[Python, 데이터분석] 판다스 관용 해법 사용하기: Pandorable한 코드(Idiomatic Pandas: Pandorable Code), 메서드 체이닝(Method Chaining)
| 판다스 관용 해법(Idiomatic Pandas)
판다스(Pandas)에서는 데이터 프레임을 통한 데이터 처리를 좀 더 수월하게 할 수 있도록 판다스만의 솔루션을 지원합니다. 이것을 관용 해법(Idiomatic Pandas)이라 말하고 Pandorable한 코드라고 판다스 커뮤니티에서는 이야기합니다.
마치 파이썬에서도 다른 언어에서는 볼 수 없는 파이썬 만의 독특한 처리방식이 있는 것처럼 판다스도 고유의 처리방법을 지원하는 거라 생각하시면 될 것 같습니다.
관용 해법은 높은 성능과 가독성을 동시에 지니고 있습니다. 이 방식을 채택하여 보통 파이썬언어가 지원하는 방식보다 간결하고 깔끔한 코드로 데이터를 처리하는 것이 가능해집니다.
| 판다스 관용해법 예제(Idiomatic Pandas Example) - 메서드 체이닝(Method Chaining)
판다스 관용해법 즉 Pandorable한 코드에서는 메서드 연쇄를 지원합니다. 함수형 프로그래밍에서의 해법과 거의 똑같죠. 아래는 그 예제를 모아놓은 것입니다. 이 예제들은 아래의 csv 파일을 가지고 진행하였습니다.
import pandas as pd
# [3193 rows x 100 columns] 의 사이즈를 가지는 데이터프레임
df = pd.read_csv("census.csv")
print(df)
'''
SUMLEV REGION ... RNETMIG2014 RNETMIG2015
0 40 3 ... 1.724718 0.712594
1 50 3 ... 2.592270 -2.187333
2 50 3 ... 20.317142 18.293499
3 50 3 ... -3.978583 -10.543299
4 50 3 ... 0.754533 1.107861
5 50 3 ... -1.577232 -0.884411
6 50 3 ... 17.243790 -13.193961
7 50 3 ... 1.184103 -6.430868
8 50 3 ... -3.912834 -2.806406
9 50 3 ... -1.290228 2.346901
10 50 3 ... 0.230419 -2.931307
11 50 3 ... 0.957636 -1.752709
12 50 3 ... -1.274984 -5.291205
13 50 3 ... -8.486280 -5.411736
14 50 3 ... 5.776708 3.986270
15 50 3 ... 3.654485 -3.123961
16 50 3 ... -0.767822 5.350738
17 50 3 ... 2.533249 0.588052
18 50 3 ... -14.645416 2.684140
19 50 3 ... -23.049483 -5.387581
20 50 3 ... 3.750759 -0.264121
21 50 3 ... 4.734577 5.087600
22 50 3 ... 5.593387 8.417777
23 50 3 ... -9.503292 -1.998668
24 50 3 ... -9.368689 -14.711389
25 50 3 ... 1.198187 0.956790
26 50 3 ... -0.951175 2.757093
27 50 3 ... -1.136950 1.243830
28 50 3 ... -1.234901 -1.588308
29 50 3 ... 0.237396 -2.857058
... ... ... ... ... ...
