Summer Program: Python Programming _ Day2 Quiz

Author

김보람

Published

July 25, 2023

—

title: “Summer Program: Python Programming _ Day2 Quiz” author: “김보람” date: “07/25/2023”

lesson 7

import pandas as pd

1

from IPython.core.display import HTML 
HTML('<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>-2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>-3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>-4</td>\n    </tr>\n  </tbody>\n</table>')
A B
0 1 -2
1 2 -3
2 3 -4 
df = pd.DataFrame({'A':[1,2,3],'B':[-2,-3,-4]})
df
A B
0 1 -2
1 2 -3
2 3 -4

2

df.columns = ['X1','X2']
df
X1 X2
0 1 -2
1 2 -3
2 3 -4

3

df = pd.DataFrame(np.random.normal(size=(100,5)),columns=list('ABCDE'))
df
A B C D E
0 1.463910 -0.189044 -1.154230 -1.063342 1.027457
1 0.821139 -0.511394 -3.710588 1.160104 -0.107635
2 -0.732671 -1.033050 -0.295740 0.282198 -0.215732
3 1.598415 -2.190977 -0.803828 -0.956396 -0.174917
4 -0.185002 1.154373 -0.236004 -2.128459 -1.623930
... ... ... ... ... ...
95 -1.085273 0.428166 0.744181 0.533127 -0.263671
96 -0.528071 1.460818 -0.773037 -0.355642 1.661265
97 -0.044476 -0.269066 0.018526 -0.318183 0.990264
98 -0.324435 0.583649 1.500259 0.257866 -2.036244
99 2.104528 0.380031 -0.353625 -1.402250 -1.109209

100 rows × 5 columns

df[['B','D']]
B D
0 -0.189044 -1.063342
1 -0.511394 1.160104
2 -1.033050 0.282198
3 -2.190977 -0.956396
4 1.154373 -2.128459
... ... ...
95 0.428166 0.533127
96 1.460818 -0.355642
97 -0.269066 -0.318183
98 0.583649 0.257866
99 0.380031 -1.402250

100 rows × 2 columns

4

df[-10:]
A B C D E
90 -0.167761 -0.664358 1.867122 -0.177171 0.197525
91 0.377890 -1.048871 2.145238 0.424379 0.525715
92 0.696097 -0.948450 0.611477 1.591229 0.091939
93 -0.419354 1.160804 -0.993989 0.987491 -1.580077
94 1.178698 -0.141507 -0.717452 1.447968 0.585297
95 -1.085273 0.428166 0.744181 0.533127 -0.263671
96 -0.528071 1.460818 -0.773037 -0.355642 1.661265
97 -0.044476 -0.269066 0.018526 -0.318183 0.990264
98 -0.324435 0.583649 1.500259 0.257866 -2.036244
99 2.104528 0.380031 -0.353625 -1.402250 -1.109209

5

df[['A','B']][:10]
A B
0 1.463910 -0.189044
1 0.821139 -0.511394
2 -0.732671 -1.033050
3 1.598415 -2.190977
4 -0.185002 1.154373
5 1.410994 -1.352321
6 0.547868 0.647899
7 -2.993996 -0.656354
8 0.016301 0.654041
9 -0.973628 0.756796 
df.loc[:,['A','B']].iloc[:10]
A B
0 1.463910 -0.189044
1 0.821139 -0.511394
2 -0.732671 -1.033050
3 1.598415 -2.190977
4 -0.185002 1.154373
5 1.410994 -1.352321
6 0.547868 0.647899
7 -2.993996 -0.656354
8 0.016301 0.654041
9 -0.973628 0.756796

