<jupyter_start><jupyter_text>Developer OverviewData of participants that do not code during their work at DLR are not included in the following charts.  Table of Contents:* [What is the highest level of education you have attained?](What-is-the-highest-level-of-education-you-have-attained?)* [In which discipline is your highest academic qualification?](In-which-discipline-is-your-highest-academic-qualification?)* [How much time do you spent on ... ?](How-much-time-do-you-spent-on-...-?)* [How many years of software development experience do you have?](How-many-years-of-software-development-experience-do-you-have?)* [Who uses the code that you write?](Who-uses-the-code-that-you-write?)  Setting up<jupyter_code># Import notebook containing sampled dataset
%run "./00_data-cleaning.ipynb"
# Filtering the df
df = df[(df['Do you write code as part of your job?'] =='Yes')]

#x = len(df[df['write_code']== 'Yes'])
#df.drop(['write_code'], axis=1, inplace=True)<jupyter_output><empty_output><jupyter_text>What is the highest level of education you have attained?<jupyter_code># What is the highest level of education you have attained?
count = df['What is the highest level of education you have attained?'].value_counts()
results = pd.DataFrame(count.values, count.index)
results.columns = ['What is the highest level of education you have attained?']
display(results)
# Levels of education
order=['Doctorate','Master degree','Undergraduate degree', 'Final secondary-school examination', 'general qualification for university entrance', 'Other']
plt.figure(figsize=(15,10))
count = df['What is the highest level of education you have attained?'].value_counts()

sns.set(style="darkgrid")
sns.barplot(count.index, count.values, order=order)
plt.xticks(rotation= 90)
plt.title('What is the highest level of education you have attained?', bbox={'facecolor':'0.8', 'pad':12})
plt.ylabel('Number of participants', fontsize=13)
plt.xlabel('Education level', fontsize=13)

ax = plt.gca()
totals = []
for i in ax.patches:
    totals.append(i.get_height())
    
total = sum(totals)

for i in ax.patches:
    ax.text(i.get_x()+ 0.15, i.get_height()+.9, \
            str(round((i.get_height()/total)*100, 2))+'%', fontsize=17,
                color='dimgrey')
plt.show()<jupyter_output><empty_output><jupyter_text>In which discipline is your highest academic qualification?<jupyter_code># In which discipline is your highest academic qualification?
count = df['In which discipline is your highest academic qualification?'].value_counts()
results = pd.DataFrame(count.values, count.index)
display(results)
# Academic discipline for education and professional development
plt.figure(figsize=(15,10))
count = df['In which discipline is your highest academic qualification?'].value_counts()

sns.set(style="darkgrid")
sns.barplot(count.index, count.values)

plt.xticks(rotation= 90)
plt.title('In which discipline is your highest academic qualification?', bbox={'facecolor':'0.8', 'pad':12})
plt.ylabel('Number of participants', fontsize=15)
plt.xlabel('Academic discipline', fontsize=15)

ax = plt.gca()
totals = []
for i in ax.patches:
    totals.append(i.get_height())
    
total = sum(totals)

for i in ax.patches:
    ax.text(i.get_x()+ 0.15, i.get_height()+.9, \
            str(round((i.get_height()/total)*100, 2))+'%', fontsize=13,
                color='dimgrey')
plt.show()<jupyter_output><empty_output><jupyter_text>How much time do you spent on ... ?<jupyter_code># In an average month, how much time do you spend on...
fig, ax =plt.subplots(4,2, sharey=True, figsize=(25,20))

count1 = df['In an average month, how much time do you spend on software development?'].value_counts()
sns.set()
ax1 = sns.barplot(count1.index, count1.values, ax=ax[0,0], palette="cubehelix")
ax1.set_title("spend software development")


count2 = df['In an average month, how much time would you like to spend on software development?'].value_counts()
ax2 = sns.barplot(count2.index, count2.values, ax=ax[0,1], palette="cubehelix")
ax2.set_title("would spend software development")

