scatterplot/ violon plot /histogram /boxplot

-

 dataset addresss=["https://data.gov.uk/dataset/bb3520e6-dd76-46d9-8bdd-86f0a2178be9/organogram-of-staff-roles-salaries/datafile/cb432dfd-a5eb-4eaa-8523-961601d5601b/preview#organogram"]

(save dataset as uk_statistic)

.

uk=pd.read_csv("uk_statistic")

.......................boxplot.....................................................

x=uk['Salary Cost of Reports (£)']

y=uk['Actual Pay Floor (£)']

plt.boxplot(x)

plt.title("UK Sststistics")

plt.xlabel('salary cost of Report')

plt.ylabel('Actual pay floor')

plt.show()



.........................................violin plot...................................................

x=uk['Salary Cost of Reports (£)']

#y=uk['Actual Pay Floor (£)']

plt.violinplot(x)

plt.show




..................................................histogram................................................

title = 'UK Sststistics'

plt.figure(figsize=(10,6))

plt.hist(uk['Actual Pay Floor (£)'])

plt.title(title, fontsize=15)

plt.xlabel('salary cost of Report')

plt.ylabel('Actual pay floor')

plt.savefig(title, dpi=300)

plt.show()



.....................................scatterplot.................................

x=uk['Salary Cost of Reports (£)']

y=uk['Actual Pay Floor (£)']

plt.scatter(x,y)

plt.title("UK Sststistics")

plt.xlabel('salary cost of Report')

plt.ylabel('Actual pay floor')

plt.show()



Comments

Post a Comment

Popular posts from this blog

Pcb Fault Detection(Deep Learning Technique)

-
  Hello,  that's how I applied my deep Learning skills to my electronics industry  application Technical except After iterating through many models we find this model to be giving good accuracy and least loss. we used  #yolo  v5 with 4000 epoch. here us the latest update to the dataset. I have gathered data related to pcb fault detection. I have gathered a huge amount of image data. and I have open sourced it via-Kaggle. feel free to explore it.
Read more

using your age and duration of workout finding how much calories you burned!

-
dataset: https://www.kaggle.com/fmendes/fmendesdat263xdemos/download ......................................................................................................................................................... import pandas as  pd import matplotlib.pyplot as plt import numpy as np import pickle  from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression df_calories=pd.read_csv("ineuron/calories.csv") df_exercise=pd.read_csv("ineuron/exercise.csv") data=df_exercise.merge(df_calories, on='User_ID') #print(data.head()) #[:,0:-1] all rows and all expect last column (independent  variable) also feature column #data_x = data.iloc[:,:-1].values data_x= df.loc[:,[ 'Weight','Age','Duration']] data_y=df.iloc[:,-1] #[:,-1]all rows and only last column (dependent variable) also target column #data_y = data.iloc[:,-1].values x_train,x_test,y_train,y_test = train_test_split(data_x,data_y,test...
Read more

list of keyword in python

-
one should not use this word as avariable in python it will throw an erroe ...............................................................................................  import keyword print (keyword.kwlist) output: ['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']
Read more