6

df=pd.read_csv('https://raw.githubusercontent.com/PacktPublishing/Pandas-Cookbook/master/data/movie.csv')
df
color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
0 Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
1 Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
2 Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
3 Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
4 NaN Doug Walker NaN NaN 131.0 NaN Rob Walker 131.0 NaN Documentary ... NaN NaN NaN NaN NaN NaN 12.0 7.1 NaN 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4911 Color Scott Smith 1.0 87.0 2.0 318.0 Daphne Zuniga 637.0 NaN Comedy|Drama ... 6.0 English Canada NaN NaN 2013.0 470.0 7.7 NaN 84
4912 Color NaN 43.0 43.0 NaN 319.0 Valorie Curry 841.0 NaN Crime|Drama|Mystery|Thriller ... 359.0 English USA TV-14 NaN NaN 593.0 7.5 16.00 32000
4913 Color Benjamin Roberds 13.0 76.0 0.0 0.0 Maxwell Moody 0.0 NaN Drama|Horror|Thriller ... 3.0 English USA NaN 1400.0 2013.0 0.0 6.3 NaN 16
4914 Color Daniel Hsia 14.0 100.0 0.0 489.0 Daniel Henney 946.0 10443.0 Comedy|Drama|Romance ... 9.0 English USA PG-13 NaN 2012.0 719.0 6.3 2.35 660
4915 Color Jon Gunn 43.0 90.0 16.0 16.0 Brian Herzlinger 86.0 85222.0 Documentary ... 84.0 English USA PG 1100.0 2004.0 23.0 6.6 1.85 456

4916 rows × 28 columns

len(df.columns)
28

7

index = df.columns
index
Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',
       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',
       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',
       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',
       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',
       'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',
       'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',
       'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],
      dtype='object')
[l for l in index if l[0]=='c' or l[0]=='d']
['color',
 'director_name',
 'duration',
 'director_facebook_likes',
 'cast_total_facebook_likes',
 'country',
 'content_rating']
len([l for l in index if l[0]=='c' or l[0]=='d'])
7

8

[l for l in df.columns if 'actor' in l]
['actor_3_facebook_likes',
 'actor_2_name',
 'actor_1_facebook_likes',
 'actor_1_name',
 'actor_3_name',
 'actor_2_facebook_likes']
len([l for l in df.columns if 'actor' in l])
6

9

df.loc[:, ['actor' in l for l in df.columns]]
actor_3_facebook_likes actor_2_name actor_1_facebook_likes actor_1_name actor_3_name actor_2_facebook_likes
0 855.0 Joel David Moore 1000.0 CCH Pounder Wes Studi 936.0
1 1000.0 Orlando Bloom 40000.0 Johnny Depp Jack Davenport 5000.0
2 161.0 Rory Kinnear 11000.0 Christoph Waltz Stephanie Sigman 393.0
3 23000.0 Christian Bale 27000.0 Tom Hardy Joseph Gordon-Levitt 23000.0
4 NaN Rob Walker 131.0 Doug Walker NaN 12.0
... ... ... ... ... ... ...
4911 318.0 Daphne Zuniga 637.0 Eric Mabius Crystal Lowe 470.0
4912 319.0 Valorie Curry 841.0 Natalie Zea Sam Underwood 593.0
4913 0.0 Maxwell Moody 0.0 Eva Boehnke David Chandler 0.0
4914 489.0 Daniel Henney 946.0 Alan Ruck Eliza Coupe 719.0
4915 16.0 Brian Herzlinger 86.0 John August Jon Gunn 23.0

4916 rows × 6 columns

lesson 9

np.random.seed(20230426)
day = ['2023-04-24(Mon)','2023-04-25(Tue)','2023-04-26(Wed)','2023-04-27(Thu)','2023-04-28(Fri)',
       '2023-05-01(Mon)','2023-05-02(Tue)','2023-05-03(Wed)','2023-05-04(Thu)','2023-05-05(Fri)',
       '2023-05-08(Mon)','2023-05-09(Tue)','2023-05-10(Wed)','2023-05-11(Thu)','2023-05-12(Fri)']
hours1 = np.random.randn(15).cumsum()*2
hours1 = hours1 - hours1.min() +1
hours2 = np.random.randn(15).cumsum()*2
hours2 = hours2 - hours2.min() +1