count3 = df['In an average month, how much time do you spend on research?'].value_counts()
ax3 = sns.barplot(count3.index, count3.values, ax=ax[1,0], palette="cubehelix")
ax3.set_title("spend research")
count4 = df['In an average month, how much time would you like to spend on research?'].value_counts()
ax4 = sns.barplot(count4.index, count4.values, ax=ax[1,1], palette="cubehelix")
ax4.set_title("would spend research")

count5 = df['In an average month, how much time do you spend on management?'].value_counts()
ax5 = sns.barplot(count5.index, count5.values, ax=ax[2,0], palette="cubehelix")
ax5.set_title("spend management")
count6 = df['In an average month, how much time would you like to spend on management?'].value_counts()
ax6 = sns.barplot(count6.index, count6.values, ax=ax[2,1], palette="cubehelix")
ax6.set_title("would spend management")

count7 = df['In an average month, how much time do you spend on other activities?'].value_counts()
ax7 = sns.barplot(count7.index, count7.values, ax=ax[3,0], palette="cubehelix")
ax7.set_title("spend other activities")
count8 = df['In an average month, how much time would you like to spend on other activities?'].value_counts()
ax8 = sns.barplot(count8.index, count8.values, ax=ax[3,1], palette="cubehelix")
ax8.set_title("would spend other activities")
fig.suptitle('In an average month, how much time do you/ would you spend on...', bbox={'facecolor':'0.8', 'pad':12})
plt.show()<jupyter_output><empty_output><jupyter_text>How many years of software development experience do you have?<jupyter_code># How many years of software development experience do you have?
count = df['How many years of software development experience do you have?'].value_counts()
results = pd.DataFrame(count.values, count.index)
results.columns = ['Programming experience']
display(results)
# How many years of software development experience -> entscheiden mit oben
#df['programming_experience'] = df['programming_experience'].fillna(value=-1)
#convert_dict = {'programming_experience': int}
#df['programming_experience'] = df['programming_experience'].astype(convert_dict)

#df['programming_experience'] = df[df['programming_experience'] >=0]
#x = len(df[df['programming_experience']!= -1])
#df.drop(['programming_experience'], axis=1, inplace=True)

plt.figure(figsize=(15,10)) 
count = df['How many years of software development experience do you have?'].value_counts()

sns.set(style="darkgrid")
sns.barplot(count.index, count.values)
plt.xticks(rotation= 90)
plt.title('How many years of software development experience do you have?', bbox={'facecolor':'0.8', 'pad':12})
plt.ylabel('Number of participants', fontsize=15)
plt.xlabel('Programming experience', fontsize=15)
plt.show()
# How many years of software development experience
plt.figure(figsize=(9,8))
plt.title('How many years of software development experience do you have?', bbox={'facecolor':'0.8', 'pad':12})
bplot = sns.boxplot(y='How many years of software development experience do you have?', data=df, width=0.5, palette="colorblind")<jupyter_output><empty_output><jupyter_text>Who uses the code that you write?<jupyter_code># Who uses the code that you write?
count = df['Who uses the code that you write?'].value_counts()
results = pd.DataFrame(count.values, count.index)
results.columns = ['Who use the code']
display(results)
# who use the code
order=['0 - Mostly me','1','2', '3', '4', '5 - Mostly other people']
plt.figure(figsize=(11,10))
count = df['Who uses the code that you write?'].value_counts()

sns.set(style="darkgrid")
sns.barplot(count.index, count.values, order=order)
# plt.xticks(rotation= 90)
plt.title('Who uses the code that you write?', bbox={'facecolor':'0.8', 'pad':12})
plt.ylabel('Number of participants', fontsize=15)
plt.xlabel('who use code', fontsize=15)

ax = plt.gca()
totals = []
for i in ax.patches:
    totals.append(i.get_height())
    
total = sum(totals)

for i in ax.patches:
    ax.text(i.get_x()+ 0.15, i.get_height()+.9, \
            str(round((i.get_height()/total)*100, 2))+'%', fontsize=17,
                color='dimgrey')
plt.show()<jupyter_output><empty_output>
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