#max function in pandas

[spreadsheetautomation]

What is The Median Function Used For in Pandas?

Within Python, the programming language, libraries can be used to extend the functionality of code. One popular library is Pandas, stylized as Pandas. This library allows data scientists to manipulate and transform data in a variety of ways, but certain functions can be used to filter and sort data as well.

The MEDIAN function in pandas is an example of a function that does just that. Using the MEDIAN function in pandas, you’re able to call out the median number in a series or dataframe. A dataframe is like a spreadsheet within Python that contains an X-axis and a Y-axis.

Data from this table can be difficult to sort by sight, particularly when working with a large volume of data. Sorting via the MEDIAN function helps data scientists quickly ascertain the median number from a large group of number data.

How Can the MEDIAN Function Be Used?

You can use the MEDIAN function for a variety of things. For instance, if you were concerned about anomalies in your data, you could use the MEDIAN function to establish the median. This result will be most likely to represent a consistent trend among all the number data in a series or dataframe. With the median established in a dataset, you can establish thresholds to more easily identify anomalies in the future.

You can also use the MEDIAN function to determine the median cost of things like homes. A Realtor could work with a data scientist to establish the median home price in a particular area when working with tabular data. Realtors often gather data from their own internal sales as well as figures reported to national associations and public information provided to local governments.

This data could be imported from Excel or another spreadsheet program. Traditionally, the Realtor would need to go through each line by hand to determine the median value within the data, but the MEDIAN function can return this value instantly. Armed with this information, the Realtor can more accurately represent properties and guide buyers and sellers to the right opportunities.

Read a similar article about Excel functions in Python here at this page.

[dezemberzwolf]

what the fuck is up i have some thoughts about hesperos and how much of him was/wasnt corrupted by hephaistos and how it would not influence his true form very much imo that bsky's character limit would not be able to handle. so here we go. stream of consciousness it is.

so ime the thing that makes hesperos stand out from the other keywardens is the fact that the vrykolakas is really well integrated into him, to the point that it's even pointed out (by themis iirc?? i forger). the protag gang worries that the other keywardens will also have their creations very well integrated but like. they do not. theyre super fucked up and slipshod. agdistis got entirely absorbed by hers and can barely keep a focused thought, while hegemone has a parasite grafted onto her that is so easy to affix, she can spread it to the wol at will.

to me this is caused by two things. the first being hesperos being the first, or at least the most delicate of experimentation that hephaistos did (and is that too not a type of yaoi. is it not a kind of gay sex to very carefully tinker about in your coworker's soul. anyways,) and he got either a lot of time or a lot of attention. or both. the second being, hesperos is super keen on leaning into his role as not only a hemitheos but also a vampire. the man is pumped to be a vampire, while adgistis seems unaware that she's a tree now and hegemone makes nothing of being snakey. this is to say: hesperos's status as a hemitheos was something he either had a hand in choosing for himself and/or actively leaned into once it was chosen.

b/c of this, there's less of a 'corruption' or 'changing' his desires per se, and just taking the things he does want (lahabrea's attention, to be a cool fun vampire, to put on a play) and maxing them out to 11. he has nuked all ability for moderation or self restraint, since auracite's function is to amplify desires. none of thise things were alien to him before, and he does still retain some things from his implied personality as a keywarden that the journal points out (that hesperos was a careful caretaker of subjects, hes still the one organizing environments for those subjects to be comfortable and powerful in while they kill you, and he says a few times he's going to feed you to them. like that is a way of looking out for them, eh?) and his admiration(love) for lahabrea.

there is also very heavily implied that his flamboyance is... not new whatsoever. erich doesn't comment on it at all unlike how he points out the other personality difference, and hesperos's fight mechanics only somewhat lean into the vampirism theme at the beginning: he is super duper intent on making you actors in a play. the actual alteration that he was given as a hemitheos is vampirism and just making his desires Impossible To Tone Down. and these desires are He Wants To Fuck His Boss and He Wants To Put On A Play. pre-pinax is 'setting the stage', his bloodrake (which is ostensibly vampirism themed) gives you 'role call' and 'miscast' (which i read as miscast as in poorly cast for the role, not mis-cast as in fucked up casting the incantation), one of his weapon casts is 'director's belone' and the entire phase 2 is A Play that he states he'd prepared (presumably for his own artistic efforts before pandae became a raid series, meaning this is a hobby for him).

so what im saying is essentially that hesperos is super lucid compared to the other wardens, and making pretty logical decisions from his standpoint, it's just that his standpoint is that he is physically incapable of ignoring or toning down his own desires. sorry, erich, but in terms of his desires you have to be expendable. (its my personal hc that hesperos was just dismissive and an ass to erich because he was intent on hurting him and getting him to leave, while sane hesperos would not be quite so harsh. obv.)

what that means for his true form is that genuinely i do not think his pre-hemitheos form would be very different from his auracite true form. there's really absolutely nothing in the phase 2 fight that alludes to vampirism, which is the center of his transformation and that means that any other changes would simply be from the auracite's amplification of desire. meaning, before raids his true form if it underwent any changes would just have been more toned down. quite possibly with his face more hidden like many of the other ancients or like, not with his tits out and the fuckin. line of sight design that goes from face to tits to dick. but he prolly would have also been just as bird themed and probably quite humanoid in face and body layout. just like. maybe hes also got a shirt on LMAO since in terms of "corruption" it was less "make your awareness and desires go sideways or all fucked up and you get One Fixation" like most everyone else it was just "VERY LOUD EVERYTHING YOU WANT IS SO LOUD YOU WANTED IT BEFORE NOW ITS JUST SOOOOOOOO LOUD"

idk how to end this. closign sentence. what were those years of college for when i still dont know a closing paragraph.

[nikeshoes2025]

Nike sneakers: an eternal legend of technology and trend from racetracks to streets

Whether you are a professional athlete or a trend enthusiast, Nike sneakers have always been an indispensable presence in the shoe cabinet. From disruptive air cushion technology to deep integration with street culture, Nike has spent over half a century creating pairs of sneakers as symbols that combine functionality and aesthetics. This blog will take you to explore Nike's classic shoes, technological innovations, and how it has become a global cultural phenomenon.Jordan shoes Online sale

1、 Classic Forever: A Milestone of Nike Sneakers

Every classic pair of Nike shoes carries the memories of the times and technological breakthroughs:

Air Force 1（1982）Nike shoes online sale

As the first basketball shoe equipped with air cushion technology, Air Force 1 has become popular on the streets with its pure white color scheme and minimalist design, becoming a symbol of hip-hop culture. Until now, it is still the "white shoe ceiling" in the hearts of trendy players.Nike Air Max shoes Sale

Air Max series (1987 present)

From the "window cushion" of Air Max 1 to the full-length visual cushion of Air Max 270, this series defines the aesthetics of running shoes with a sense of technology design, and even gives rise to the annual celebration of "Air Max Day".Nike Jordan shoes sale

Dunk SB（2002）

The Dunk SB, optimized specifically for skateboarding, has become a representative of millennial street culture with its thick tongue, Zoom Air cushioning, and collaborations such as "Panda Pigeon" and "Purple Lobster".Nike Shoes Online Shop

[sak-shi]

The Foundation of Clean Data Analysis

Clean data is the cornerstone of accurate and reliable data analysis. A robust foundation in data cleaning ensures that your insights are valid, consistent, and actionable. Here’s an outline of the foundational steps for clean data analysis:

1. Understanding the Dataset

Familiarize Yourself with the Data Understand the context of the dataset, including its purpose, variables, and expected outcomes. Tools: Data dictionaries, documentation, or domain expertise.

Inspect the Data Use methods like .head(), .info(), and .describe() in Python to gain an overview of the dataset's structure and summary statistics.

2. Identifying and Handling Missing Data

Locate Missing Data Identify missing values using functions like .isnull().sum() in Python. Visualization: Heatmaps (e.g., Seaborn) can highlight missing data patterns.

