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Lab 13 Finding How The Data Is Distributed.ipynb
Lab 13 Finding How The Data Is Distributed.ipynb
Finding How The Data Is Distributed
Estimated time needed: 30 minutes
In this lab, you will work with a cleaned dataset to perform Exploratory Data Analysis (EDA). You will examine the structure of the data, visualize key variables, and analyze trends related to developer experience, tools, job satisfaction, and other important aspects.
Objectives
In this lab you will perform the following:
-
Understand the structure of the dataset.
-
Perform summary statistics and data visualization.
-
Identify trends in developer experience, tools, job satisfaction, and other key variables.
Install the required libraries
!pip install pandas
!pip install matplotlib
!pip install seaborn
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Step 1: Import Libraries and Load Data
-
Import the
pandas,matplotlib.pyplot, andseabornlibraries. -
You will begin with loading the dataset. You can use the pyfetch method if working on JupyterLite. Otherwise, you can use pandas' read_csv() function directly on their local machines or cloud environments.
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import textwrap
# Load the Stack Overflow survey dataset
data_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/n01PQ9pSmiRX6520flujwQ/survey-data.csv'
df = pd.read_csv(data_url)
# Display the first few rows of the dataset
print(df.head())
print('Initial dataframe:', df.shape)
ResponseId MainBranch Age \
0 1 I am a developer by profession Under 18 years old
1 2 I am a developer by profession 35-44 years old
2 3 I am a developer by profession 45-54 years old
3 4 I am learning to code 18-24 years old
4 5 I am a developer by profession 18-24 years old
Employment RemoteWork Check \
0 Employed, full-time Remote Apples
1 Employed, full-time Remote Apples
2 Employed, full-time Remote Apples
3 Student, full-time NaN Apples
4 Student, full-time NaN Apples
CodingActivities \
0 Hobby
1 Hobby;Contribute to open-source projects;Other...
2 Hobby;Contribute to open-source projects;Other...
3 NaN
4 NaN
EdLevel \
0 Primary/elementary school
1 Bachelor’s degree (B.A., B.S., B.Eng., etc.)
2 Master’s degree (M.A., M.S., M.Eng., MBA, etc.)
3 Some college/university study without earning ...
4 Secondary school (e.g. American high school, G...
LearnCode \
0 Books / Physical media
1 Books / Physical media;Colleague;On the job tr...
2 Books / Physical media;Colleague;On the job tr...
3 Other online resources (e.g., videos, blogs, f...
4 Other online resources (e.g., videos, blogs, f...
LearnCodeOnline ... JobSatPoints_6 \
0 NaN ... NaN
1 Technical documentation;Blogs;Books;Written Tu... ... 0.0
2 Technical documentation;Blogs;Books;Written Tu... ... NaN
3 Stack Overflow;How-to videos;Interactive tutorial ... NaN
4 Technical documentation;Blogs;Written Tutorial... ... NaN
JobSatPoints_7 JobSatPoints_8 JobSatPoints_9 JobSatPoints_10 \
0 NaN NaN NaN NaN
1 0.0 0.0 0.0 0.0
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
JobSatPoints_11 SurveyLength SurveyEase ConvertedCompYearly JobSat
0 NaN NaN NaN NaN NaN
1 0.0 NaN NaN NaN NaN
2 NaN Appropriate in length Easy NaN NaN
3 NaN Too long Easy NaN NaN
4 NaN Too short Easy NaN NaN
[5 rows x 114 columns]
Initial dataframe: (65437, 114)
Step 2: Examine the Structure of the Data
-
Display the column names, data types, and summary information to understand the data structure.
-
Objective: Gain insights into the dataset's shape and available variables.