df = pd.DataFrame({'hours(R)':hours1, 'hours(Python)':hours2},index=day)
df
hours(R) hours(Python)
2023-04-24(Mon) 11.064829 9.254671
2023-04-25(Tue) 9.790750 7.327548
2023-04-26(Wed) 5.993362 9.185495
2023-04-27(Thu) 7.542498 12.525569
2023-04-28(Fri) 8.598600 10.906909
2023-05-01(Mon) 6.933549 9.865538
2023-05-02(Tue) 6.456987 11.081043
2023-05-03(Wed) 4.976548 10.240239
2023-05-04(Thu) 6.021139 5.822405
2023-05-05(Fri) 1.851839 5.522484
2023-05-08(Mon) 1.000000 4.319094
2023-05-09(Tue) 1.350073 1.000000
2023-05-10(Wed) 3.138700 2.633662
2023-05-11(Thu) 3.153756 4.870860
2023-05-12(Fri) 1.353976 1.785441

1

index = [l.replace(')','').split('(') for l in df.index]
index
[['2023-04-24', 'Mon'],
 ['2023-04-25', 'Tue'],
 ['2023-04-26', 'Wed'],
 ['2023-04-27', 'Thu'],
 ['2023-04-28', 'Fri'],
 ['2023-05-01', 'Mon'],
 ['2023-05-02', 'Tue'],
 ['2023-05-03', 'Wed'],
 ['2023-05-04', 'Thu'],
 ['2023-05-05', 'Fri'],
 ['2023-05-08', 'Mon'],
 ['2023-05-09', 'Tue'],
 ['2023-05-10', 'Wed'],
 ['2023-05-11', 'Thu'],
 ['2023-05-12', 'Fri']]
df.assign(day = [day for day, _ in index], weekday = [weekday for _, weekday in index])
hours(R) hours(Python) day weekday
2023-04-24(Mon) 11.064829 9.254671 2023-04-24 Mon
2023-04-25(Tue) 9.790750 7.327548 2023-04-25 Tue
2023-04-26(Wed) 5.993362 9.185495 2023-04-26 Wed
2023-04-27(Thu) 7.542498 12.525569 2023-04-27 Thu
2023-04-28(Fri) 8.598600 10.906909 2023-04-28 Fri
2023-05-01(Mon) 6.933549 9.865538 2023-05-01 Mon
2023-05-02(Tue) 6.456987 11.081043 2023-05-02 Tue
2023-05-03(Wed) 4.976548 10.240239 2023-05-03 Wed
2023-05-04(Thu) 6.021139 5.822405 2023-05-04 Thu
2023-05-05(Fri) 1.851839 5.522484 2023-05-05 Fri
2023-05-08(Mon) 1.000000 4.319094 2023-05-08 Mon
2023-05-09(Tue) 1.350073 1.000000 2023-05-09 Tue
2023-05-10(Wed) 3.138700 2.633662 2023-05-10 Wed
2023-05-11(Thu) 3.153756 4.870860 2023-05-11 Thu
2023-05-12(Fri) 1.353976 1.785441 2023-05-12 Fri 
df = df.assign(day = [day for day, _ in index], weekday = [weekday for _, weekday in index]).reset_index().loc[:,'hours(R)':]
df
hours(R) hours(Python) day weekday
0 11.064829 9.254671 2023-04-24 Mon
1 9.790750 7.327548 2023-04-25 Tue
2 5.993362 9.185495 2023-04-26 Wed
3 7.542498 12.525569 2023-04-27 Thu
4 8.598600 10.906909 2023-04-28 Fri
5 6.933549 9.865538 2023-05-01 Mon
6 6.456987 11.081043 2023-05-02 Tue
7 4.976548 10.240239 2023-05-03 Wed
8 6.021139 5.822405 2023-05-04 Thu
9 1.851839 5.522484 2023-05-05 Fri
10 1.000000 4.319094 2023-05-08 Mon
11 1.350073 1.000000 2023-05-09 Tue
12 3.138700 2.633662 2023-05-10 Wed
13 3.153756 4.870860 2023-05-11 Thu
14 1.353976 1.785441 2023-05-12 Fri

2

df.query('day < "2023-05-01"')['hours(R)'].sum()
42.99003889835529
df.query('day < "2023-05-01"')['hours(Python)'].sum()
49.20019054928582
  • python

