데이터의 빈도수 시각화

 

 

 

데이터 원본 : 2017 Kaggle Machine Learning & Data Science Survey

 

Kaggle과 Colab 연결¶

In [ ]:

!pip install kaggle 
from google.colab import files 
files.upload()

 

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)
Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.1)
Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)
Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)
Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.9.24)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)

 

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Saving kaggle.json to kaggle.json

Out[ ]:

{'kaggle.json': b'{"username":"minaahayley","key":"3af569f6a8c028e6233a6d29ce2439d3"}'}

In [ ]:

ls -1ha kaggle.json

 

kaggle.json

In [ ]:

!mkdir -p ~/.kaggle #create folder name Kaggle
!cp kaggle.json ~/.kaggle #copy kaggle.jason into folder Kaggle
!chmod 600 ~/.kaggle/kaggle.json #ignore Permission Warning

In [ ]:

#ls 명령어는 특정 경로에 어떤 파일이 있는지 확인해 보는 명령어다. 
%ls ~/.kaggle

 

competitions/  kaggle.json

In [ ]:

!kaggle datasets download -d kaggle/kaggle-survey-2017
!unzip kaggle-survey-2017.zip

 

Downloading kaggle-survey-2017.zip to /content
  0% 0.00/3.52M [00:00<?, ?B/s]
100% 3.52M/3.52M [00:00<00:00, 236MB/s]
Archive:  kaggle-survey-2017.zip
  inflating: RespondentTypeREADME.txt  
  inflating: conversionRates.csv     
  inflating: freeformResponses.csv   
replace multipleChoiceResponses.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
  inflating: multipleChoiceResponses.csv  
  inflating: schema.csv              

In [ ]:

!mkdir -p ~/.kaggle/competitions/kaggle-survey-2017 #create folder name Kaggle
!cp RespondentTypeREADME.txt ~/.kaggle/competitions/kaggle-survey-2017 #copy into folder Kaggle
!cp conversionRates.csv ~/.kaggle/competitions/kaggle-survey-2017 
!cp freeformResponses.csv ~/.kaggle/competitions/kaggle-survey-2017 
!cp multipleChoiceResponses.csv ~/.kaggle/competitions/kaggle-survey-2017 
!cp schema.csv ~/.kaggle/competitions/kaggle-survey-2017 

In [ ]:

#ls 명령어는 특정 경로에 어떤 파일이 있는지 확인해 보는 명령어다. 
%ls ~/.kaggle/competitions/kaggle-survey-2017

 

conversionRates.csv    multipleChoiceResponses.csv  SurveySchema.csv
freeformResponses.csv  RespondentTypeREADME.txt
freeFormResponses.csv  schema.csv

In [ ]:

#노트북안에서 그래프를 그리기 위해
%matplotlib inline

import pandas as pd 
import numpy as np 
from scipy import stats 
import matplotlib.pyplot as plt
import seaborn as sns 

#노트북에서 warning이 보이지 않게 하기 위해 
import warnings 
warnings.filterwarnings('ignore')

In [ ]:

survey = pd.read_csv('~/.kaggle/competitions/kaggle-survey-2017/schema.csv')
ffr = pd.read_csv('~/.kaggle/competitions/kaggle-survey-2017/freeformResponses.csv', encoding='ISO-8859-1', low_memory=False)
mcr = pd.read_csv('~/.kaggle/competitions/kaggle-survey-2017/multipleChoiceResponses.csv', encoding='ISO-8859-1', low_memory=False)

In [ ]:

mcr.sample()

Out[ ]:

	GenderSelect	Country	Age	EmploymentStatus	StudentStatus	LearningDataScience	CodeWriter	CareerSwitcher	CurrentJobTitleSelect	TitleFit	...	JobFactorExperienceLevel	JobFactorDepartment	JobFactorTitle	JobFactorCompanyFunding	JobFactorImpact	JobFactorRemote	JobFactorIndustry	JobFactorLeaderReputation	JobFactorDiversity	JobFactorPublishingOpportunity
11420	Male	Other	25.0	Employed full-time	NaN	NaN	Yes	NaN	Data Scientist	Fine	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

1 rows × 228 columns

 

sns.countplot()¶

In [ ]:

#성별 
#countplot 명령을 사용하면 각 카테고리 값별로 데이터가 얼마나 있는지 표시할 수 있다.
sns.countplot(y='GenderSelect', data=mcr)

Out[ ]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f90472e0ad0>

 

In [ ]:

#학력 
sns.countplot(y='FormalEducation', data=mcr)

Out[ ]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9043c79310>

 

In [ ]:

sns.countplot(y='FormalEducation',
              hue='GenderSelect',
              data=mcr).legend(loc='center left',
                       bbox_to_anchor=(1, 0.5))

Out[ ]:

<matplotlib.legend.Legend at 0x7f9040d7b650>

 

 

sns.distplot()¶

In [ ]:

#Seaborn의 distplot 명령은 러그와 커널 밀도 표시 기능이 있어서 Matplotlib의 hist 명령보다 많이 사용된다.

sns.distplot(mcr[mcr['Age']>0]['Age'])

Out[ ]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f90472c96d0>

 

In [ ]:

#연령대를 성별에 따라 그리기 
figure, (ax1, ax2) = plt.subplots(ncols=2)
figure.set_size_inches(12, 5)

sns.distplot(mcr[mcr['GenderSelect'] == 'Female']['Age'].dropna(),
             norm_hist=False, ax=ax1)
plt.title('Female')

sns.distplot(mcr[mcr['GenderSelect'] == 'Male']['Age'].dropna(),
             norm_hist=False, ax=ax2)
plt.title('Male')

Out[ ]:

Text(0.5, 1.0, 'Male')