## Write your code here
print(df.dtypes.sort_index().to_string())
print(df.info())
AIAcc str
AIBen str
AIChallenges str
AIComplex str
AIEthics str
AINextLess integrated str
AINextMore integrated str
AINextMuch less integrated str
AINextMuch more integrated str
AINextNo change str
AISearchDevAdmired str
AISearchDevHaveWorkedWith str
AISearchDevWantToWorkWith str
AISelect str
AISent str
AIThreat str
AIToolCurrently Using str
AIToolInterested in Using str
AIToolNot interested in Using str
Age str
BuildvsBuy str
BuyNewTool str
Check str
CodingActivities str
CompTotal float64
ConvertedCompYearly float64
Country str
Currency str
DatabaseAdmired str
DatabaseHaveWorkedWith str
DatabaseWantToWorkWith str
DevType str
EdLevel str
EmbeddedAdmired str
EmbeddedHaveWorkedWith str
EmbeddedWantToWorkWith str
Employment str
Frequency_1 str
Frequency_2 str
Frequency_3 str
Frustration str
ICorPM str
Industry str
JobSat float64
JobSatPoints_1 float64
JobSatPoints_10 float64
JobSatPoints_11 float64
JobSatPoints_4 float64
JobSatPoints_5 float64
JobSatPoints_6 float64
JobSatPoints_7 float64
JobSatPoints_8 float64
JobSatPoints_9 float64
Knowledge_1 str
Knowledge_2 str
Knowledge_3 str
Knowledge_4 str
Knowledge_5 str
Knowledge_6 str
Knowledge_7 str
Knowledge_8 str
Knowledge_9 str
LanguageAdmired str
LanguageHaveWorkedWith str
LanguageWantToWorkWith str
LearnCode str
LearnCodeOnline str
MainBranch str
MiscTechAdmired str
MiscTechHaveWorkedWith str
MiscTechWantToWorkWith str
NEWCollabToolsAdmired str
NEWCollabToolsHaveWorkedWith str
NEWCollabToolsWantToWorkWith str
NEWSOSites str
OfficeStackAsyncAdmired str
OfficeStackAsyncHaveWorkedWith str
OfficeStackAsyncWantToWorkWith str
OfficeStackSyncAdmired str
OfficeStackSyncHaveWorkedWith str
OfficeStackSyncWantToWorkWith str
OpSysPersonal use str
OpSysProfessional use str
OrgSize str
PlatformAdmired str
PlatformHaveWorkedWith str
PlatformWantToWorkWith str
ProfessionalCloud str
ProfessionalQuestion str
ProfessionalTech str
PurchaseInfluence str
RemoteWork str
ResponseId int64
SOAccount str
SOComm str
SOHow str
SOPartFreq str
SOVisitFreq str
SurveyEase str
SurveyLength str
TBranch str
TechDoc str
TechEndorse str
TimeAnswering str
TimeSearching str
ToolsTechAdmired str
ToolsTechHaveWorkedWith str
ToolsTechWantToWorkWith str
WebframeAdmired str
WebframeHaveWorkedWith str
WebframeWantToWorkWith str
WorkExp float64
YearsCode str
YearsCodePro str
<class 'pandas.DataFrame'>
RangeIndex: 65437 entries, 0 to 65436
Columns: 114 entries, ResponseId to JobSat
dtypes: float64(13), int64(1), str(100)
memory usage: 56.9 MB
None
Step 3: Handle Missing Data
-
Identify missing values in the dataset.
-
Impute or remove missing values as necessary to ensure data completeness.
## Write your code here
frnan = df.isna().mean().sort_values(ascending=False).round(decimals=4)
print('Fraction of missing values in each column')
print(frnan.head())
Fraction of missing values in each column
AINextMuch less integrated 0.9825
AINextLess integrated 0.9640
AINextNo change 0.8090
AINextMuch more integrated 0.7946
EmbeddedAdmired 0.7443
dtype: float64
# Drop the rows where all elements are missing.
df_m = df.dropna(how="all")
print('Initial dataframe:', df.shape)
print('Modified dataframe:', df_m.shape)
Initial dataframe: (65437, 114)
Modified dataframe: (65437, 114)
Step 4: Analyze Key Columns
-
Examine key columns such as
Employment,JobSat(Job Satisfaction), andYearsCodePro(Professional Coding Experience). -
Instruction: Calculate the value counts for each column to understand the distribution of responses.
