python vs R
resp=response.dropna(subset=['WorkToolsSelect'])
resp=resp.merge(rates,left_on='CompensationCurrency',right_on='originCountry',how='left')
python=resp[(resp['WorkToolsSelect'].str.contains('Python'))&(~resp['WorkToolsSelect'].str.contains('R'))]
R=resp[(~resp['WorkToolsSelect'].str.contains('Python'))&(resp['WorkToolsSelect'].str.contains('R'))]
both=resp[(resp['WorkToolsSelect'].str.contains('Python'))&(resp['WorkToolsSelect'].str.contains('R'))]
response['LanguageRecommendationSelect'].value_counts()[:2].plot.bar()
plt.show()
importance
f,ax=plt.subplots(1,2,figsize=(18,8))
response['JobSkillImportancePython'].value_counts().plot.pie(ax=ax[0],autopct='%1.1f%%',explode=[0.1,0,0],shadow=True,colors=['g','lightblue','r'])
ax[0].set_title('Python Necessity')
ax[0].set_ylabel('')
response['JobSkillImportanceR'].value_counts().plot.pie(ax=ax[1],autopct='%1.1f%%',explode=[0,0.1,0],shadow=True,colors=['lightblue','g','r'])
ax[1].set_title('R Necessity')
ax[1].set_ylabel('')
plt.show()
The proportion of users
from matplotlib_venn import venn2
# pip install matplotlib_venn 画交集用的
f,ax=plt.subplots(1,2,figsize=(18,8))
pd.Series([python.shape[0],R.shape[0],both.shape[0]],index=['Python','R','Both']).plot.bar(ax=ax[0])
ax[0].set_title('Number of Users')
venn2(subsets = (python.shape[0],R.shape[0],both.shape[0]), set_labels = ('Python Users', 'R Users'))
plt.title('Venn Diagram for Users')
plt.show()
py_sal=(pd.to_numeric(python['CompensationAmount'].dropna())*python['exchangeRate']).dropna()
py_sal=py_sal[py_sal<1000000]
R_sal=(pd.to_numeric(R['CompensationAmount'].dropna())*R['exchangeRate']).dropna()
R_sal=R_sal[R_sal<1000000]
both_sal=(pd.to_numeric(both['CompensationAmount'].dropna())*both['exchangeRate']).dropna()
both_sal=both_sal[both_sal<1000000]
trying=pd.DataFrame([py_sal,R_sal,both_sal])
trying=trying.transpose()
trying.columns=['Python','R','Both']
print('Median Salary For Individual using Python:',trying['Python'].median())
print('Median Salary For Individual using R:',trying['R'].median())
print('Median Salary For Individual knowing both languages:',trying['Both'].median())
Median Salary For Individual using Python: 48725.600000000006
Median Salary For Individual using R: 48421.99999999999
Median Salary For Individual knowing both languages: 59791.3
Different types of preferences
py1=python.copy()
r=R.copy()
py1['WorkToolsSelect']='Python'
r['WorkToolsSelect']='R'
r_vs_py=pd.concat([py1,r])
r_vs_py=r_vs_py.groupby(['CurrentJobTitleSelect','WorkToolsSelect'])['Age'].count().to_frame().reset_index()
r_vs_py.pivot('CurrentJobTitleSelect','WorkToolsSelect','Age').plot.barh(width=0.8)
fig=plt.gcf()
fig.set_size_inches(10,15)
plt.title('Job Title vs Language Used',size=15)
plt.show()
r Python than visually. Therefore, with job titles such as data analysts, business analysts and other people who play a very important role in the graphic and visual, they like R instead of Python. Also, almost 90% of the statistical personnel R, as mentioned earlier, Python better machine learning, so others machine learning engineers, scientists, and data or the like DBA programmers prefer Python.
Using language many years it!
r_vs_py=pd.concat([py1,r])
r_vs_py=r_vs_py.groupby(['Tenure','WorkToolsSelect'])['Age'].count().to_frame().reset_index()
r_vs_py.pivot('Tenure','WorkToolsSelect','Age').plot.barh(width=0.8)
fig=plt.gcf()
fig.set_size_inches(10,10)
plt.title('Job Tenure vs Language Used')
plt.show()
Different industries which language is used
r_vs_py=pd.concat([py1,r])
r_vs_py=r_vs_py.groupby(['EmployerIndustry','WorkToolsSelect'])['Age'].count().to_frame().reset_index()
r_vs_py.pivot('EmployerIndustry','WorkToolsSelect','Age').plot.barh(width=0.8)
fig=plt.gcf()
fig.set_size_inches(10,10)
plt.title('Industry vs Language Used')
plt.show()
r in the government sector is still very strong, the rest of the other industries, Python share of about 15-20% higher than R.
What Python and R, respectively, the most commonly used tool is it?
f,ax=plt.subplots(1,2,figsize=(20,15))
py_comp=python['WorkToolsSelect'].str.split(',')
py_comp1=[]
for i in py_comp:
py_comp1.extend(i)
plt1=pd.Series(py_comp1).value_counts()[1:15].sort_values(ascending=False).to_frame()
sns.barplot(plt1[0],plt1.index,ax=ax[0],palette=sns.color_palette('inferno_r',15))
R_comp=R['WorkToolsSelect'].str.split(',')
R_comp1=[]
for i in R_comp:
R_comp1.extend(i)
plt1=pd.Series(R_comp1).value_counts()[1:15].sort_values(ascending=False).to_frame()
sns.barplot(plt1[0],plt1.index,ax=ax[1],palette=sns.color_palette('inferno_r',15))
ax[0].set_title('Commonly Used Tools with Python')
ax[1].set_title('Commonly Used Tools with R')
plt.subplots_adjust(wspace=0.8)
plt.show()
