# Right To Work States Analysis Using Python

In this project, we are going to analysis the right to work in different state. This dataset intends to compare certain statistics between RTW and non-RTW states. Namely, union membership, poverty rate, and median household income.

let’s get our environment ready with the libraries we’ll need and then import the data!

``````import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns``````

Check out the Data

``````df = pd.read_csv('~/accident_UK.csv')
``df.info()``

Let’s get the statistic information from our data.

``df.describe()``

It’s time to figure out our missing value in our dataset.

``````import missingno as msno
msno.matrix(df)``````

There is no any missing value in our dataset.

Now, It’s time to get the data summary. For this purpose we are going to visualise the result using Seaborn library.

``````plt.figure(figsize=(12,8))
sns.heatmap(df.describe()[1:].transpose(),
annot=True,linecolor="w",
linewidth=2,cmap=sns.color_palette("tab20"))
plt.title("Data summary")
plt.show()``````

Let’s get the correlation for each feature in our dataset.

``````cor_mat= df[:].corr()
fig=plt.gcf()
fig.set_size_inches(30,12)

Now, It is time to create pairplot in order to see the correlation for each feature.

``````plt.figure(figsize=(25,15))
sns.pairplot(df)``````

It’s time to create bar plot in order to see the count of state for each region.

``sns.catplot(x="Region", kind="count", palette="ch:.26", data=df, size = 9)``

Let’s visualise the count of state in each region based on the right to work criteria.

``````plt.figure(figsize=(25,15))
sns.countplot(x='Region',data=df,hue='RightToWork',palette='viridis')

# To relocate the legend

Now, It is the time to visualise the states which they don’t have right to work.

``````norighttoworkstate = df[df['RightToWork']=='No']
sns.catplot(x="StateAbbrev", palette="ch:.26", data=norighttoworkstate, size = 11, kind = 'count')``````

Let’s create the pie chart to see the percentage of right to work which is allowed and not in our dataset.

``````
explode = (0.1,0)
fig1, ax1 = plt.subplots(figsize=(12,7))
ax1.pie(df['RightToWork'].value_counts(), explode=explode,labels=['Yes','No'], autopct='%1.1f%%',
# Equal aspect ratio ensures that pie is drawn as a circle
ax1.axis('equal')
plt.tight_layout()
plt.legend()
plt.show()
``````

It is the time to visualise the Distribution of Poverty Rate by Region using Seaborn library.

``````plt.figure(figsize=(12,8))
plt.title("Distribution of Poverty Rate by Region")
for i in df['Region'].unique():
sns.distplot(df[(df['Region']==i)]['PovertyRate2022'], hist=False, kde=True, label=i)``````

Now, Let’s create heatmap based on Poverty Rate and Median Household Income.

``````plt.figure(figsize=(12,8))
sns.set()
sns.kdeplot( df['PovertyRate2022'], df['MedianHouseholdIncome2022'],

Let’s create Scatter plot of “Poverty Rate vs Median Household Income” based on right to work criteria

``````plt.figure(figsize=(12,8))
sns.scatterplot(x='PovertyRate2022',y='MedianHouseholdIncome2022',data=df,palette='viridis',hue='RightToWork')
plt.title('Scatter plot of Poverty Rate vs Median Household Income')``````

Let’s create Scatter plot of “Union Member Density vs Median Household Income” based on right to work criteria.

``````plt.figure(figsize=(12,8))
sns.scatterplot(x='UnionMemberDensity2021',y='MedianHouseholdIncome2022',data=df,palette='coolwarm',hue='RightToWork')
plt.title('Scatter plot of Union Member Density vs Median Household Income')``````

For the last analysis, Let’s see the Top 10 States By Poverty Rate with right to work critera

``````top10statesbyincome=df.sort_values(by='PovertyRate2022',ascending=False).head(10)
fig,ax=plt.subplots(figsize=(16,6))
ax=sns.barplot(x='StateName',y='PovertyRate2022',data=top10statesbyincome,palette="mako", hue = 'RightToWork')
ax.set_title('Top 10 States By Poverty Rate')``````

I hope you liked this analysis. you can keep in touch with me via my social medias. Thanks for your time to read this article 🙂

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