Strategies to Handle Missing Data

Removal: Drop rows or columns with excessive missing values.

Imputation: Fill missing values using statistical methods (mean, median, mode) or predictive models.

3. Addressing Outliers

Detect Outliers Use visualizations like boxplots or statistical methods like Z-scores and IQR to identify outliers.

Handle Outliers Options include capping, transformation, or removal, depending on the context of the analysis.

4. Resolving Inconsistencies

Standardize Data Formats Ensure consistency in formats (e.g., date formats, text capitalization, units of measurement). Example: Converting all text to lowercase using .str.lower() in Python.

Validate Entries Check for and correct invalid entries like negative ages or typos in categorical data.

5. Dealing with Duplicates

Detect Duplicates Use methods like .duplicated() to identify duplicate rows.

Handle Duplicates Drop duplicates unless they are meaningful for the analysis.

6. Ensuring Correct Data Types

Verify Data Types Check that variables have appropriate types (e.g., integers for counts, strings for categories).

Convert Data Types Use type-casting functions like astype() in Python to fix mismatched data types.

7. Data Transformation

Feature Scaling Apply normalization or standardization for numerical features used in machine learning. Techniques: Min-Max scaling or Z-score normalization.

Encoding Categorical Variables Use one-hot encoding or label encoding for categorical features.

Key Tools for Data Cleaning:

Libraries: Pandas, NumPy, Seaborn, Matplotlib (for Python users).

Software: Excel, OpenRefine, or specialized ETL tools like Talend or Alteryx.

By mastering these foundational steps, you’ll ensure your data is clean, consistent, and ready for exploration and analysis. Would you like more detailed guidance or code examples for any of these steps?

[workforcesolution]

Battle of the Programming Languages: R vs Python

Battle of the Programming Languages: R vs Python

We are going to compare Functions exist in both R and Python for same operations. And for this we took the Titanic dataset which contains the Passenger details. Importing a CSV Reading Data in both the languages is similar, but the only difference is for python we have to import pandas library for reading the Data. Once the importing is done we can look into the data by applying the below functions.RPythontitanicimport pandas as pd

titanic = pd.read_csv("train.csv")

Dimension and Shape If we want to look the Dimension of the above imported Data. You can get it from the below functions.RPythondim(titanic)titanic.shape[1] 891 12(891, 12)

The above code brings you the number of passengers in titanic ship and the number of columns present in data. Head and Tail If you want to see some of the data like top rows (Any number of rows by default it gets 5 rows) or bottom rows form the Data frame. There are functions in similar functions in both R and Python.RPythonhead(titanic,2)titanic.head(2)PassengerId Survived Pclass

1 1 0 3

2 2 1 1

 PassengerId Survived Pclass 0 1 0 3 1 2 1 1tail(titanic,2)titanic.tail(2) PassengerId Survived Pclass

890 890 1 1

891 891 0 3

PassengerId Survived Pclass 889 890 1 1

890 891 0 3

Here head and tail functions applied on Titanic dataset to look at the first two rows of Data. If you observe clearly the index values are different in both R and Python. It is because Python index starts with '0'. Basic Statistics of Data (Summary and Describe)RPythonsummary(titanic)titanic.describe()PassengerId SurvivedPassengerId SurvivedMin. : 1.0 Min. :0.0000

1st Qu.:223.5 1st Qu.:0.0000

Median :446.0 Median :0.0000

Mean :446.0 Mean :0.3838

3rd Qu.:668.5 3rd Qu.:1.0000

Max. : 891.0 Max. :1.0000count 891.000000 891.000000

mean 446.000000 0.383838

std 257.353842 0.486592

min 1.000000 0.000000

25% 223.500000 0.000000

50% 446.000000 0.000000

75% 668.500000 1.000000

max 891.000000 1.000000

The above two functions are for determining some basic statistics column wise. Whereas python gives two more statistic values compared to R function. The main difference between these functions is R contains Separate functions and for Python we have to call the required methods on the Data as it is more of object oriented type programming. Slicing the Datatitanic[1:5,1:3]titanic.iloc[0:5,0:3]PassengerId Survived Pclass

1 1 0 3

2 2 1 1

3 3 1 3

4 4 1 1

5 5 0 3PassengerId Survived Pclass

0 1 0 3

1 2 1 1

2 3 1 3

3 4 1 1

4 5 0 3

Sub setting Data Here in this case for sub setting the Data I took only some columns from the titanic Dataset. For the convenience of displaying the output. Using sam_data sam_data = titanic[['PassengerId', 'Survived','Sex','Age']] for Python I created sam_data for applying subset function. subset(sam_data,Survived == 1& Sex == 'male')sam_data[(sam_data.Sex == 'male') & (sam_data.Survived ==1)].head(2)PassengerId Survived Sex Age

18 18 1 male NA 22 22 1 male 34 24 24 1 male 28 37 37 1 male NA 56 56 1 male NA 66 66 1 male NAPassengerId Survived Sex Age

17 18 1 male NaN 21 22 1 male 34.00 23 24 1 male 28.00 36 37 1 male NaN 55 56 1 male NaN 65 66 1 male NaN

The important thing here is the representation of NA in Python is NaN. Ordering the Data We ordered the Sample Dataset By arrange(sam_data, Survived, desc(Age))sam_data.sort_index(by=['Survived', 'Age'], ascending=[True, False])PassengerId Survived Sex Age 1 852 0 male 74.0 2 97 0 male 71.0 3 494 0 male 71.0 4 117 0 male 70.5 5 673 0 male 70.0 6 746 0 male 70.0PassengerId Survived Sex Age 851 852 0 male 74.0 493 494 0 male 71.0 96 97 0 male 71.0 116 117 0 male 70.5 672 673 0 male 70.0 745 746 0 male 70.0

Joins For Performing join operations we created three different data frames from the titanic Dataset df_Survived df_Sex df_Age df_Survived = sam_data[['PassengerId', 'Survived']] df_Sex = sam_data [['PassengerId', 'Sex']] df_Age = sam_data [[ 'PassengerId', 'Age']] Inner

JoinTable_Inner_J = merge ( merge(df_Survived, df_Sex, key = "PassengerId" ),

df_Age ,

key = "PassengerId")Table_Inner_J = pd.merge ( pd.merge(df_Survived, df_Sex, on = "PassengerId" , how = "inner" ),

df_Age ,

on = "PassengerId" , how = "inner")Outer JoinTable_Outer_J = merge ( merge(df_Survived, df_Sex, key = "PassengerId" , all =TRUE),

df_Age ,

key = "PassengerId", all = TRUE)Table_Outer_J = pd.merge ( pd.merge(df_Survived, df_Sex, on = "PassengerId" , how = "outer"),

df_Age ,

on = "PassengerId", how = "outer")Left

JoinTable_Left_J = merge ( merge(df_Survived, df_Sex, key = "PassengerId" , all.x =TRUE),

df_Age ,

key = "PassengerId", all.x = TRUE)Table_Left_J = pd.merge ( pd.merge(df_Survived, df_Sex, on = "PassengerId" , how = "left"),

df_Age ,

on = "PassengerId" , how = "left")Right

JoinTable_Right_J = merge ( merge(df_Survived, df_Sex, key = "PassengerId", all.y = TRUE),

df_Age ,

key = "PassengerId", all.y = TRUE )Table_Right_J = pd.merge ( pd.merge(df_Survived, df_Sex, on = "PassengerId" , how = "right"),

df_Age ,

on = "PassengerId" , how = "right")