3

{s:df.loc[df.weekday == s, 'hours(R)'].sum() for s in set(df.weekday)}
{'Thu': 16.717393020928853,
 'Mon': 18.99837797631909,
 'Wed': 14.10860912003022,
 'Fri': 11.804415159359687,
 'Tue': 17.597810683605076}

4

{s:df.loc[df.weekday == s, 'hours(R)'].sum() for s in set(df.weekday)}
{'Thu': 16.717393020928853,
 'Mon': 18.99837797631909,
 'Wed': 14.10860912003022,
 'Fri': 11.804415159359687,
 'Tue': 17.597810683605076}
{s:df.loc[df.weekday == s, 'hours(Python)'].sum() for s in set(df.weekday)}
{'Thu': 23.21883427607518,
 'Mon': 23.43930191418699,
 'Wed': 22.05939511920843,
 'Fri': 18.214832977059253,
 'Tue': 19.408591083196185}
{s:df.loc[df.weekday == s,'hours(R)',].sum()+df.loc[df.weekday == s,'hours(Python)'].sum() for s in set(df.weekday)}
{'Thu': 39.93622729700404,
 'Mon': 42.43767989050608,
 'Wed': 36.16800423923865,
 'Fri': 30.019248136418938,
 'Tue': 37.00640176680126}

5

import matplotlib.pyplot as plt
y = df['hours(R)'] - df['hours(Python)']
plt.plot(y,'--o')

lesson 10

df = pd.read_csv('https://raw.githubusercontent.com/guebin/DV2022/master/posts/FIFA23_official_data.csv')
df.head(n=3)
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Real Face Position Joined Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... Yes <span class="pos pos28">SUB Jul 1, 2018 NaN 2026 189cm 82kg €157M 8.0 NaN
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... Yes <span class="pos pos15">LCM Jan 30, 2020 NaN 2026 179cm 69kg €155M 8.0 NaN
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... No <span class="pos pos7">LB Sep 14, 2020 NaN 2024 172cm 69kg €97.7M 19.0 NaN

3 rows × 29 columns

1

df.Position
0        <span class="pos pos28">SUB
1        <span class="pos pos15">LCM
2          <span class="pos pos7">LB
3        <span class="pos pos13">RCM
4        <span class="pos pos13">RCM
                    ...             
17655    <span class="pos pos29">RES
17656    <span class="pos pos29">RES
17657    <span class="pos pos29">RES
17658    <span class="pos pos29">RES
17659    <span class="pos pos29">RES
Name: Position, Length: 17660, dtype: object
df.loc[:,'Position']
0        <span class="pos pos28">SUB
1        <span class="pos pos15">LCM
2          <span class="pos pos7">LB
3        <span class="pos pos13">RCM
4        <span class="pos pos13">RCM
                    ...             
17655    <span class="pos pos29">RES
17656    <span class="pos pos29">RES
17657    <span class="pos pos29">RES
17658    <span class="pos pos29">RES
17659    <span class="pos pos29">RES
Name: Position, Length: 17660, dtype: object
s = df["Position"]
s
0        <span class="pos pos28">SUB
1        <span class="pos pos15">LCM
2          <span class="pos pos7">LB
3        <span class="pos pos13">RCM
4        <span class="pos pos13">RCM
                    ...             
17655    <span class="pos pos29">RES
17656    <span class="pos pos29">RES
17657    <span class="pos pos29">RES
17658    <span class="pos pos29">RES
17659    <span class="pos pos29">RES
Name: Position, Length: 17660, dtype: object

2

s = s.dropna()
s
0        <span class="pos pos28">SUB
1        <span class="pos pos15">LCM
2          <span class="pos pos7">LB
3        <span class="pos pos13">RCM
4        <span class="pos pos13">RCM
                    ...             
17655    <span class="pos pos29">RES
17656    <span class="pos pos29">RES
17657    <span class="pos pos29">RES
17658    <span class="pos pos29">RES
17659    <span class="pos pos29">RES
Name: Position, Length: 17625, dtype: object
'<span class="pos pos28">SUB'