## Write your code here
keycol = ['Employment', 'JobSat', 'YearsCodePro']
for col in keycol:
print(df_m[col].value_counts(), '\n')
Employment
Employed, full-time 39041
Independent contractor, freelancer, or self-employed 4846
Student, full-time 4709
Employed, full-time;Independent contractor, freelancer, or self-employed 3557
Not employed, but looking for work 2341
...
Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Employed, part-time 1
Student, full-time;Retired 1
Employed, full-time;Not employed, but looking for work;Student, part-time 1
Not employed, and not looking for work;Student, part-time;Employed, part-time 1
Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Retired 1
Name: count, Length: 110, dtype: int64
JobSat
8.0 7509
7.0 6379
6.0 3751
9.0 3626
10.0 2251
5.0 1956
3.0 1165
4.0 1130
2.0 772
0.0 311
1.0 276
Name: count, dtype: int64
YearsCodePro
2 4168
3 4093
5 3526
10 3251
4 3215
Less than 1 year 2856
6 2843
1 2639
8 2549
7 2517
12 1777
15 1635
20 1549
9 1493
11 1312
13 1127
14 1082
25 998
16 946
18 867
17 814
30 689
24 632
19 516
22 492
23 448
26 426
27 380
21 380
28 342
35 285
29 196
32 194
40 194
34 169
38 134
33 132
36 119
31 106
37 104
45 56
42 55
39 54
41 51
More than 50 years 50
44 42
43 37
46 21
50 14
48 14
49 11
47 10
Name: count, dtype: int64
Step 5: Visualize Job Satisfaction (Focus on JobSat)
-
Create a pie chart or KDE plot to visualize the distribution of
JobSat. -
Provide an interpretation of the plot, highlighting key trends in job satisfaction.
## Write your code here
JobSatpl = df_m.JobSat.value_counts()
JobSatpl.plot(kind='pie',
figsize=(8, 5),
autopct='%1.1f%%', # add in percentages
labels=None,
startangle=0,
pctdistance=1.12, # the ratio between the center of each pie slice and the start of the text
shadow=False,
colormap='Spectral'
)
plt.title('Job satisfaction')
plt.axis('equal') # Sets the pie chart to look like a circle
plt.legend(labels=JobSatpl.index.astype(int), loc='upper left')
plt.show()
plt.figure(figsize=(8,5))
sns.kdeplot(data=df_m.JobSat, bw_adjust=0.3)
plt.title('Job satisfaction')
plt.grid(True, linestyle='dashed')
plt.xticks(range(0, 11, 1))
plt.ylabel('Proportion of answers')
plt.show()
# Most of the people experienced job satisfaction higher than 5.
Step 6: Programming Languages Analysis
-
Compare the frequency of programming languages in
LanguageHaveWorkedWithandLanguageWantToWorkWith. -
Visualize the overlap or differences using a Venn diagram or a grouped bar chart.
## Write your code here
for col in ['LanguageHaveWorkedWith', 'LanguageWantToWorkWith']:
cat = set()
lang = df_m[col].dropna().unique()
for el in lang:
if ';' in el:
cat = cat | set(el.split(';'))
else:
cat.add(el)
cat = sorted(cat)
print(col, cat, len(cat)) # Columns that should be added to the dataframe
LanguageHaveWorkedWith ['Ada', 'Apex', 'Assembly', 'Bash/Shell (all shells)', 'C', 'C#', 'C++', 'Clojure', 'Cobol', 'Crystal', 'Dart', 'Delphi', 'Elixir', 'Erlang', 'F#', 'Fortran', 'GDScript', 'Go', 'Groovy', 'HTML/CSS', 'Haskell', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'MATLAB', 'MicroPython', 'Nim', 'OCaml', 'Objective-C', 'PHP', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Solidity', 'Swift', 'TypeScript', 'VBA', 'Visual Basic (.Net)', 'Zephyr', 'Zig'] 49
LanguageWantToWorkWith ['Ada', 'Apex', 'Assembly', 'Bash/Shell (all shells)', 'C', 'C#', 'C++', 'Clojure', 'Cobol', 'Crystal', 'Dart', 'Delphi', 'Elixir', 'Erlang', 'F#', 'Fortran', 'GDScript', 'Go', 'Groovy', 'HTML/CSS', 'Haskell', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'MATLAB', 'MicroPython', 'Nim', 'OCaml', 'Objective-C', 'PHP', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Solidity', 'Swift', 'TypeScript', 'VBA', 'Visual Basic (.Net)', 'Zephyr', 'Zig'] 49