3163 50 2 ... 2.104796 0.059931
3164 50 2 ... 2.077633 0.593567
3165 50 2 ... -0.134227 -0.173022
3166 50 2 ... -4.535615 -4.024395
3167 50 2 ... -1.545153 -3.685304
3168 50 2 ... -5.040889 -3.414223
3169 40 4 ... -3.727831 -2.091573
3170 50 4 ... 1.429233 -5.166460
3171 50 4 ... -7.608378 5.513554
3172 50 4 ... -8.509402 10.978525
3173 50 4 ... -2.338590 -22.600668
3174 50 4 ... -19.358233 1.126443
3175 50 4 ... 7.071547 19.309219
3176 50 4 ... -12.013555 -13.352750
3177 50 4 ... -5.761986 -10.635133
3178 50 4 ... -6.224712 -5.663940
3179 50 4 ... -10.715742 0.933652
3180 50 4 ... -0.207819 1.673640
3181 50 4 ... 6.234414 1.662823
3182 50 4 ... -0.110593 0.793743
3183 50 4 ... -12.603387 7.492114
3184 50 4 ... -5.585731 0.856839
3185 50 4 ... 5.598720 4.207414
3186 50 4 ... 4.781489 -2.198937
3187 50 4 ... -14.409938 -20.664059
3188 50 4 ... -14.075283 -14.070195
3189 50 4 ... 16.308671 1.520747
3190 50 4 ... -14.740608 -12.606351
3191 50 4 ... -18.020168 1.441961
3192 50 4 ... 1.533635 6.935294
'''
# 기존의 인덱싱 방식
df_1 = df[df["SUMLEV"]==50]
df_1.set_index(['STNAME', 'CTYNAME'], inplace=True)
df.rename(columns={"ESTIMATESBASE2010": "Estimates Base 2010"})
print(df_1.columns.values)
'''
['SUMLEV' 'REGION' 'DIVISION' 'STATE' 'COUNTY' 'CENSUS2010POP'
'ESTIMATESBASE2010' 'POPESTIMATE2010' 'POPESTIMATE2011' 'POPESTIMATE2012'
'POPESTIMATE2013' 'POPESTIMATE2014' 'POPESTIMATE2015' 'NPOPCHG_2010'
'NPOPCHG_2011' 'NPOPCHG_2012' 'NPOPCHG_2013' 'NPOPCHG_2014'
'NPOPCHG_2015' 'BIRTHS2010' 'BIRTHS2011' 'BIRTHS2012' 'BIRTHS2013'
'BIRTHS2014' 'BIRTHS2015' 'DEATHS2010' 'DEATHS2011' 'DEATHS2012'
'DEATHS2013' 'DEATHS2014' 'DEATHS2015' 'NATURALINC2010' 'NATURALINC2011'
'NATURALINC2012' 'NATURALINC2013' 'NATURALINC2014' 'NATURALINC2015'
'INTERNATIONALMIG2010' 'INTERNATIONALMIG2011' 'INTERNATIONALMIG2012'
'INTERNATIONALMIG2013' 'INTERNATIONALMIG2014' 'INTERNATIONALMIG2015'
'DOMESTICMIG2010' 'DOMESTICMIG2011' 'DOMESTICMIG2012' 'DOMESTICMIG2013'
'DOMESTICMIG2014' 'DOMESTICMIG2015' 'NETMIG2010' 'NETMIG2011'
'NETMIG2012' 'NETMIG2013' 'NETMIG2014' 'NETMIG2015' 'RESIDUAL2010'
'RESIDUAL2011' 'RESIDUAL2012' 'RESIDUAL2013' 'RESIDUAL2014'
'RESIDUAL2015' 'GQESTIMATESBASE2010' 'GQESTIMATES2010' 'GQESTIMATES2011'
'GQESTIMATES2012' 'GQESTIMATES2013' 'GQESTIMATES2014' 'GQESTIMATES2015'
'RBIRTH2011' 'RBIRTH2012' 'RBIRTH2013' 'RBIRTH2014' 'RBIRTH2015'
'RDEATH2011' 'RDEATH2012' 'RDEATH2013' 'RDEATH2014' 'RDEATH2015'
'RNATURALINC2011' 'RNATURALINC2012' 'RNATURALINC2013' 'RNATURALINC2014'
'RNATURALINC2015' 'RINTERNATIONALMIG2011' 'RINTERNATIONALMIG2012'
'RINTERNATIONALMIG2013' 'RINTERNATIONALMIG2014' 'RINTERNATIONALMIG2015'
'RDOMESTICMIG2011' 'RDOMESTICMIG2012' 'RDOMESTICMIG2013'
'RDOMESTICMIG2014' 'RDOMESTICMIG2015' 'RNETMIG2011' 'RNETMIG2012'
'RNETMIG2013' 'RNETMIG2014' 'RNETMIG2015']
'''
print(df_1)
'''
SUMLEV ... RNETMIG2015
STNAME CTYNAME ...
Alabama Autauga County 50 ... -2.187333
Baldwin County 50 ... 18.293499
Barbour County 50 ... -10.543299
Bibb County 50 ... 1.107861
Blount County 50 ... -0.884411
Bullock County 50 ... -13.193961
Butler County 50 ... -6.430868
Calhoun County 50 ... -2.806406
Chambers County 50 ... 2.346901
Cherokee County 50 ... -2.931307
Chilton County 50 ... -1.752709
Choctaw County 50 ... -5.291205
Clarke County 50 ... -5.411736
Clay County 50 ... 3.986270
Cleburne County 50 ... -3.123961
Coffee County 50 ... 5.350738
Colbert County 50 ... 0.588052
Conecuh County 50 ... 2.684140
Coosa County 50 ... -5.387581
Covington County 50 ... -0.264121
Crenshaw County 50 ... 5.087600
Cullman County 50 ... 8.417777
Dale County 50 ... -1.998668
Dallas County 50 ... -14.711389
DeKalb County 50 ... 0.956790
Elmore County 50 ... 2.757093
Escambia County 50 ... 1.243830
Etowah County 50 ... -1.588308
Fayette County 50 ... -2.857058
Franklin County 50 ... 0.442422
... ... ... ...