3

position_dict = {
    'GOALKEEPER':['GK'],
    'DEFENDER':['CB','RCB','LCB','RB','LB','RWB','LWB'],
    'MIDFIELDER':['CM','RCM','LCM','CDM','RDM','LDM','CAM','RAM','LAM','RM','LM'],
    'FORWARD':['ST','CF','RF','LF','RW','LW','RS','LS'],
    'SUB':['SUB'],
    'RES':['RES']
}
lst = list(map(lambda x: x.split('>')[-1],s))
s.apply(lambda x: x.split('>')[-1])
0        SUB
1        LCM
2         LB
3        RCM
4        RCM
        ... 
17655    RES
17656    RES
17657    RES
17658    RES
17659    RES
Name: Position, Length: 17625, dtype: object
[(k,v) for k,v in position_dict.items()]
[('GOALKEEPER', ['GK']),
 ('DEFENDER', ['CB', 'RCB', 'LCB', 'RB', 'LB', 'RWB', 'LWB']),
 ('MIDFIELDER',
  ['CM', 'RCM', 'LCM', 'CDM', 'RDM', 'LDM', 'CAM', 'RAM', 'LAM', 'RM', 'LM']),
 ('FORWARD', ['ST', 'CF', 'RF', 'LF', 'RW', 'LW', 'RS', 'LS']),
 ('SUB', ['SUB']),
 ('RES', ['RES'])]
[k for l in s.apply(lambda x: x.split('>')[-1]) for k,v in  position_dict.items() if l in v]
['SUB',
 'MIDFIELDER',
 'DEFENDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'MIDFIELDER',
 'SUB',
 'DEFENDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'SUB',
 'SUB',
 'MIDFIELDER',
 'FORWARD',
 'DEFENDER',
 'MIDFIELDER',
 'DEFENDER',
 'FORWARD',
 'DEFENDER',
 'MIDFIELDER',
 'FORWARD',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'DEFENDER',
 'SUB',
 'SUB',
 'MIDFIELDER',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'SUB',
 'DEFENDER',
 'FORWARD',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'DEFENDER',
 'DEFENDER',
 'SUB',
 'FORWARD',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'FORWARD',
 'MIDFIELDER',
 'DEFENDER',
 'MIDFIELDER',
 'FORWARD',
 'MIDFIELDER',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
 'SUB',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'MIDFIELDER',
 'SUB',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'RES',
 'SUB',
 'FORWARD',
 'MIDFIELDER',
 'DEFENDER',
 'FORWARD',
 'MIDFIELDER',
 'SUB',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'DEFENDER',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'RES',
 'DEFENDER',
 'DEFENDER',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
 'MIDFIELDER',
 'RES',
 'SUB',
 'DEFENDER',
 'FORWARD',
 'DEFENDER',
 'FORWARD',
 'FORWARD',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'DEFENDER',
 'SUB',
 'RES',
 'MIDFIELDER',
 'FORWARD',
 'SUB',
 'SUB',
 'SUB',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'RES',
 'MIDFIELDER',
 'DEFENDER',
 'MIDFIELDER',
 'FORWARD',
 'MIDFIELDER',
 'DEFENDER',
 'MIDFIELDER',
 'DEFENDER',
 'FORWARD',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
 'DEFENDER',
 'SUB',
 'FORWARD',
 'MIDFIELDER',
 'SUB',
 'SUB',
 'DEFENDER',
 'SUB',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'DEFENDER',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'DEFENDER',
 'MIDFIELDER',
 'DEFENDER',
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 'MIDFIELDER',
 'SUB',
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 'SUB',
 'SUB',
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 'MIDFIELDER',
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 'SUB',
 'MIDFIELDER',
 'MIDFIELDER',
 'SUB',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'DEFENDER',
 'MIDFIELDER',
 'FORWARD',
 'MIDFIELDER',
 'SUB',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
 'SUB',
 'SUB',
 'SUB',
 'FORWARD',
 'MIDFIELDER',
 'SUB',
 'DEFENDER',
 'FORWARD',
 'MIDFIELDER',
 'MIDFIELDER',
 'MIDFIELDER',
 'SUB',
 'FORWARD',
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 'MIDFIELDER',
 'FORWARD',
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 'MIDFIELDER',
 'MIDFIELDER',
 'SUB',
 ...]
# list(map(lambda l : [k for l in s for k,v in position_dict.items() if l in v],s))