plaWorked = df_m[['LanguageHaveWorkedWith']].dropna()
dummiesWorked = plaWorked['LanguageHaveWorkedWith'].str.get_dummies(sep=';')
plaWorked = dummiesWorked.agg('sum').T
plaWant = df_m[['LanguageWantToWorkWith']].dropna()
dummiesWant = plaWant['LanguageWantToWorkWith'].str.get_dummies(sep=';')
plaWant = dummiesWant.agg('sum').T
pla = pd.concat([plaWorked, plaWant], axis=1)
pla.columns = ['HaveWorkedWith', 'WantToWorkWith']
# Sort languages by HaveWorkedWith (optional but usually helpful)
pla_plot = (pla.reset_index().rename(columns={'index': 'Language'})
.melt(id_vars='Language',
var_name='Category',
value_name='Count'))
plt.figure(figsize=(14, 8))
sns.barplot(
data=pla_plot,
x='Language',
y='Count',
hue='Category')
plt.xticks(rotation=90)
plt.xlabel("Programming Language")
plt.ylabel("Number of Respondents")
plt.grid(True, axis='y', linestyle='dashed')
plt.title("Languages: Have Worked With vs Want to Work With")
plt.tight_layout()
plt.show()
Step 7: Analyze Remote Work Trends
- Visualize the distribution of RemoteWork by region using a grouped bar chart or heatmap.
## Write your code here
rwt = df_m[['Country', 'RemoteWork']].dropna()
rwt_tab = pd.crosstab(rwt.Country, rwt.RemoteWork)
print(rwt_tab.head())
RemoteWork Hybrid (some remote, some in-person) In-person Remote
Country
Afghanistan 17 13 10
Albania 14 10 14
Algeria 19 8 22
Andorra 1 5 4
Angola 4 6 5
rwt_tabfr = rwt_tab.div(rwt_tab.sum(axis=1), axis=0) # row‑wise normalization
plt.figure(figsize=(10, 40))
sns.heatmap(rwt_tabfr)
plt.title("Remote Work Trends by Region")
plt.show()
Step 8: Correlation between Job Satisfaction and Experience
-
Analyze the correlation between overall job satisfaction (
JobSat) andYearsCodePro. -
Calculate the Pearson or Spearman correlation coefficient.
## Write your code here
cjsycp = df_m[['JobSat', 'YearsCodePro']].dropna()
cjsycp = cjsycp.replace({'Less than 1 year': 0, 'More than 50 years': 51}).astype(int)
cjsycp.plot(kind='scatter', x='YearsCodePro', y='JobSat')
plt.title('Correlation Between Job Satisfaction and Experience')
plt.ylabel('JobSat')
plt.xlabel('YearsCodePro')
plt.show()
!pip install scipy
from scipy import stats
Collecting scipy
Downloading scipy-1.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)
Requirement already satisfied: numpy<2.7,>=1.26.4 in /opt/conda/lib/python3.12/site-packages (from scipy) (2.4.1)
Downloading scipy-1.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (35.0 MB)
[2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m35.0/35.0 MB[0m [31m151.1 MB/s[0m eta [36m0:00:00[0m00:01[0m
Installing collected packages: scipy
Successfully installed scipy-1.17.0
pearson_coef, p_valuep = stats.pearsonr(cjsycp['YearsCodePro'], cjsycp['JobSat'])
print("The Pearson Correlation Coefficient is", round(pearson_coef, 4), " with a P-value of P =", round(p_valuep, 4))
spearman_coef, p_values = stats.spearmanr(cjsycp['YearsCodePro'], cjsycp['JobSat'])
print("The Spearman Correlation Coefficient is", round(spearman_coef, 4), " with a P-value of P =", round(p_values, 4))
The Pearson Correlation Coefficient is 0.1042 with a P-value of P = 0.0
The Spearman Correlation Coefficient is 0.1192 with a P-value of P = 0.0
# Values of job satisfaction (JobSat) and Experience (YearsCodePro) show almost no linear or monotonic
# correlation. The correlation values between these two variables are statistically significant.
# The two variables most likely do not affect each other.
Step 9: Cross-tabulation Analysis (Employment vs. Education Level)
-
Analyze the relationship between employment status (
Employment) and education level (EdLevel). -
Instruction: Create a cross-tabulation using
pd.crosstab()and visualize it with a stacked bar plot if possible.