The major Difference in R and Python for joining operation is both can be done using merge function. But for python we have to import pandas library for using the merge function to perform these join functions. We can join three Data frames at a time by applying merge function two times. Missing Values Treatment: In Missing Values treatment first thing we have to do is identify the NA values by running the first block of code in below table. After getting the variables where missing values are there then you can impute them with the mean value of that respective column. Here, second block of code replaces the NA values with the respective mean values. tail(is.na(sam_data))sam_data.isnull().tail()PassengerId Survived Sex Age [886,] FALSE FALSE FALSE FALSE [887,] FALSE FALSE FALSE FALSE [888,] FALSE FALSE FALSE FALSE [889,] FALSE FALSE FALSE TRUE [890,] FALSE FALSE FALSE FALSE [891,] FALSE FALSE FALSE FALSEPassengerId Survived Sex Age 886 False False False False 887 False False False False 888 False False False False 889 False False False True 890 False False False Falsesam_data["Age"][is.na(sam_data["Age"])]meanAge = np.mean(sam_data.Age)

sam_data.Age = sam_data.Age.fillna(meanAge)PassengerId Survived Sex Age

[886,] FALSE FALSE FALSE FALSE

[887,] FALSE FALSE FALSE FALSE

[888,] FALSE FALSE FALSE FALSE

[889,] FALSE FALSE FALSE FALSE

[890,] FALSE FALSE FALSE FALSE

[891,] FALSE FALSE FALSE FALSEPassengerId Survived Sex Age

886 False False False False

887 False False False False

888 False False False False

889 False False False False

890 False False False False

About Rang Technologies: Headquartered in New Jersey, Rang Technologies has dedicated over a decade delivering innovative solutions and best talent to help businesses get the most out of the latest technologies in their digital transformation journey. Read More...

[edcater]

Demystifying Data Science: Essential Concepts for Beginners

In today's data-driven world, the field of data science stands out as a beacon of opportunity. With Python programming as its cornerstone, data science opens doors to insights, predictions, and solutions across countless industries. If you're a beginner looking to dive into this exciting realm, fear not! This article will serve as your guide, breaking down essential concepts in a straightforward manner.

1. Introduction to Data Science

Data science is the art of extracting meaningful insights and knowledge from data. It combines aspects of statistics, computer science, and domain expertise to analyze complex data sets.

2. Why Python?

Python has emerged as the go-to language for data science, and for good reasons. It boasts simplicity, readability, and a vast array of libraries tailored for data manipulation, analysis, and visualization.

3. Setting Up Your Python Environment

Before we dive into coding, let's ensure your Python environment is set up. You'll need to install Python and a few key libraries such as Pandas, NumPy, and Matplotlib. These libraries will be your companions throughout your data science journey.

4. Understanding Data Types

In Python, everything is an object with a type. Common data types include integers, floats (decimal numbers), strings (text), booleans (True/False), and more. Understanding these types is crucial for data manipulation.

5. Data Structures in Python

Python offers versatile data structures like lists, dictionaries, tuples, and sets. These structures allow you to organize and work with data efficiently. For instance, lists are sequences of elements, while dictionaries are key-value pairs.

6. Introduction to Pandas

Pandas is a powerhouse library for data manipulation. It introduces two main data structures: Series (1-dimensional labeled array) and DataFrame (2-dimensional labeled data structure). These structures make it easy to clean, transform, and analyze data.

7. Data Cleaning and Preprocessing

Before diving into analysis, you'll often need to clean messy data. This involves handling missing values, removing duplicates, and standardizing formats. Pandas provides functions like dropna(), fillna(), and replace() for these tasks.

8. Basic Data Analysis with Pandas

Now that your data is clean, let's analyze it! Pandas offers a plethora of functions for descriptive statistics, such as mean(), median(), min(), and max(). You can also group data using groupby() and create pivot tables for deeper insights.

9. Data Visualization with Matplotlib

They say a picture is worth a thousand words, and in data science, visualization is key. Matplotlib, a popular plotting library, allows you to create various charts, histograms, scatter plots, and more. Visualizing data helps in understanding trends and patterns.

Conclusion

Congratulations! You've embarked on your data science journey with Python as your trusty companion. This article has laid the groundwork, introducing you to essential concepts and tools. Remember, practice makes perfect. As you explore further, you'll uncover the vast possibilities data science offers—from predicting trends to making informed decisions. So, grab your Python interpreter and start exploring the world of data!

In the realm of data science, Python programming serves as the key to unlocking insights from vast amounts of information. This article aims to demystify the field, providing beginners with a solid foundation to begin their journey into the exciting world of data science.

[subashdhoni86]

A Beginner's Guide to Data Preprocessing in Machine Learning: Cleaning and Preparing Data for Analysis

Introduction:

Data is the fuel that powers the world of machine learning, but it's rarely in perfect shape when we first get our hands on it. Raw data is often messy, containing missing values, outliers, and inconsistencies that can negatively impact the performance of machine learning models. That's where data preprocessing comes in – a crucial step in the machine learning pipeline that involves cleaning and preparing the data to ensure it's in a suitable format for analysis. In this blog, we'll walk through the basics of data preprocessing and introduce some popular libraries to help you get started on your machine learning journey.

1. Importing the Necessary Libraries:

Before diving into data preprocessing, let's make sure we have the right tools at our disposal. We'll need to import the following libraries in Python:

- Pandas: For data manipulation and analysis.

- NumPy: For numerical operations and array processing.

- Matplotlib and Seaborn: For data visualization.

- Scikit-learn: For machine learning algorithms and additional preprocessing functions.

You can install these libraries using pip by running the following commands:

pip install pandas numpy matplotlib seaborn scikit-learn

2. Understanding the Data:

The first step in data preprocessing is to gain an understanding of the data you're working with. Look for the following aspects:

- The dimensions of the dataset (rows and columns).

- The types of features present (numerical, categorical, text, etc.).

- The presence of any missing values.

- Distribution of the target variable (for supervised learning tasks).

3. Handling Missing Data:

Missing data is a common issue in datasets and can lead to biased results if not handled properly. There are several approaches to deal with missing values:

- **Removal**: Remove rows or columns with missing values. However, this should be done with caution as it may result in a loss of valuable information.

- **Imputation**: Fill in missing values using various techniques such as mean, median, mode, or advanced imputation methods like K-nearest neighbors.

We can use Pandas to perform these operations:

import pandas as pd

# Load the dataset

data = pd.read_csv('your_dataset.csv')

# Check for missing values

print(data.isnull().sum())

# Impute missing values with mean

data.fillna(data.mean(), inplace=True)

4. Handling Categorical Data:

Machine learning models typically work with numerical data, so we need to convert categorical data into numerical form. One common technique is one-hot encoding, where each category becomes a binary column.

# One-hot encoding using pandas

data = pd.get_dummies(data, columns=['categorical_column'])

5. Feature Scaling:

Feature scaling ensures that all numerical features are on a similar scale, preventing certain features from dominating others during model training. Two popular scaling techniques are Min-Max scaling and Standardization.

from sklearn.preprocessing import MinMaxScaler, StandardScaler

# Min-Max Scaling

scaler = MinMaxScaler()

data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])

# Standardization

scaler = StandardScaler()

data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])

6. Handling Outliers:

Outliers can significantly impact model performance. You can visualize them using box plots and handle them using various techniques like truncation or capping.

import seaborn as sns

# Box plot to identify outliers

sns.boxplot(data=data[['feature1', 'feature2']])

7. Splitting the Data:

Before training the model, we need to split the data into training and testing sets. This allows us to evaluate the model's performance on unseen data.

from sklearn.model_selection import train_test_split

X = data.drop('target', axis=1)

y = data['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Conclusion:

Data preprocessing is a critical step in the machine learning workflow, as it ensures that the data is cleaned and transformed into a suitable format for model training. In this blog, we covered the basics of data preprocessing, including handling missing data, categorical data, feature scaling, and outliers. By using libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, you can effectively preprocess your data and set the foundation for building powerful machine learning models.

Remember that data preprocessing is not a one-size-fits-all process. Different datasets may require different preprocessing techniques, so always be prepared to explore and adapt your approach accordingly. Happy learning and good luck on your machine learning journey!