4

df.Age
0        27
1        27
2        30
3        31
4        25
         ..
17655    19
17656    17
17657    25
17658    18
17659    20
Name: Age, Length: 17660, dtype: int64
df.Age.mean()
23.127746319365798
list(map(lambda x: 'OB' if x>df.Age.mean() else 'YB', df.Age))
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or

df.Age.apply(

5

BMI = 키 / 몸무게^2

df.Height.apply( x[:-2])
df.Height
0        189cm
1        179cm
2        172cm
3        181cm
4        172cm
         ...  
17655    190cm
17656    195cm
17657    190cm
17658    187cm
17659    186cm
Name: Height, Length: 17660, dtype: object
df.Weight
0        82kg
1        69kg
2        69kg
3        70kg
4        68kg
         ... 
17655    78kg
17656    84kg
17657    82kg
17658    79kg
17659    78kg
Name: Weight, Length: 17660, dtype: object
_df = df.loc[:,'Height':'Weight'].applymap(lambda x: int(x[:-2])).eval('BMI=Height/Weight**2')
_df
Height Weight BMI
0 189 82 0.028108
1 179 69 0.037597
2 172 69 0.036127
3 181 70 0.036939
4 172 68 0.037197
... ... ... ...
17655 190 78 0.031229
17656 195 84 0.027636
17657 190 82 0.028257
17658 187 79 0.029963
17659 186 78 0.030572

17660 rows × 3 columns

6

_df.BMI.mean()
0.03343718444596221
list(map(lambda x: 'H' if x>_df.BMI.mean() else 'L', _df.BMI))
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_df.BMI.apply(lambda x: 'H' if x>_df.BMI.mean() else 'L')
0        L
1        H
2        H
3        H
4        H
        ..
17655    L
17656    L
17657    L
17658    L
17659    L
Name: BMI, Length: 17660, dtype: object

lesson 11

lesson

_category = ['A']*5+['B']*5
_value = np.concatenate([np.random.randn(5), np.random.randn(5)+10])
df = pd.DataFrame({'category':_category, 'value':_value})
df
category value
0 A 1.086803
1 A 0.448341
2 A 0.575944
3 A 0.277854
4 A -1.924633
5 B 9.844358
6 B 9.819660
7 B 9.425310
8 B 10.191885
9 B 11.187810 
df.groupby("category").mean()
value
category
A 0.092862
B 10.093805 
df.groupby("category").aggregate(np.mean)
value
category
A 0.092862
B 10.093805

flights data

df=pd.read_csv('https://raw.githubusercontent.com/PacktPublishing/Pandas-Cookbook/master/data/flights.csv')
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 58492 entries, 0 to 58491
Data columns (total 14 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   MONTH      58492 non-null  int64  
 1   DAY        58492 non-null  int64  
 2   WEEKDAY    58492 non-null  int64  
 3   AIRLINE    58492 non-null  object 
 4   ORG_AIR    58492 non-null  object 
 5   DEST_AIR   58492 non-null  object 
 6   SCHED_DEP  58492 non-null  int64  
 7   DEP_DELAY  57659 non-null  float64
 8   AIR_TIME   57474 non-null  float64
 9   DIST       58492 non-null  int64  
 10  SCHED_ARR  58492 non-null  int64  
 11  ARR_DELAY  57474 non-null  float64
 12  DIVERTED   58492 non-null  int64  
 13  CANCELLED  58492 non-null  int64  
dtypes: float64(3), int64(8), object(3)
memory usage: 6.2+ MB

- 예제1: 항공사(AIRLINE)별로 도착지연시간의(ARR_DELAY)의 평균을 구하라.