Wisconsin Washburn County 50 ... -4.995197
Washington County 50 ... 0.059931
Waukesha County 50 ... 0.593567
Waupaca County 50 ... -0.173022
Waushara County 50 ... -4.024395
Winnebago County 50 ... -3.685304
Wood County 50 ... -3.414223
Wyoming Albany County 50 ... -5.166460
Big Horn County 50 ... 5.513554
Campbell County 50 ... 10.978525
Carbon County 50 ... -22.600668
Converse County 50 ... 1.126443
Crook County 50 ... 19.309219
Fremont County 50 ... -13.352750
Goshen County 50 ... -10.635133
Hot Springs County 50 ... -5.663940
Johnson County 50 ... 0.933652
Laramie County 50 ... 1.673640
Lincoln County 50 ... 1.662823
Natrona County 50 ... 0.793743
Niobrara County 50 ... 7.492114
Park County 50 ... 0.856839
Platte County 50 ... 4.207414
Sheridan County 50 ... -2.198937
Sublette County 50 ... -20.664059
Sweetwater County 50 ... -14.070195
Teton County 50 ... 1.520747
Uinta County 50 ... -12.606351
Washakie County 50 ... 1.441961
Weston County 50 ... 6.935294
[3142 rows x 98 columns]
'''
'''
# 메서드 체이닝 방식 기존의 것과 비교하면 불필요한 임시 변수 생성 없이
간결하게 처리 가능하다.
df_1 = df[df["SUMLEV"]==50]
df_1.set_index(['STNAME', 'CTYNAME'], inplace=True)
df.rename(columns={"ESTIMATESBASE2010": "Estimates Base 2010"})
'''
df_2 = (df.where(df['SUMLEV']==50)
.dropna()\
.set_index(['STNAME','CTYNAME'])
.rename(columns={"ESTIMATESBASE2010": "Estimates Base 2010"}))
print(df_2)
'''
SUMLEV ... RNETMIG2015
STNAME CTYNAME ...
Alabama Autauga County 50.0 ... -2.187333
Baldwin County 50.0 ... 18.293499
Barbour County 50.0 ... -10.543299
Bibb County 50.0 ... 1.107861
Blount County 50.0 ... -0.884411
Bullock County 50.0 ... -13.193961
Butler County 50.0 ... -6.430868
Calhoun County 50.0 ... -2.806406
Chambers County 50.0 ... 2.346901
Cherokee County 50.0 ... -2.931307
Chilton County 50.0 ... -1.752709
Choctaw County 50.0 ... -5.291205
Clarke County 50.0 ... -5.411736
Clay County 50.0 ... 3.986270
Cleburne County 50.0 ... -3.123961
Coffee County 50.0 ... 5.350738
Colbert County 50.0 ... 0.588052
Conecuh County 50.0 ... 2.684140
Coosa County 50.0 ... -5.387581
Covington County 50.0 ... -0.264121
Crenshaw County 50.0 ... 5.087600
Cullman County 50.0 ... 8.417777
Dale County 50.0 ... -1.998668
Dallas County 50.0 ... -14.711389
DeKalb County 50.0 ... 0.956790
Elmore County 50.0 ... 2.757093
Escambia County 50.0 ... 1.243830
Etowah County 50.0 ... -1.588308
Fayette County 50.0 ... -2.857058
Franklin County 50.0 ... 0.442422
... ... ... ...