@TalentServe

#DataPreprocessing #MachineLearning #Cleaning #Analysis

[aspiringauthorintraining]

Demonstration

“Friends, students…” you call to their attention, “-and Sukuna.“ you added a moment later. “Today, I lost my dear friend Gojo Satoru.”

“Quit telling everyone I’m dead, (Y/N).”

Ignoring the voice coming from behind, you adorned a frown on your face. “Sometimes, I can still hear his voice.”

“I’m not dead!”

“He was many things: a teacher, a friend, a confidant. Now, was he good at these things? The answer is no.”

“Hey!”

“But we must not speak ill of those who are not with us anymore.” you continued.

Most of the Tokyo students nod their heads in agreement, while some of the Kyoto students kept glancing from you to a figure behind.

“It’s as if he’s in the room with us, right now.” You bring a hand to your mouth to prevent a sob (laugh) from escaping, at the sound of Gojo’s whines. “But as you all may know, curses like to act like those who we miss, so we mustn’t be tricked. Therefore, if for whatever reason you happen to see something that resembles Gojo Satoru, please exorcise it with your fullest ability, so that we may all one day move on and allow time to heal our hearts.”

One of the Kyoto students, Miwa Kasumi, blinked in confusion, unable to understand what was going on. You were standing in front of everyone participating in the sister school exchange event, mourning over Gojo Satoru’s death- while the said jujutsu sorcerer was standing behind you, tugging on your arms to get you to look in his direction.

“Am I the only one who sees Gojo-sensei alive?” she whispered to a nearby Tokyo student sitting next to her, who happened to be Maki Zenin.

Maki shook her head. “If (Y/N)-sensei says that the idiot is dead, then he is.”

“Tuna-mayo.” Inumaki agreed, nodding his head.

“Best if you just go with it.” Panda whispered back to the still confused girl. 

It seemed as though everyone else from the Kyoto branch had no complaints, accepting your words as the truth with a nod of their heads.

“(Y/N)!!” Gojo whined, placing his chin on your shoulder. “I said I was sorry.”

Your patience with the tall man-child had finally reached its max.

“Sorry?!” You flicked his forehead off of you, and pushed his face away. “I leave for one week, and you basically bulldoze half the campus forest to prevent a special-grade curse from running away, who might I add, was able to run away again! And then, I find out you were the one who ate my food that I had been saving in the freezer.”

Gojo merely shrugged at your accusations. “I was on a time crunch with the first one. And as for the second thing, it’s not my fault someone should’ve wrote their name on their mochi ice cream if they didn’t want someone else eating it.”

“I did write my name on the box, and I know you fucking ate it!” 

“There in lies your mistake, sweetheart. You should’ve written your name on the mochi itself. How was I supposed to know it was yours once I threw away the box?”

“Because my name was on the box, you motherfu-“

“Shouldn’t we trying to stop them from killing each other?” Yuuji whispered to Megumi, who was watching their teachers bicker with a straight face.

“Yaga-sensei will eventually stop them.” Megumi replied nonchalantly, as the boys overheard you screaming at Gojo.

“I told you the last time you ate my stuff, you would be dead to me!”

“You can’t just kill me off, (Y/N).”

“We’ll never know unless we try, now will we?” you argued.

And just as Megumi had said, Principle Yaga grabbed Gojo by his collar to prevent you from choking the man to death.

“Alright, that’s enough you two.” Yaga ordered, making you weaken the strangling grip you held on Gojo’s neck. “We should go ahead and start the baseball game.”

The hostility on your face quickly morphed into a smile, at your sensei’s words.

“Sensei, don’t you think we should give the students a demonstration beforehand?”

“Is that really necessary?” Gojo complained, massaging his neck in pain.

“Of course it is! After all, you were the one who suggested the game, you should be the one to show them how to properly hit a ball.”

Yaga just sighed, nodding his head to your suggestion.

“Just try not to kill him please, (Y/N).”

Your smile grew wider, causing a shiver to run down Gojo’s back. 

“Can’t make any promises, sensei.”

_________

Yuuji looked out from the sidelines onto the baseball field, watching Gojo stare appreciatively at the sight of you stretching on the pitcher’s mound.

“Don’t we all know how to play baseball?” he wondered aloud.

“Did you just figure that out, dummy?” Nobara retorted.

“Then why the need for the demonstration?” 

“Because we all want to see Gojo-sensei get pummeled by (Y/N)-sensei.” Maki responded.

“But isn’t he just going to use his technique?”

“Her technique basically cancels his out.”

“Huh? How?” 

“Gojo-sensei’s techniques deal with infinite time, and (Y/N)-sensei’s technique deals with manipulating time.”

“But no matter how fast you speed time up, you can never reach infinity, right?” Yuuji tried to reason, thinking back on the lectures he slept through in his math classes.

“In that case, yes.” Panda answered. “Infinity requires time to function. But if time were to stop, infinity would be cancelled out, because having an infinite number of nothing would still leave you with nothing.” 

“Not to mention (Y/N)-sensei has a wicked throw.” Maki added.

And shortly, a few moments later, a cry of pain was heard from the batter’s box.

__________________

Gojo pouted as you returned to him with a bag of ice, placing it gently on his cheek.

“Ow!” he whined from the sudden coldness. “You could’ve gone a little softer with that pitch, sweetheart.” 

“Please, you deserved it.” you said, rolling your eyes.

But seeing the light bruise formed on the side of his face, you couldn’t help but feel slightly guilty. 

Lifting the ice bag momentarily, you placed a soft kiss on his bruised cheek, immediately silencing him. 

“Does it feel better now?” you asked.

He nodded quickly. “But, you missed a spot. I got hit here too.” he said, pointing to his puckered lips.

You rolled your eyes, seeing right through his play; but gave in to his request, nonetheless. As you motioned to step back from the kiss, Gojo snaked his arms around your waist, bringing your body flush against his with your lips still attached to his.

He finally let you breathe a long minute later, after you hit his chest repeatedly for air.

“Now I feel a bit better.” he sighed, contentedly, before adding, “But I do think I’m going to need more intensive care tonight in my room.”

“Hmmm, is that so.” you thought, placing a hand on his other, non bruised cheek with a smile. 

Gojo burrowed his face into your palm, with a playful smirk. 

A quiet second later, right when he least expected it, you lightly slapped his cheek, quickly squirming out of his grasp. You dodged his grabby hands, a laugh escaping your lips when a pout made its way back onto his face. 

“I’ll be sure to ask Yaga-sensei to stop by your room tonight then.” you smiled, furthering the distance between you two.

“No, (Y/N)!” Gojo whined, quickly chasing after you.

_____

*(A/N): i’m in a love/hate relationship with gojo satoru  ¯\_(ツ)_/¯

[aion-rsa]

New on Netflix: July 2021 Releases

https://ift.tt/eA8V8J

By the power of Grayskull, Netflix’s list of new releases for July 2021 is here!

As you may have been able to tell by that clever opening, July is the month that Masters of the Universe: Revelation arrives on Netflix. This animated series from Kevin Smith continues the classic stories of He-Man and his buff friends. If nostalgia not be what ye seek, Netflix has plenty other original series this month as well. The amazingly hilarious sketch series I Think You Should Leave With Tim Robinson returns for season 2 on July 6. Also returning for a second season are Beastars, Never Have I Ever (both on July 15), and Outer Banks (July 30).

Netflix’s movie offerings are pretty thick this month since July marks the real beginning of the summer blockbuster season. The streamer is bringing not one, but three Fear Street films based on R.L. Stine’s classic book series. They arrive on July 1, July 9, and July 16 respectively. Also of note are Gundpowder Milkshake (July 14), Trollhunters: Rise of the Titans (July 21), and The Last Letter From Your Lover (July 23).