df.groupby("AIRLINE")["ARR_DELAY"].mean()
AIRLINE
AA     5.542661
AS    -0.833333
B6     8.692593
DL     0.339691
EV     7.034580
F9    13.630651
HA     4.972973
MQ     6.860591
NK    18.436070
OO     7.593463
UA     7.765755
US     1.681105
VX     5.348884
WN     6.397353
Name: ARR_DELAY, dtype: float64
df.groupby("AIRLINE").aggregate({'ARR_DELAY':[np.mean,'count']})
ARR_DELAY
mean count
AIRLINE
AA 5.542661 8720
AS -0.833333 768
B6 8.692593 540
DL 0.339691 10539
EV 7.034580 5697
F9 13.630651 1305
HA 4.972973 111
MQ 6.860591 3314
NK 18.436070 1486
OO 7.593463 6425
UA 7.765755 7680
US 1.681105 1593
VX 5.348884 986
WN 6.397353 8310

- 예제2: 항공사(AIRLINE)별로 비행취소건수(CANCELLED)의 합계를 구하라. 취소건수가 가장 높은 두개의 항공사는 어디인가?

df.groupby("AIRLINE").aggregate({'CANCELLED':'sum'}).sort_values(by="CANCELLED", ascending=False)
CANCELLED
AIRLINE
AA 154
MQ 152
EV 146
OO 142
UA 93
WN 93
DL 38
NK 25
US 21
F9 10
VX 6
B6 1
AS 0
HA 0

- 예제3: 항공사(AIRLINE)별로 비행취소율(CANCELLED)을 구하라. 비행취소율이 가장 높은 두개의 항공사는 어디인가?

df.groupby("AIRLINE").aggregate({'CANCELLED':'mean'}).sort_values(by="CANCELLED", ascending=False)
CANCELLED
AIRLINE
MQ 0.043791
EV 0.024923
OO 0.021554
AA 0.017303
NK 0.016491
US 0.013003
UA 0.011935
WN 0.011048
F9 0.007593
VX 0.006042
DL 0.003585
B6 0.001842
AS 0.000000
HA 0.000000

- 예제5: 아래는 운행거리의 요약통계량이다. 운행거리를 구간별로 [-np.inf,391,690,1199,np.inf]와 같이 나눈뒤 비행취소건수와 취소율을 구하여라.

df.DIST.describe()
count    58492.000000
mean       872.900072
std        624.996805
min         67.000000
25%        391.000000
50%        690.000000
75%       1199.000000
max       4502.000000
Name: DIST, dtype: float64
_bin = [-np.inf,391,690,1199,np.inf]
df.assign(DIST2 = pd.cut(df.DIST,_bin)).groupby('DIST2').agg('mean')
MONTH DAY WEEKDAY SCHED_DEP DEP_DELAY AIR_TIME DIST SCHED_ARR ARR_DELAY DIVERTED CANCELLED
DIST2
(-inf, 391.0] 6.120488 15.719539 3.901425 1450.911398 9.414477 44.863971 259.463094 1552.313161 6.320828 0.000814 0.022659
(391.0, 690.0] 6.215019 15.736686 3.929315 1422.562246 10.105660 79.233721 547.309611 1563.083018 5.673505 0.002549 0.013503
(690.0, 1199.0] 6.244324 15.643155 3.934032 1355.024318 12.076761 121.376579 909.723096 1573.918111 6.603278 0.002553 0.013637
(1199.0, inf] 6.304648 15.710334 3.943070 1322.569298 12.077681 219.595209 1794.080413 1507.135670 4.616760 0.003484 0.010313