## Write your code here
emped = df_m[['EdLevel', 'Employment']].dropna()
dummies = emped['Employment'].str.get_dummies(sep=';').astype(bool)
emped = pd.concat([emped, dummies], axis=1).drop(['Employment'], axis=1)
emped_tab = pd.DataFrame()
for col in dummies.columns:
emped_tab[col] = emped[emped[col]].EdLevel.value_counts()
emped_tab = emped_tab.T.astype(int)
print(emped_tab.head())
EdLevel Bachelor’s degree (B.A., B.S., B.Eng., etc.) \
Employed, full-time 19136
Employed, part-time 1344
I prefer not to say 96
Independent contractor, freelancer, or self-emp... 3711
Not employed, and not looking for work 226
EdLevel Master’s degree (M.A., M.S., M.Eng., MBA, etc.) \
Employed, full-time 12489
Employed, part-time 753
I prefer not to say 53
Independent contractor, freelancer, or self-emp... 2679
Not employed, and not looking for work 110
EdLevel Some college/university study without earning a degree \
Employed, full-time 4494
Employed, part-time 669
I prefer not to say 51
Independent contractor, freelancer, or self-emp... 1634
Not employed, and not looking for work 174
EdLevel Professional degree (JD, MD, Ph.D, Ed.D, etc.) \
Employed, full-time 2312
Employed, part-time 149
I prefer not to say 29
Independent contractor, freelancer, or self-emp... 485
Not employed, and not looking for work 23
EdLevel Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) \
Employed, full-time 1860
Employed, part-time 623
I prefer not to say 92
Independent contractor, freelancer, or self-emp... 902
Not employed, and not looking for work 338
EdLevel Associate degree (A.A., A.S., etc.) \
Employed, full-time 1260
Employed, part-time 137
I prefer not to say 9
Independent contractor, freelancer, or self-emp... 323
Not employed, and not looking for work 22
EdLevel Something else \
Employed, full-time 448
Employed, part-time 69
I prefer not to say 83
Independent contractor, freelancer, or self-emp... 145
Not employed, and not looking for work 45
EdLevel Primary/elementary school
Employed, full-time 201
Employed, part-time 79
I prefer not to say 51
Independent contractor, freelancer, or self-emp... 159
Not employed, and not looking for work 155
ax = emped_tab.plot(kind='bar', figsize=(10, 6), stacked=True)
xlabels = [textwrap.fill(label.get_text(), width=20) for label in ax.get_xticklabels()]
ax.set(xlabel='Employment type', ylabel='Count of answers')
ax.set_xticklabels(xlabels)
plt.title('Relationship between employment status and education level')
plt.grid(True, axis='y', linestyle='dashed')
plt.show()
Step 10: Export Cleaned Data
- Save the cleaned dataset to a new CSV file for further use or sharing.
## Write your code here
df_m.to_csv("Lab13out.csv", index=False)
Summary:
In this lab, you practiced key skills in exploratory data analysis, including:
-
Examining the structure and content of the Stack Overflow survey dataset to understand its variables and data types.
-
Identifying and addressing missing data to ensure the dataset's quality and completeness.
-
Summarizing and visualizing key variables such as job satisfaction, programming languages, and remote work trends.
-
Analyzing relationships in the data using techniques like:
-
Comparing programming languages respondents have worked with versus those they want to work with.
-
Exploring remote work preferences by region.
-
-
Investigating correlations between professional coding experience and job satisfaction.
-
Performing cross-tabulations to analyze relationships between employment status and education levels.
Authors:
Ayushi Jain
Other Contributors:
Rav Ahuja Lakshmi Holla Malika
Copyright © IBM Corporation. All rights reserved.