And if that weren’t enough, July sees a big influx of TV properties on Netflix. The Walking Dead season 10 (July 26), Wynonna Earp season 4 (July 26), and The Flash season 7 (July 28) all arrive at month’s end. These library titles will be complemented by The Twilight Saga (July 16) and the usual bevy of July 1 releases.

New on Netflix: July 2021

Coming Soon Cheech & Chong’s Still Smokin Feels Like Ishq — NETFLIX SERIES  How to Sell Drugs Online (Fast): Season 3 — NETFLIX SERIES 

July 1 Audible — NETFLIX DOCUMENTARY Dynasty Warriors — NETFLIX FILM  Generation 56k — NETFLIX SERIES  Mobile Suit Gundam Hathaway — NETFLIX ANIME FILM  Young Royals — NETFLIX SERIES  Air Force One Austin Powers in Goldmember Austin Powers: International Man of Mystery Austin Powers: The Spy Who Shagged Me The Best of Enemies Boogie Nights Born to Play Bureau of Magical Things: Season 1 Charlie’s Angels Congo Dennis the Menace The Game Hampstead The Karate Kid The Karate Kid Part II The Karate Kid Part III Kung Fu Panda Kung Fu Panda 2 Life as We Know It Love Actually Mary Magdalene Memoirs of a Geisha Midnight Run Mortal Kombat (1995) No Strings Attached Not Another Teen Movie Ophelia Sailor Moon Crystal: Seasons 1-3 She’s Out of My League Spanglish Star Trek The Strangers Stuart Little Supermarket Sweep: Season 1 Sword of Trust Talladega Nights: The Ballad of Ricky Bobby Terminator 2: Judgment Day Underworld Underworld: Awakening Underworld: Rise of the Lycans What Dreams May Come Why Do Fools Fall in Love ZATHURA: A SPACE ADVENTURE

July 2 The 8th Night — NETFLIX FILM  Big Timber — NETFLIX SERIES  Fear Street Part 1: 1994 — NETFLIX FILM Haseen Dillruba — NETFLIX FILM  Mortel: Season 2 — NETFLIX SERIES Snowpiercer

July 3 Grey’s Anatomy: Season 17

July 4 We The People — NETFLIX FAMILY

July 5 You Are My Spring — NETFLIX SERIES 

July 6 I Think You Should Leave with Tim Robinson: Season 2 — NETFLIX COMEDY SPECIAL

July 7 Brick Mansions Cat People — NETFLIX DOCUMENTARY Dogs: Season 2 — NETFLIX DOCUMENTARY The Mire: ’97 — NETFLIX SERIES  The War Next-door — NETFLIX SERIES  Major Grom: Plague Doctor — NETFLIX FILM  This Little Love of Mine

July 8 Elize Matsunaga: Once Upon a Crime — NETFLIX DOCUMENTARY  Home Again Midnight Sun RESIDENT EVIL: Infinite Darkness — NETFLIX ANIME

July 9 Atypical: Season 4 — NETFLIX SERIES Biohackers: Season 2 — NETFLIX SERIES  The Cook of Castamar — NETFLIX SERIES  Fear Street Part 2: 1978 — NETFLIX FILM How I Became a Superhero — NETFLIX FILM  Last Summer — NETFLIX FILM  Lee Su-geun: The Sense Coach — NETFLIX COMEDY SPECIAL  Virgin River: Season 3 — NETFLIX SERIES

July 10 American Ultra

July 13 Ridley Jones — NETFLIX FAMILY

July 14 A Classic Horror Story — NETFLIX FILM  The Guide to the Perfect Family — NETFLIX FILM  Gunpowder Milkshake — NETFLIX FILM Heist — NETFLIX DOCUMENTARY My Unorthodox Life — NETFLIX SERIES Private Network: Who Killed Manuel Buendía? — NETFLIX DOCUMENTARY

July 15 A Perfect Fit — NETFLIX FILM  BEASTARS: Season 2 — NETFLIX ANIME  Emicida: AmarElo – Live in São Paulo — NETFLIX DOCUMENTARY  My Amanda — NETFLIX FILM  Never Have I Ever: Season 2 — NETFLIX SERIES

July 16 The Beguiled Deep — NETFLIX FILM  Explained: Season 3 — NETFLIX DOCUMENTARY (NEW EPISODES WEEKLY) Fear Street Part 3: 1666 — NETFLIX FILM Johnny Test — NETFLIX FAMILY Twilight The Twilight Saga: New Moon The Twilight Saga: Eclipse The Twilight Saga: Breaking Dawn: Part 1 The Twilight Saga: Breaking Dawn: Part 2

July 17 Cosmic Sin

July 20 milkwater

July 21 Chernobyl 1986 — NETFLIX FILM  The Movies That Made Us: Season 2 — NETFLIX DOCUMENTARY One on One with Kirk Cameron: Season 1 Sexy Beasts — NETFLIX SERIES  Too Hot to Handle: Brazil — NETFLIX SERIES Trollhunters: Rise of the Titans — NETFLIX FAMILY

July 22 Still Working 9 to 5  Words Bubble Up Like Soda Pop — NETFLIX ANIME 

July 23 A Second Chance: Rivals! — NETFLIX FAMILY Bankrolled — NETFLIX FILM  Blood Red Sky — NETFLIX FILM  Kingdom: Ashin of the North — NETFLIX FILM  The Last Letter From Your Lover — NETFLIX FILM Masters of the Universe: Revelation — NETFLIX SERIES Sky Rojo: Season 2 — NETFLIX SERIES 

July 24 Charmed: Season 3 Django Unchained

July 26 The Walking Dead: Season 10 Wynonna Earp: Season 4

July 27 All American: Season 3 Mighty Express: Season 4 — NETFLIX FAMILY The Operative

July 28 Bartkowiak — NETFLIX FILM   Fantastic Fungi  The Flash: Season 7 The Snitch Cartel: Origins — NETFLIX SERIES  Tattoo Redo — NETFLIX SERIES Too Hot to Handle: Brazil — NETFLIX SERIES

July 29 Resort to Love — NETFLIX FILM Transformers: War for Cybertron: Kingdom — NETFLIX ANIME

July 30 Centaurworld — NETFLIX FAMILY Glow Up: Season 3 — NETFLIX SERIES  The Last Mercenary — NETFLIX FILM Myth & Mogul: John DeLorean — NETFLIX DOCUMENTARY Outer Banks: Season 2 — NETFLIX SERIES

July 31 The Vault

cnx.cmd.push(function() { cnx({ playerId: "106e33c0-3911-473c-b599-b1426db57530", }).render("0270c398a82f44f49c23c16122516796"); });

Leaving Netflix: July 2021

July 5 The Iron Lady

July 7 The Invitation

July 14 Holidays

July 15 The Princess and the Frog

July 19 Love Sick: The Series: Season 1

July 22 Oh My Ghost Oh My Ghost 2 Oh My Ghost 3 Oh My Ghost 4

July 28 The Croods

July 30 Spotlight

July 31 A Clockwork Orange  Bride of Chucky Child’s Play 2 Child’s Play 3  Eat Pray Love  Four Christmases  Freak Show  Fred Claus  Friends with Benefits G.I. Joe: The Rise of Cobra Grand Designs: Season 10  Grand Designs: Season 15  Hardcore Henry  Hinterland: Seasons 1-3 Hook Horns Jupiter Ascending King Arthur  Little Baby Bum: Nursery Rhyme Friends: S1 The Little Rascals Mad Max My Best Friend’s Wedding Nacho Libre  Nights in Rodanthe The Patriot  Remember Me Seed of Chucky Step Up: Revolution Your Highness  Zombieland 

The post New on Netflix: July 2021 Releases appeared first on Den of Geek.

from Den of Geek https://ift.tt/3dxU7H0

[analysisbyzee]

Classification Decision Tree for Heart Attack Analysis

Primarily, the required dataset is loaded. Here, I have uploaded the dataset available at Kaggle.com in the csv format.