Wisconsin Washburn County 50.0 ... -4.995197
Washington County 50.0 ... 0.059931
Waukesha County 50.0 ... 0.593567
Waupaca County 50.0 ... -0.173022
Waushara County 50.0 ... -4.024395
Winnebago County 50.0 ... -3.685304
Wood County 50.0 ... -3.414223
Wyoming Albany County 50.0 ... -5.166460
Big Horn County 50.0 ... 5.513554
Campbell County 50.0 ... 10.978525
Carbon County 50.0 ... -22.600668
Converse County 50.0 ... 1.126443
Crook County 50.0 ... 19.309219
Fremont County 50.0 ... -13.352750
Goshen County 50.0 ... -10.635133
Hot Springs County 50.0 ... -5.663940
Johnson County 50.0 ... 0.933652
Laramie County 50.0 ... 1.673640
Lincoln County 50.0 ... 1.662823
Natrona County 50.0 ... 0.793743
Niobrara County 50.0 ... 7.492114
Park County 50.0 ... 0.856839
Platte County 50.0 ... 4.207414
Sheridan County 50.0 ... -2.198937
Sublette County 50.0 ... -20.664059
Sweetwater County 50.0 ... -14.070195
Teton County 50.0 ... 1.520747
Uinta County 50.0 ... -12.606351
Washakie County 50.0 ... 1.441961
Weston County 50.0 ... 6.935294
[3142 rows x 98 columns]
'''
import numpy as np
# POPESTIMATE (인수 추정) 2010~2015년도 데이터 중
# 가장 작은 값과 가장 작은 값을 반환하는 메서드
# 전 행들을 걸쳐서 진행되므로 반환값은 Series
def min_max(row):
data = row[['POPESTIMATE2010',
'POPESTIMATE2011',
'POPESTIMATE2012',
'POPESTIMATE2013',
'POPESTIMATE2014',
'POPESTIMATE2015']]
return pd.Series({'min':np.min(data), 'max':np.max(data)})
# apply 메서드를 통해 df의 전 row에 대해서 min_max 메서드를 적용할 수 있다.
df_3 = df.apply(min_max, axis=1)
print(df_3)
'''
min max
0 4785161 4858979
1 54660 55347
2 183193 203709
3 26489 27341
4 22512 22861
5 57373 57776
6 10606 10887
7 20154 20944
8 115620 118437
9 33993 34153
10 25859 26084
11 43665 43943
12 13170 13841
13 24675 25767
14 13456 13880
15 14921 15072
16 50177 51211
17 54354 54514
18 12662 13208
19 10724 11758
20 37796 38060
21 13853 13963
22 80374 82005
23 49501 50358
24 41131 43803
25 70869 71387
26 79465 81468
27 37784 38309
28 103057 104442
29 16759 17231
... ... ...
3163 131967 133674
3164 390076 396488
3165 51945 52422
3166 24033 24581
3167 167059 169639
3168 73435 74807
3169 564516 586107
3170 36428 37956
3171 11672 12022
3172 46244 49220
3173 15559 15856
3174 13728 14343
3175 7114 7444
3176 40222 41129
3177 13383 13666
3178 4741 4846
3179 8552 8636
3180 92271 97121
3181 17943 18722
3182 75472 82178
3183 2475 2548
3184 28259 29237
3185 8678 8812
3186 29146 30020
3187 9899 10418
3188 43593 45162
3189 21297 23125
3190 20822 21102
3191 8316 8545
3192 7065 7234
'''
# POPESTIMATE (인수 추정) 2010~2015년도 데이터 중
# 가장 작은 값과 가장 작은 값을 의미하는 max와 min 컬럼을 만들고
# 그 값을 각각의 행들에 대해 max와 min 컬럼에 값을 넣고 그 행을 반환
def min_max(row):
data = row[['POPESTIMATE2010',
'POPESTIMATE2011',
'POPESTIMATE2012',
'POPESTIMATE2013',
'POPESTIMATE2014',
'POPESTIMATE2015']]
row['max'] = np.max(data)
row['min'] = np.min(data)
return row
