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Merge pull request codehouseindia#360 from mishra-ayushknp/patch-5
Create data analysis using python
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‎data analysis using python‎

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#!/usr/bin/env python
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# coding: utf-8
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# # H1B VISA DATA ANALYSIS
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# # importing libraries
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# In[2]:
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import pandas as pd
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# In[3]:
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Reading csv file
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# In[4]:
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f = pd.read_csv("h1b_kaggle.csv")
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f.head()
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# deleting unnamed column
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# In[5]:
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del f['Unnamed: 0']
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len(f)
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# In[6]:
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f = f.dropna()
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f.reset_index()
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lng = len(f)
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print(lng)
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# In[6]:
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f.head()
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# TOP EMPLOYERS
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# In[7]:
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f.EMPLOYER_NAME.value_counts().head(15)
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# In[8]:
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f['EMPLOYER_NAME'].value_counts().head(15).plot(kind = 'bar',title = "TOP 15 Hiring Company")
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# ANALYZING THE PREVAILING WAGE
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# In[21]:
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f.PREVAILING_WAGE.value_counts().sort_values(ascending = False ).head()
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# In[9]:
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f.PREVAILING_WAGE.mean()
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# In[24]:
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###Wages given by the employers
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# In[10]:
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a = f.groupby(['EMPLOYER_NAME']).mean()['PREVAILING_WAGE'].nlargest(15).plot(kind = 'bar')
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# WORKSITE
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# In[11]:
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f['WORKSITE'].value_counts().head(20)
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# In[12]:
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f.loc[:,'WORKSITE'] = f.loc[:,'WORKSITE'].apply(lambda rec:rec.split(',')[1][1:])
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def change_NA(rec):
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if (rec=='NA') :
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return 'MARIANA ISLAND'
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return rec
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f.loc[:,'WORKSITE'] = f.loc[:,'WORKSITE'].apply(lambda rec: change_NA(rec))
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print(len(f['WORKSITE'].unique()))
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# In[13]:
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f['WORKSITE'].value_counts().head(20).plot(kind='bar')
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# Changing the columns name
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# In[14]:
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f.rename(columns ={'EMPLOYER_NAME':'EMPLOYER','FULL_TIME_POSITION':'FULL_T','PREVAILING_WAGE':'PREV_WAGE','WORKSITE':'STATES','lon':'LON','lat':'LAT'},inplace = True)
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# In[34]:
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columns_to_keep = ['CASE_STATUS','YEAR','STATES','SOC_NAME','JOB_TITLE','FULL_T','PREV_WAGE','EMPLOYER','LON','LAT']
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f =f[columns_to_keep]
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f.columns
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# Setting the precision value upto 2 decimal place
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# In[15]:
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f['LON'] = f['LON'].apply(lambda lon : float("% .2f" %lon))
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# In[16]:
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f['LAT'] = f['LAT'].apply(lambda lat : float("% .2f" %lat))
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# In[17]:
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f.head()
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# In[18]:
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f['CASE_STATUS'].unique()
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# calculating the petition ditribution by case status
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# In[19]:
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s_f = [0]*7
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states = ['CERTIFIED-WITHDRAWN', 'WITHDRAWN', 'CERTIFIED', 'DENIED',
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'REJECTED', 'INVALIDATED',
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'PENDING QUALITY AND COMPLIANCE REVIEW - UNASSIGNED']
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for i in range(0,7):
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s_f[i]= f[f.CASE_STATUS == states[i]]['CASE_STATUS'].count()
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s_f
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# In[20]:
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from matplotlib.pyplot import pie , axis , show
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plt.figure(figsize=(4.5,4.5))
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plt.title("PETITION BY CASE STATUS")
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axis('equal')
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pie(s_f[:4],labels = states[:4])
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show()
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# calculating petitions distribution per year
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# In[21]:
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f['YEAR'].unique()
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# In[22]:
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y = [2016., 2015., 2014., 2013., 2012., 2011.]