All python libraries need to be loaded that are required in creation for a classification decision tree. Following are the libraries that are necessary to import:

The following code is used to load the dataset. read_csv() function is used to load the dataset.

column_names = ['age','sex','chest pain','resting blood pressure','cholestrol','fasting blood sugar','resting ecg','max heart rate','excercise included','old peak','slp','caa','THALL','output']

data= pd.read_csv("heart.csv",header=None,names=column_names)

data = data.iloc[1: , :] # removes the first row of dataframe

Now, we divide the columns in the dataset as dependent or independent variables. The output variable is selected as target variable for heart disease prediction system. The dataset contains 13 feature variables and 1 target variable.

feature_cols = ['age','sex','chest pain','chest pain','resting blood pressure','cholestrol','fasting blood sugar','resting ecg','max heart rate','excercise included','old peak','slp','caa','THALL']

pred = data[feature_cols] # Features

tar = data.output # Target variable

Now, dataset is divided into a training set and a test set. This can be achieved by using train_test_split() function. The size ratio is set as 60% for the training sample and 40% for the test sample.

pred_train, pred_test, tar_train, tar_test = train_test_split(X, y, test_size=0.4, random_state=1)

Using the shape function, we observe that the training sample has 181 observations (nearly 60% of the original sample) and 10 explanatory variables whereas the test sample contains 122 observations(nearly 40 % of the original sample) and 10 explanatory variables.

Now, we need to create an object claf_mod to initialize the decision tree classifer. The model is then trained using the fit function which takes training features and training target variables as arguments.

# To create an object of Decision Tree classifer

claf_mod = DecisionTreeClassifier()

# Train the model

claf_mod = claf_mod.fit(pred_train,tar_train)

To check the accuracy of the model, we use the accuracy_score function of metrics library. Our model has a classification rate of 58.19 %. Therefore, we can say that our model has good accuracy for finding out a person has a heart attack.

To find out the correct and incorrect classification of decision tree, we use the confusion matrix function. Our model predicted 18 true negatives for having a heart disease and 53 true positives for having a heart attack. The model also predicted 31 false negatives and 20 false positives for having a heart attack.

To display the decision tree we use export_graphviz function. The resultant graph is unpruned.

dot_data = StringIO()

export_graphviz(claf_mod, out_file=dot_data,

filled=True, rounded=True,

special_characters=True,class_names=['0','1'])

graph = pydotplus.graph_from_dot_data(dot_data.getvalue())

graph.write_png('heart attack.png')

Image(graph.create_png())

To get a prune graph, we changed the criterion as entropy and initialized the object again. The maximum depth of the tree is set as 3 to avoid overfitting.

# Create Decision Tree classifer object

claf_mod = DecisionTreeClassifier(criterion="entropy", max_depth=3)

# Train Decision Tree Classifer

claf_mod = claf_mod.fit(pred_train,tar_train)

#Predict the response for test dataset

tar_pred = claf_mod.predict(pred_test)

By optimizing the performance, the classification rate of the model increased to 72.13%.

By passing the object again into export_graphviz function, we obtain the prune graph.

From the above graph, we can infer that :

1) individuals having cholesterol less than 338 mg/dl, age less than or equal to 70.5 years, and whose previous peak was less than or equal to 1.55: 84 of them are more likely to have a heart attack whereas 42 of them will less likely to have a heart attack.

2) individuals having cholesterol less than 338 mg/dl, age less than or equal to 70.5 years, and whose previous peak was more than 1.55: 6 of them will less likely to have a heart attack whereas 38 of them are more likely to have a heart attack.

3) individuals having cholesterol less than 338 mg/dl and age less than or equal to 76.5 years: are less likely to have a heart attack

4) individuals having cholesterol less than 338 mg/dl and age more than 76.5 years: are more likely to have a heart attack

5) individuals having cholesterol more than 338 mg/dl : are less likely to have a heart attack

The Whole Code:

from google.colab import files uploaded = files.upload()

import pandas as pd from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier from sklearn.model_selection import train_test_split # Import train_test_split function from sklearn.metrics import classification_report import sklearn.metrics #Import scikit-learn metrics module for accuracy calculation from sklearn.tree import export_graphviz from sklearn.externals.six import StringIO from IPython.display import Image import pydotplus

column_names = ['age','sex','chest pain','resting blood pressure','cholestrol','fasting blood sugar','resting ecg','max heart rate','excercise included','old peak','slp','caa','THALL','output'] data= pd.read_csv("heart.csv",header=None,names=column_names) data = data.iloc[1: , :] # removes the first row of dataframe (In this case, ) #split dataset in features and target variable feature_cols = ['age','sex','chest pain','chest pain','resting blood pressure','cholestrol','fasting blood sugar','resting ecg','max heart rate','excercise included','old peak','slp','caa','THALL'] pred = data[feature_cols] # Features tar = data.output # Target variable pred_train, pred_test, tar_train, tar_test = train_test_split(X, y, test_size=0.4, random_state=1) # 60% training and 40% test pred_train.shape pred_test.shape tar_train.shape tar_test.shape

# To create an object of Decision Tree classifer claf_mod = DecisionTreeClassifier() # Train the model claf_mod = claf_mod.fit(pred_train,tar_train) #Predict the response for test dataset tar_pred = claf_mod.predict(pred_test) sklearn.metrics.confusion_matrix(tar_test,tar_pred) # Model Accuracy, how often is the classifier correct? print("Accuracy:",metrics.accuracy_score(tar_test, tar_pred)) dot_data = StringIO() export_graphviz(claf_mod, out_file=dot_data, filled=True, rounded=True, special_characters=True,class_names=['0','1']) graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) graph.write_png('heart attack.png') Image(graph.create_png())

# Create Decision Tree classifer object claf_mod = DecisionTreeClassifier(criterion="entropy", max_depth=3) # Train Decision Tree Classifer claf_mod = claf_mod.fit(pred_train,tar_train) #Predict the response for test dataset tar_pred = claf_mod.predict(pred_test) # Model Accuracy, how often is the classifier correct? print("Accuracy:",metrics.accuracy_score(tar_test, tar_pred)) from sklearn.externals.six import StringIO from IPython.display import Image from sklearn.tree import export_graphviz import pydotplus dot_data = StringIO() export_graphviz(claf_mod, out_file=dot_data, filled=True, rounded=True, special_characters=True, class_names=['0','1']) graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) graph.write_png('improved heart attack.png') Image(graph.create_png())

[kkmt]

Week - 1 Assignment, Analysis Of Variance

clear all

""" Week - 1 Assignment, Analysis Of Variance

Hypothesis - Do people tend to have more beer as they have more members above 18 years in the household to give them company

Variables chosen for this analysis

How often you consumer beer - S2AQ5B should be less than 99 (page 33 of NESARC) No of beers you drink - S2AQ5D should be less than 99 (page 33 of NESARC) No of persons 18 years and older in the household - NUMPER18 (page 2 of NESARC)

Null Hypothesis :The Average Number of Beers consumed is not different across families having 1 household member, 2 to 3 household members 4 to 6 household members and greater than 6 household members who are older than 18 years old

Alternate Hypothesis : The Average Number of Beers consumed is different across families having 1 household member, 2 to 3 household members 4 to 6 household members and greater than 6 household members who are older than 18 years old

If the Alternate Hypothesis is proved right, then does it mean that household members having greater than 6 members tend to consume more beer ?