df_4 = df.apply(min_max, axis=1)
print(df_4.columns.values)
'''
['SUMLEV' 'REGION' 'DIVISION' 'STATE' 'COUNTY' 'STNAME' 'CTYNAME'
'CENSUS2010POP' 'ESTIMATESBASE2010' 'POPESTIMATE2010' 'POPESTIMATE2011'
'POPESTIMATE2012' 'POPESTIMATE2013' 'POPESTIMATE2014' 'POPESTIMATE2015'
'NPOPCHG_2010' 'NPOPCHG_2011' 'NPOPCHG_2012' 'NPOPCHG_2013'
'NPOPCHG_2014' 'NPOPCHG_2015' 'BIRTHS2010' 'BIRTHS2011' 'BIRTHS2012'
'BIRTHS2013' 'BIRTHS2014' 'BIRTHS2015' 'DEATHS2010' 'DEATHS2011'
'DEATHS2012' 'DEATHS2013' 'DEATHS2014' 'DEATHS2015' 'NATURALINC2010'
'NATURALINC2011' 'NATURALINC2012' 'NATURALINC2013' 'NATURALINC2014'
'NATURALINC2015' 'INTERNATIONALMIG2010' 'INTERNATIONALMIG2011'
'INTERNATIONALMIG2012' 'INTERNATIONALMIG2013' 'INTERNATIONALMIG2014'
'INTERNATIONALMIG2015' 'DOMESTICMIG2010' 'DOMESTICMIG2011'
'DOMESTICMIG2012' 'DOMESTICMIG2013' 'DOMESTICMIG2014' 'DOMESTICMIG2015'
'NETMIG2010' 'NETMIG2011' 'NETMIG2012' 'NETMIG2013' 'NETMIG2014'
'NETMIG2015' 'RESIDUAL2010' 'RESIDUAL2011' 'RESIDUAL2012' 'RESIDUAL2013'
'RESIDUAL2014' 'RESIDUAL2015' 'GQESTIMATESBASE2010' 'GQESTIMATES2010'
'GQESTIMATES2011' 'GQESTIMATES2012' 'GQESTIMATES2013' 'GQESTIMATES2014'
'GQESTIMATES2015' 'RBIRTH2011' 'RBIRTH2012' 'RBIRTH2013' 'RBIRTH2014'
'RBIRTH2015' 'RDEATH2011' 'RDEATH2012' 'RDEATH2013' 'RDEATH2014'
'RDEATH2015' 'RNATURALINC2011' 'RNATURALINC2012' 'RNATURALINC2013'
'RNATURALINC2014' 'RNATURALINC2015' 'RINTERNATIONALMIG2011'
'RINTERNATIONALMIG2012' 'RINTERNATIONALMIG2013' 'RINTERNATIONALMIG2014'
'RINTERNATIONALMIG2015' 'RDOMESTICMIG2011' 'RDOMESTICMIG2012'
'RDOMESTICMIG2013' 'RDOMESTICMIG2014' 'RDOMESTICMIG2015' 'RNETMIG2011'
'RNETMIG2012' 'RNETMIG2013' 'RNETMIG2014' 'RNETMIG2015' 'max' 'min'] <= max, min추가
'''
print(df_4["max"])
'''
SUMLEV ... RNETMIG2015
STNAME CTYNAME ...
Alabama Autauga County 50.0 ... -2.187333
Baldwin County 50.0 ... 18.293499
Barbour County 50.0 ... -10.543299
Bibb County 50.0 ... 1.107861
Blount County 50.0 ... -0.884411
Bullock County 50.0 ... -13.193961
Butler County 50.0 ... -6.430868
Calhoun County 50.0 ... -2.806406
Chambers County 50.0 ... 2.346901
Cherokee County 50.0 ... -2.931307
Chilton County 50.0 ... -1.752709
Choctaw County 50.0 ... -5.291205
Clarke County 50.0 ... -5.411736
Clay County 50.0 ... 3.986270
Cleburne County 50.0 ... -3.123961
Coffee County 50.0 ... 5.350738
Colbert County 50.0 ... 0.588052
Conecuh County 50.0 ... 2.684140
Coosa County 50.0 ... -5.387581
Covington County 50.0 ... -0.264121
Crenshaw County 50.0 ... 5.087600
Cullman County 50.0 ... 8.417777
Dale County 50.0 ... -1.998668
Dallas County 50.0 ... -14.711389
DeKalb County 50.0 ... 0.956790
Elmore County 50.0 ... 2.757093
Escambia County 50.0 ... 1.243830
Etowah County 50.0 ... -1.588308
Fayette County 50.0 ... -2.857058
Franklin County 50.0 ... 0.442422
... ... ... ...
