

Remember, when working with Pandas Boolean indexing, don’t forget to import the pandas library, use proper syntax, and keep practicing ! This way, you’ll be a Boolean indexing pro in no time !

Pandas Boolean Indexing Columns

Boolean indexing in pandas refers to the process of selecting subsets of data based on their actual values rather than row or column labels or integer locations. It utilizes a boolean vector as a filter for the data in a DataFrame . This powerful technique enables users to easily access specific data pieces based on conditions while performing data analysis tasks .

In pandas, boolean indexing commonly employs logical operators such as AND (&), OR (|), and NOT (~) to create a boolean mask which can be used to filter the DataFrame. The process usually involves creating these logical expressions by applying conditions to one or more columns, and then applying the boolean mask to the DataFrame to achieve the desired subset .

Here’s a table showing some examples of boolean indexing with pandas:

Example	Description
df[df['A'] > 2]	Filter DataFrame where values in column A are greater than 2 .
df[(df['A'] > 2) & (df['B'] < 5)]	Select rows where column A values are greater than 2, and column B values are less than 5 .
df[df['C'].isin([1, 3, 5])]	Filter DataFrame where column C contains any of the values 1, 3, or 5 .
df[~df['D'].str.contains('abc')]	Select rows where column D doesn’t contain the substring ‘abc’ .

Boolean indexing is an essential tool for data manipulation in pandas, offering a versatile solution to filter and identify specific elements within the data. Harnessing the power of boolean indexing can greatly improve the efficiency of data analysis tasks, making it a valuable skill to master for users working with pandas data structures .

Pandas Boolean Indexing Set Value

In Pandas, Boolean indexing is a powerful feature that allows users to filter data based on the actual values in a DataFrame , instead of relying on their row or column labels. This technique uses a Boolean vector (True or False values) to filter out and select specific data points in a DataFrame . Let’s dive into how it works!

Using logical operators such as AND (&), OR (|), and NOT (~), Pandas makes it easy to combine multiple conditions while filtering data. Below is a table showcasing some examples of how to use Boolean indexing in Pandas to set values with different conditions:

When working with Pandas, Boolean indexing can tremendously simplify the process of filtering and modifying datasets for specific tasks . Remember that the possibilities are virtually endless, and you can always combine conditional statements to manipulate your datasets in numerous ways!

Pandas Boolean Indexing Not Working

Sometimes when working with Pandas, you may encounter issues with Boolean indexing. There are a few common scenarios that can lead to Boolean indexing not functioning as expected. Let’s go through these cases and their possible solutions.

One common issue arises when using Boolean Series as an indexer. This may lead to an IndexingError: Unalignable boolean Series provided as indexer error. This usually occurs when the Boolean mask cannot be aligned on the index, which is used by default when trying to filter a DataFrame (source).

To overcome this problem, ensure that your Boolean Series index aligns with your DataFrame index. You can use the `.loc` method with the same index as the DataFrame to make sure the Series is alignable:

df[df.notnull().any(axis=0).loc[df.columns]]

Another issue that may arise is confusion with logical operators during the Boolean indexing process. In Pandas, logical operators for Boolean indexing are different from standard Python logical operators. You should use & for logical AND, | for logical OR, and ~ for logical NOT (source).

For example, to filter rows based on two conditions:

df[(df['col1'] == x) & (df['col2'] == y)]

Here is a table with some examples of Boolean indexing in Pandas:

By understanding these potential pitfalls, you can ensure smoother Boolean indexing in your Pandas projects. Good luck, and happy data wrangling!

Be on the Right Side of Change