@author: Keerthi Kumar (Keerthi.Kumar@mindtree.com) """

import numpy as np import pandas as pd import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi

data = pd.read_csv('E:\\My_Learnings\\Yorbit\\20200921_Data_Analysis_Tools\\week_1\\dataset\\nesarc.csv', low_memory=False)

#setting variables to numeric data['S2AQ5B'] = data['S2AQ5B'].convert_objects(convert_numeric=True) data['S2AQ5D'] = data['S2AQ5D'].convert_objects(convert_numeric=True) data['NUMPER18'] = data['NUMPER18'].convert_objects(convert_numeric=True)

#subset data for respondents who have consumed beer sub1=data[(data['S2AQ5B']<99) & (data['S2AQ5D']<=99)] len(sub1) #18291 Records

#subset data only for required columns sub2 = sub1[["S2AQ5B", "S2AQ5D","NUMPER18"]] len(sub2) #18291 Records

#checking for any missing records sum(pd.isnull(sub2['S2AQ5B'])) # No Records are null sum(pd.isnull(sub2['S2AQ5D'])) # No Records are null sum(pd.isnull(sub2['NUMPER18'])) # No Records are null

#converting the explanatory variable (i.e The number of members in a household - S2AQ5D) # converting the explanatory variable into 4 bins where # Bin 1 - 1 household member, Bin 2 - 2 to 3 household members, Bin 3 = 4 to 6 household members, Bin 34 = greater than 6 household members

#checking for levels in the data print(sub2['NUMPER18'].min()) print(sub2['NUMPER18'].max())

#creating bins in the data for the explanatory variable i.e Number18- Number of household members greater than 18 in the family sub2['Family_members_bin']=pd.cut(x = sub2['NUMPER18'],                        bins = [0,1,3,4,9],                        labels = [1,2,3,4]) sub2.head() check_bins = sub2.groupby('Family_members_bin').agg({'NUMPER18': ['min', 'max']}) print(check)

#checking for average number of beers consumed across groups and frequency within the groups stats_beer = sub2.groupby('Family_members_bin').agg({'S2AQ5D': ['mean','std','size']}) print(stats_beer) # Interpretation - It appears that the average consumption is different across groups but whether it is significant is the question

# using ols function for calculating the F-statistic and associated p value model1 = smf.ols(formula='S2AQ5D ~ C(Family_members_bin)', data=sub2) results1 = model1.fit() print (results1.summary())

mc1 = multi.MultiComparison(sub2['S2AQ5D'], sub2['Family_members_bin']) res1 = mc1.tukeyhsd() print(res1.summary())

""" Null Hypothesis - The average consumption of beer is not different between different size of Households for members of the households being older then 18 years Alternate Hypothesis - The average consumption of beer is different between different size of Housolds for members of the households being older than 18 years

A. Initial Analysis Interpretation

                      mean       std   size Family_members_bin                           1                  2.897834  6.043876   6509 2                   2.729754  4.724074  11101 3                   3.608611  6.703192    511 4                   3.323529  3.206139    170 Label 1 - 1 household member older than than 18 years 2 - 2 to 3 household members older than 18 years 3 - 4 to 6 household members older than 18 years 4- greater than 6 household members older than 18 years

a. From the above analysis, it is evident that the means are different between the groups. b. It is very clear that group 3 and group 4 have higher consumption of beer c. However it is unclear if 1 and 2 are different from each other, also if 3 and 4 are different from each other

B. Results from the F - Test and OLS results

OLS Regression Results                             ============================================================================== Dep. Variable:                 S2AQ5D   R-squared:                       0.001 Model:                            OLS   Adj. R-squared:                  0.001 Method:                 Least Squares   F-statistic:                     5.864 Date:                Tue, 22 Sep 2020   Prob (F-statistic):           0.000536 Time:                        18:18:12   Log-Likelihood:                -56392. No. Observations:               18291   AIC:                         1.128e+05 Df Residuals:                   18287   BIC:                         1.128e+05 Df Model:                           3                                         Covariance Type:            nonrobust                                         ==============================================================================================                                 coef    std err          t      P>|t|      [0.025      0.975] ---------------------------------------------------------------------------------------------- Intercept                      2.8978      0.065     44.267      0.000       2.770       3.026 C(Family_members_bin)[T.2]    -0.1681      0.082     -2.039      0.042      -0.330      -0.006 C(Family_members_bin)[T.3]     0.7108      0.243      2.929      0.003       0.235       1.186 C(Family_members_bin)[T.4]     0.4257      0.410      1.037      0.300      -0.379       1.230 ============================================================================== Omnibus:                    34676.204   Durbin-Watson:                   2.005 Prob(Omnibus):                  0.000   Jarque-Bera (JB):         53125932.316 Skew:                          14.817   Prob(JB):                         0.00 Kurtosis:                     265.354   Cond. No.                         12.6 ==============================================================================

Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Analysis Interpretation- The prob fstatistic is less than 0.05 and hence we could reject the null hypothesis and accept the alternate hypothesis stating that the groups have different consumption behavior.

C. Results from the Post Hoc multiple comparisons test

Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff  lower  upper  reject --------------------------------------------  1      2    -0.1681  -0.3799 0.0438 False  1      3     0.7108   0.0874 1.3342  True  1      4     0.4257  -0.6285 1.4799 False  2      3     0.8789   0.2649 1.4928  True  2      4     0.5938  -0.4549 1.6424 False  3      4    -0.2851  -1.4865 0.9164 False

Analysis Interpretation - a. Group 1 (1 household member older than 18 years) consume significantly less quantum of beer than than Group 3 (4 to 6 households members older than 18 years).   b. Group 2 (2 to 3 household member older than 18 years) consume significantly less beer than than Group 3 (4 to 6 households members older than 18 years).   c. However this relation does not seem to hold for the larger group when then there are more than 6 members in the household.

"""

[cloviaglade]

Gender: Female

Star sign: Pieces 

Current time: 1:55pm

Favorite artist: Skillet, Red

Favorite songs: Whispers in the Dark(skillet) Let it burn(Red), Hold me now(Red),Don't wake me(Skillet), Never too Late(Three days Grace) [I'm emo as hell]

Song stuck in your head: Sweet but a Psycho Ava Max

Last thing you googled: according to my phone "student body positions" (for that RAD headcanon list)

Other blogs: nope just this one 

Do you get asks: yes some are those silly chain mail stuff most are drabble and headcanon requests (I'm always accepting request but whether or not I have time to do them is up in the air)

Reason for your URL: Clovia= of clovers, glade= a clearing in the woods. My birthday is right after St. Patrick's day so I get excited about clovers because birthday

Average amount of sleep: I need 10 hours to function at 100% but I normally get around 5-7  a Night

Lucky number: 8

Currently wearing: skinny jeans with a graphic tee that has a ninja panda and a caption "I'm a ninja you cant see me" as well as some ankle socks

Dream job: author 😊😊😊

Dream trips: anywhere historic. I love museums and stuff. I wanna learn the things like a dumbass nerd ok. I also like to go hiking but I'm not picky about either as long as it's not cold out when I hike

Favorite food: I feel like the twins when it comes to food… I love burgers and sushi (deep fried eel rolls are my favorite) 

Play any instruments: is mayonnaise an instrument no I don't have an ear for making music

I'm not gonna tag people because I don't wanna be rude… if you wanna do the thing do the thing

@azulsartdump I did this instead of homework you are a horrible influence

[generatour1]

top 10 free python programming books pdf online download 

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[clearsundays]

CHARACTER PROFILE: Sun’li Yhunja

Hello to all you lovely people in the FFXIV RP community! I just hopped off a looooong hiatus (tempted back by ShB... hehe) and am looking to slowly get back into RP. Okay, let’s dive right in!!!

BASICS ––– ☼

ALIAS: Sun

FULL/BIRTH NAME: Sun’li Yhunja 

AGE: 22

RACE: Miqo’te (Keeper of the Moon)

GENDER: Male

SEXUALITY: Demi-bisexual/romantic

PERSONAL ––– ☼

PROFESSION: Traveling conjurer and apothecary, also does odd jobs

HOBBIES: Scaling cliffs in search of rare plants, doing food challenges and Hingan number puzzles, sneaking into restricted libraries, being a busybody and sticking his nose where he shouldn’t

LANGUAGES: Common, Huntspeak, Ishgardian (limited), Hingan (limited), Xaelic (VERY limited)

RESIDENCE: Officially has an estate in the Lavender Beds, but is rarely at home. (His mailbox is often stuffed at max capacity...)