Wisconsin Washburn County 50.0 ... -4.995197
Washington County 50.0 ... 0.059931
Waukesha County 50.0 ... 0.593567
Waupaca County 50.0 ... -0.173022
Waushara County 50.0 ... -4.024395
Winnebago County 50.0 ... -3.685304
Wood County 50.0 ... -3.414223
Wyoming Albany County 50.0 ... -5.166460
Big Horn County 50.0 ... 5.513554
Campbell County 50.0 ... 10.978525
Carbon County 50.0 ... -22.600668
Converse County 50.0 ... 1.126443
Crook County 50.0 ... 19.309219
Fremont County 50.0 ... -13.352750
Goshen County 50.0 ... -10.635133
Hot Springs County 50.0 ... -5.663940
Johnson County 50.0 ... 0.933652
Laramie County 50.0 ... 1.673640
Lincoln County 50.0 ... 1.662823
Natrona County 50.0 ... 0.793743
Niobrara County 50.0 ... 7.492114
Park County 50.0 ... 0.856839
Platte County 50.0 ... 4.207414
Sheridan County 50.0 ... -2.198937
Sublette County 50.0 ... -20.664059
Sweetwater County 50.0 ... -14.070195
Teton County 50.0 ... 1.520747
Uinta County 50.0 ... -12.606351
Washakie County 50.0 ... 1.441961
Weston County 50.0 ... 6.935294
[3142 rows x 98 columns]
'''
# lambda로 익명함수를 만들어서 apply에 적용가능
# lambda에 들어가는 입력값은 각각의 행들이 들어감
rows = ['POPESTIMATE2010',
'POPESTIMATE2011',
'POPESTIMATE2012',
'POPESTIMATE2013',
'POPESTIMATE2014',
'POPESTIMATE2015']
df_5 = df.apply(lambda x: np.max(x[rows]), axis=1)
print(df_5)
'''
SUMLEV ... RNETMIG2015
STNAME CTYNAME ...
Alabama Autauga County 50.0 ... -2.187333
Baldwin County 50.0 ... 18.293499
Barbour County 50.0 ... -10.543299
Bibb County 50.0 ... 1.107861
Blount County 50.0 ... -0.884411
Bullock County 50.0 ... -13.193961
Butler County 50.0 ... -6.430868
Calhoun County 50.0 ... -2.806406
Chambers County 50.0 ... 2.346901
Cherokee County 50.0 ... -2.931307
Chilton County 50.0 ... -1.752709
Choctaw County 50.0 ... -5.291205
Clarke County 50.0 ... -5.411736
Clay County 50.0 ... 3.986270
Cleburne County 50.0 ... -3.123961
Coffee County 50.0 ... 5.350738
Colbert County 50.0 ... 0.588052
Conecuh County 50.0 ... 2.684140
Coosa County 50.0 ... -5.387581
Covington County 50.0 ... -0.264121
Crenshaw County 50.0 ... 5.087600
Cullman County 50.0 ... 8.417777
Dale County 50.0 ... -1.998668
Dallas County 50.0 ... -14.711389
DeKalb County 50.0 ... 0.956790
Elmore County 50.0 ... 2.757093
Escambia County 50.0 ... 1.243830
Etowah County 50.0 ... -1.588308
Fayette County 50.0 ... -2.857058
Franklin County 50.0 ... 0.442422
... ... ... ...
Wisconsin Washburn County 50.0 ... -4.995197
Washington County 50.0 ... 0.059931
Waukesha County 50.0 ... 0.593567
Waupaca County 50.0 ... -0.173022
Waushara County 50.0 ... -4.024395
Winnebago County 50.0 ... -3.685304
Wood County 50.0 ... -3.414223
Wyoming Albany County 50.0 ... -5.166460
Big Horn County 50.0 ... 5.513554
Campbell County 50.0 ... 10.978525
Carbon County 50.0 ... -22.600668
Converse County 50.0 ... 1.126443
Crook County 50.0 ... 19.309219
Fremont County 50.0 ... -13.352750
Goshen County 50.0 ... -10.635133
Hot Springs County 50.0 ... -5.663940
Johnson County 50.0 ... 0.933652
Laramie County 50.0 ... 1.673640
Lincoln County 50.0 ... 1.662823
Natrona County 50.0 ... 0.793743
Niobrara County 50.0 ... 7.492114
Park County 50.0 ... 0.856839
Platte County 50.0 ... 4.207414
Sheridan County 50.0 ... -2.198937
Sublette County 50.0 ... -20.664059
Sweetwater County 50.0 ... -14.070195
Teton County 50.0 ... 1.520747
Uinta County 50.0 ... -12.606351
Washakie County 50.0 ... 1.441961
Weston County 50.0 ... 6.935294
[3142 rows x 98 columns]
'''
참고자료 : https://www.coursera.org/learn/python-data-analysis
출처: https://engkimbs.tistory.com/690?category=763908 [새로비]
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