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-161
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‎Notebook/06.ipynb‎

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"version": "3.9.11"
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},
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"toc-autonumbering": false,
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"toc-showcode": true,
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"toc-showcode": false,
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"toc-showmarkdowntxt": false,
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"toc-showtags": true
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},

‎Notebook/08.ipynb‎

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‎Notebook/09.ipynb‎

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}
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],
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"source": [
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"## 직업별 월금 평균표 만들기\n",
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"## 직업별 월급 평균표 만들기\n",
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"\n",
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"# job, income 결측치 제거\n",
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"# job별 분리\n",
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"source": [
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"# 막대 그래프 만들기\n",
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"sns.barplot(data = bottom10, y = 'job', x = 'mean_income') \\\n",
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" .set(xlim = (0, 800))"
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" .set(xlim = [0, 800])"
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]
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},
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{
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],
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"source": [
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"# 남성 직업 빈도 막대 그래프 만들기\n",
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"sns.barplot(data = job_male, y = 'job', x = 'n').set(xlim = (0, 500))"
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"sns.barplot(data = job_male, y = 'job', x = 'n').set(xlim = [0, 500])"
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]
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},
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{
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],
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"source": [
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"# 여성 직업 빈도 막대 그래프 만들기\n",
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"sns.barplot(data = job_female, y = 'job', x = 'n').set(xlim = (0, 500))"
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"sns.barplot(data = job_female, y = 'job', x = 'n').set(xlim = [0, 500])"
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]
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},
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{
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],
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"source": [
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"# 막대 그래프 만들기\n",
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"sns.barplot(data = rel_div, x = \"religion\", y = 'proportion')"
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"sns.barplot(data = rel_div, x = 'religion', y = 'proportion')"
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]
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},
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{
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],
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"source": [
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"# 막대 그래프 만들기\n",
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"sns.barplot(data = age_div, x = \"ageg\", y = 'proportion')"
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"sns.barplot(data = age_div, x = 'ageg', y = 'proportion')"
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]
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},
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{

‎Notebook/12.ipynb‎

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‎Notebook/14.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 1,
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"id": "9a087723-e261-4694-a1ea-daf2d73d0178",
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"metadata": {
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"eval": false
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "0dd901a9-4afc-4d31-8fd2-ea5d13e1ef0b",
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"metadata": {
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"eval": false
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"1 suv 62 13.50000"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"id": "8a3d4443",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "18bd4d2e",
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"metadata": {},
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"outputs": [
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"Ttest_indResult(statistic=11.917282584324107, pvalue=2.3909550904711282e-21)"
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]
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},
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"metadata": {},
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}
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"cell_type": "code",
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"outputs": [
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"1 r 168 16.738095"
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},
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}
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},
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{
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"cell_type": "code",
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"id": "fbc58f04-3800-47ac-90de-8f69925a1d54",
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},
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{
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"cell_type": "code",
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"id": "99706a11-8e88-4ece-ba49-93205fd58f68",
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"metadata": {},
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"outputs": [
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"Ttest_indResult(statistic=-1.066182514588919, pvalue=0.28752051088667036)"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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}
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "0c9ef8ee",
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"metadata": {
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"eval": false
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"pce 0.614518 1.000000"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "37683dec",
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"metadata": {},
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"outputs": [
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"(0.6145176141932079, 6.773527303292085e-61)"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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"cell_type": "code",
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"id": "0806863d",
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"4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2"
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]
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"cell_type": "code",
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"id": "95f888bd",
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"outputs": [
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"carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1.00"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"id": "11fafd52",
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"metadata": {
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"eval": false
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},
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"outputs": [],
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"source": [
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"import matplotlib as plt\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams.update({'figure.dpi' : '120', # 해상도 설정\n",
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" 'figure.figsize': [7.5, 5.5]}) # 가로 세로 크기 설정"
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"eval": false
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"<AxesSubplot:>"
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"outputs": [
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" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
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"output_type": "execute_result"
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"outputs": [
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" [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
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"<AxesSubplot:>"
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" linewidths = .5, # 경계 구분선 추가\n",
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" vmax = 1, # 가장 진한 파란색으로 표현할 최대값\n",
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" vmin = -1, # 가장 진한 빨간색으로 표현할 최소값\n",
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" cbar_kws = {\"shrink\": .5}) # 범례 크기 줄이기"
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" cbar_kws = {'shrink': .5}) # 범례 크기 줄이기"
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]
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}
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],

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