BIRTHPLACE:  South Shroud

ENJOYS: Platonic skinship, taking care of others, dad jokes, friendly competition

FEARS: Uncertainty, loss of control, inadequacy

“Hey! You alright there? Keep your chin up; things’ll work out.”

PHYSICAL APPEARANCE ––– ☼

HAIR: Rose-taupe, pale blond stripe

EYES: Rose-gold

HEIGHT: 5′3″; on the short side for his race and gender. But he doesn’t seem to realize he’s short. (Or rather, doesn’t seem to realize that most people are taller...) 

BUILD: Slender at first glance, but his musculature is lean and toned. Having had acrobatics training in his childhood, he is flexible and light on his feet.

DISTINGUISHING MARKS: Keeper markings on his cheeks and, in the unlikely event you somehow manage to catch him with his shirt or jacket off, two large wing tattoos on his shoulder blades spanning his upper back and triceps.

COMMON APPEARANCE | ACCESSORIES: Dark red jacket, scarf, leather gloves, two-handed conjurer’s staff, hawkbill blade sheathed at his upper thigh, and a metal locket attached to a chain that contains a sealed sachet of smelling salts.

FAMILY ––– ☼

PARENTS: Sun Yhunja & Ezye’ir Yhunja. Currently vacationing in Yanxia.

SIBLINGS: (Oh boy!) From eldest to youngest: Sunha (eldest sister), Sun’a (eldest brother), Sunyi (elder sister), Sunyu (elder sister), Sun’to (elder brother), Sunhi (younger sister), Sunsa (younger sister), Sunne (younger sister), Sunan (younger sister), Sunai (youngest sister). 

PETS: Kava (Red panda, ferries his mail), Aska (Chocobo, only takes with him on extended trips)

TRAITS ––– ☼

EXTROVERTED / introverted / in between

disorganized / organized / IN BETWEEN

close minded / OPEN MINDED / in between

CALM / anxious / in between

disagreeable / agreeable / IN BETWEEN

cautious / RECKLESS / in between

patient / impatient / IN BETWEEN

OUTSPOKEN / reserved / in between

LEADER / follower / neither

EMPATHETIC / apathetic / in between

OPTIMISTIC / pessimistic / in between

traditional / MODERN / in between

HARD-WORKING / lazy / in between

cultured / uncultured / IN BETWEEN

LOYAL / disloyal / situational

RP HOOKS ––– ☼

Medic: Very straightforward - he’s obviously a healer with that big ol’ staff strapped to his back. He also used to tutor aspiring conjurers at Stillglade Fane and occasionally returns to the Shroud to run favors for his family and E-Sumi-Yan.

Obviously a Foreigner: Even in the blistering heat of Thanalan, Sun doesn’t take off his scarf or shed his jacket. Sometimes he runs jobs all the way to the Far East, and pretty much bulls through the language barrier with sheer tenacity and excessive charades. Sure, he might be dressed for Ishgard, but he definitely isn’t culturally groomed for any fancy House parties. If he’s committing a social faux pas, be a dear and let him know! Or if you’re fluent in the language, you could teach Sun a word or two. He’d certainly appreciate it!

Antagonist/Rival/FOIL: Sun is almost obnoxiously optimistic with a straight-arrow moral compass that he is quite often both reckless and loud about. An idealist at heart, he tends to look for the best in people even if it gets him bitten - but if that happens, don’t expect him to curl up and start crying in fetal position; he most certainly bites back. I’d certainly be interested in exploring this aspect of his character with someone who doesn’t mind butting heads with him! 

More at Sun’s RPC Wiki Page! And of course, I’m open to suggestions!

☼ ––– OOC NOTES –––– ☼

Hi everyone! First off, thanks for taking the time to read this! Secondly, I would like to make clear that although Sun is a warm-hearted and generally kind character, he can be very blunt and stubborn at times. I firmly draw the line between IC and OOC, and would ask that you extend the same courtesy during interactions. If Sun has any conflict with your character or vice versa, please do not take it personally.

That said, I have a strong preference for paragraph and plot-based RPs with some direction. If you don’t have any in mind, I’d be happy to brainstorm ideas with you! I prefer Discord or GDocs RP or even Tumblr RP, as my working hours are unpredictable and those methods provide a measure of flexibility. 

I WILL PLAY: most forms of RP, from serious darker themes to lighthearted fluff and humor. Plots are the best! Whether you want to involve him in your plot or vice versa, let’s talk about it!! Relationship development (whether platonic or romantic) is welcome and encouraged, but MUST make sense and develop organically. Just a disclaimer though, if you’re looking to attempt to romance him, expect the slowest burn of all slow burns. I’m talking ‘try to light some wet charcoal, but first you have to find the match in the lake’ slow burn. This man is seriously dense af; he’s oblivious when it comes to romantic or sexual suggestions unless it has pretty much been directly slapped in his face. And even then he will assume you’re joking. 

ASK ABOUT: long-term or permanent injury, scarring, long-term captivity or imprisonment, significant mental tampering, or anything that might dramatically change his character.

I WON’T PLAY: permanent character death, permanent crippling, anything that would leave him unable to function independently. Also, should be kinda obvious, but please do not interact if you’re only looking to ERP. 

SERVER: Mateus | Balmung (alt) | Crystal DC

TIME ZONE: PST (UTC -8)

AVAILABILITY: Afternoons or evenings; it varies as I keep an unpredictable work schedule. 

CONTACT: @clearsundays or @elevensuns on Tumblr. In-game at Sun’li Yhunja on Mateus and Balmung (mostly on Mateus)!

Thank you again for reading! Pa-pa-ya pinging: ♥  @mooglemeet @ffxiv-crystal-rp @crystalxivrp @balmungrp

[greetingsfromeboncreek]

A two for one this time! Normally I wouldn’t do two at a time like this, but my species selections for these two are thematically related (both bears), so it felt necessary. Design notes under the cut!

-I don’t quiiite headcanon either of their body shapes looking like this. I try to blend the animal and human anatomy with these designs, and bears are just sort of... lumpy, shapeless animals. Sorry, bears.

-Max definitely gave me more trouble to draw. ;n; I didn’t mean for him to look so stiff when I try to add a sense of dynamism in these drawings, but... well, he’s a very stiff, formal sort of person, and I didn’t want to draw him out of character either. Oh well, I suppose?

-I noted at the start of this series of drawings that familial relationships weren’t going to define what species they were, (such as Samantha and Jericho not even being mammals while their dad is a panda.) but considering how these two are foils to each other narratively and are rarely seen apart, I thought making them two species that are similar in appearance and genetics but very different in personality and public perception/symbolism was fun and fitting.

-The only ‘rule’ I have so far is that all the Wights are carnivorous species of animals because Symbolism... with the exception of Arthur for the irony. I’m sure he’s only a little bit bitter about it.

-I think Max was like, the second character I wrote a species down for in this series. He gives a very strong polar bear vibe, idk.

-Arthur was originally going to be a sun bear (which are. uh. they’re very. just google them. they certainly ARE.), but pandas felt more thematically appropriate. I also like how the black eye markings function both as eye bags and eyebrows.

-Sin Arthur (Arthwell? Maxthur? idk man)... ehnnn I thought about drawing him, but the lack of decent references and the fact that Max gave me enough trouble made me decide not to. P much he’d look a lot like Arthur but with sharper teeth+claws, washed out looking markings, and of course the altered hair style and outfit.

-As for what’s next... I’ll PROBABLY do Sigvaldi and King Law, but unfortunately most of the old Wights don’t have good enough visuals for me to do proper justice.