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y_c = [0]*6
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for j in range(0,6):
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y_c[j] = f[f.YEAR==y[j]]['YEAR'].count()
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y_c
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# In[23]:
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sns.set_context("notebook",font_scale = 1.0)
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plt.figure(figsize = (13,3))
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plt.title("PETITIONS DISTRIBUTION PER YEAR")
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sns.countplot(f['YEAR'])
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# ANALYZING DENIED PETITIONS PER YEAR
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# In[24]:
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den = f[f.CASE_STATUS=='DENIED']
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len(den)
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# In[25]:
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del den['CASE_STATUS']
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den = den.reset_index()
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den.head(2)
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# Calculating denied petition distribution per year
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# In[26]:
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d_y = [0]*6
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for i in range(0,6):
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d_y[i]=den[den.YEAR==y[i]]['YEAR'].count()
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d_y
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# In[27]:
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sns.set_context("notebook",font_scale = 1.0)
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plt.figure(figsize = (13,3))
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plt.title(" DENIED PETITIONS DISTRIBUTION PER YEAR")
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sns.countplot(den['YEAR'])
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# Calculating the rate at which denied petitins per year
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#
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# In[28]:
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d_y_r = [0]*6
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for i in range(0,6):
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d_y_r[i] = float("% .2f" %((d_y[i]/y_c[i])*100))
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ratio = pd.DataFrame()
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ratio['YEAR'] = y
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ratio['denied rate %'] = d_y_r
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ratio = ratio.set_index(['YEAR'])
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ratio
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# In[29]:
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ratio.T
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# In[30]:
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ratio = ratio.reset_index()
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ratio
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# In[31]:
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sns.set_context("notebook",font_scale = 1.0)
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plt.figure(figsize=(13,3))
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plt.title("DENIED PETITIONS PER YEAR")
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g = sns.barplot(x ='YEAR',y ='denied rate %',data=ratio)
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# Calculating the number of petitions filled by the states
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#
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# In[32]:
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f['STATES'].unique()
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# In[34]:
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US_states = ['ALABAMA','ALASKA','ARIZONA','ARKANSAS','CAALIFORNIA','COLORADO','CONNECTICUT','DELAWARE','DISTRICT OF COLUMBIA','FLORIDA','GEORGIA','HAWII','IDAHO','ILLINOIS','INDIANA','IOWA','KANSAS','KENTUCKY','LOUISTANA','MAINE','MARIANA ISLANDS','MARYLAND','MASSACHUUSSETS','MICHIGAN','MINNESOTA','MISSISSIPPI','MISSOURI','MONTANA','NEBRASKA','NEVADA','NEW HAMPSHIRE','NEW JERSEY','NEW MEXICO','NEW YORK','NORTH CAROLINA','NORTH DAKOTA','OHIO','OKALHOMA','OREGON','PENNSYLVANIA','PUERTO RICO','RHODE ISLAND','SOUTH CAROLINA','SOUTH DAKOTA','TENNESEE','TEXAS','UTAH','VERMONT','VIRGINIA','WASHINGTON','WEST VIRGINIA','WISCONSIN','WYOMING']
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p_s = [0]*53
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for i in range(0,53):
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p_s[i]= f[f.STATES == US_states[i]]['STATES'].count()
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pe = pd.DataFrame()
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pe['STATES']= US_states
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pe['FIELD PETITIONS'] = p_s
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print(sum(p_s))
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# In[56]:
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sns.set_context("notebook",font_scale = 1.0)
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plt.figure(figsize=(13,7))
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plt.title("FIELD PETITION BY STATE")
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g = sns.barplot(x ='STATES',y ='FIELD PETITIONS',data=pe)
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v = g.set_xticklabels(g.get_xticklabels(),rotation = 90)
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# NO. OF PETITIONS DENIED BY STATE
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# In[49]:
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deni = [0]*53
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for i in range(0,53):
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deni[i]= den[den.STATES==US_states[i]]['STATES'].count()
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de_s = pd.DataFrame()
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de_s['STATES'] = US_states
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de_s['DENIED PETITIONS'] = deni
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de_s
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# In[50]:
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print(sum(deni))
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# In[59]:
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sns.set_context("notebook",font_scale = 1.0)
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plt.figure(figsize=(13,3))
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plt.title("DENIED PETITIONS BY STATE")
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g = sns.barplot(x ='STATES',y ='DENIED PETITIONS',data=de_s)
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v = g.set_xticklabels(g.get_xticklabels(),rotation = 90)
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