Quiz

df = pd.read_csv('https://raw.githubusercontent.com/guebin/DV2022/master/posts/HRDataset_v14.csv')
df
Employee_Name EmpID MarriedID MaritalStatusID GenderID EmpStatusID DeptID PerfScoreID FromDiversityJobFairID Salary ... ManagerName ManagerID RecruitmentSource PerformanceScore EngagementSurvey EmpSatisfaction SpecialProjectsCount LastPerformanceReview_Date DaysLateLast30 Absences
0 Adinolfi, Wilson K 10026 0 0 1 1 5 4 0 62506 ... Michael Albert 22.0 LinkedIn Exceeds 4.60 5 0 1/17/2019 0 1
1 Ait Sidi, Karthikeyan 10084 1 1 1 5 3 3 0 104437 ... Simon Roup 4.0 Indeed Fully Meets 4.96 3 6 2/24/2016 0 17
2 Akinkuolie, Sarah 10196 1 1 0 5 5 3 0 64955 ... Kissy Sullivan 20.0 LinkedIn Fully Meets 3.02 3 0 5/15/2012 0 3
3 Alagbe,Trina 10088 1 1 0 1 5 3 0 64991 ... Elijiah Gray 16.0 Indeed Fully Meets 4.84 5 0 1/3/2019 0 15
4 Anderson, Carol 10069 0 2 0 5 5 3 0 50825 ... Webster Butler 39.0 Google Search Fully Meets 5.00 4 0 2/1/2016 0 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
306 Woodson, Jason 10135 0 0 1 1 5 3 0 65893 ... Kissy Sullivan 20.0 LinkedIn Fully Meets 4.07 4 0 2/28/2019 0 13
307 Ybarra, Catherine 10301 0 0 0 5 5 1 0 48513 ... Brannon Miller 12.0 Google Search PIP 3.20 2 0 9/2/2015 5 4
308 Zamora, Jennifer 10010 0 0 0 1 3 4 0 220450 ... Janet King 2.0 Employee Referral Exceeds 4.60 5 6 2/21/2019 0 16
309 Zhou, Julia 10043 0 0 0 1 3 3 0 89292 ... Simon Roup 4.0 Employee Referral Fully Meets 5.00 3 5 2/1/2019 0 11
310 Zima, Colleen 10271 0 4 0 1 5 3 0 45046 ... David Stanley 14.0 LinkedIn Fully Meets 4.50 5 0 1/30/2019 0 2

311 rows × 36 columns

1

df.groupby(by='RaceDesc').agg({'EmpID':'count'})
EmpID
RaceDesc
American Indian or Alaska Native 3
Asian 29
Black or African American 80
Hispanic 1
Two or more races 11
White 187

2

’RaceDesc==White’의 성별(Sex)임금차이는 2000이상이다.

df.groupby(by=['RaceDesc','Sex']).agg({'Salary':'mean'})
Salary
RaceDesc Sex
American Indian or Alaska Native F 63436.500000
M 70545.000000
Asian F 67520.117647
M 69939.416667
Black or African American F 66963.829787
M 85066.121212
Hispanic M 83667.000000
Two or more races F 58068.500000
M 62313.800000
White F 68846.519231
M 65334.132530

3

퇴직한사람(Termd==1)은 모두 104명이며 백인여성의 퇴직자수가 가장 많다.

(df.Termd==1).sum()
104
df.groupby(['RaceDesc','Sex']).agg({'Termd':'sum'})
Termd
RaceDesc Sex
American Indian or Alaska Native F 0
M 0
Asian F 6
M 3
Black or African American F 15
M 14
Hispanic M 0
Two or more races F 2
M 1
White F 37
M 26

4

퇴직한사람중 아시아인의 비율은 10%가 넘지 않는다.

df.groupby(by=['RaceDesc']).agg({'Termd':'sum'})
Termd
RaceDesc
American Indian or Alaska Native 0
Asian 9
Black or African American 29
Hispanic 0
Two or more races 3
White 63 
9/104
0.08653846153846154

5

[성별(Sex), 결혼유무(MarriedID)] 별 연봉(Salary)의 평균을 조사하라. 어떠한 그룹이 가장 평균연봉이 적은가?

df.groupby(['Sex','MarriedID']).agg({'Salary':'mean'})
Salary
Sex MarriedID
F 0 66504.394231
1 69638.986111
M 0 70967.939759
1 70089.038462

6

[성별(Sex), 인종별(RaceDesc)] 별 연봉(Salary)의 중앙값(‘median’)을 조사하라. 연봉의 중앙값이 가장 높은 그룹은 무엇인가?

df.groupby(['Sex','RaceDesc']).agg({'Salary':'median'})
Salary
Sex RaceDesc
F American Indian or Alaska Native 63436.5
Asian 63676.0
Black or African American 61584.0
Two or more races 57837.0
White 62405.0
M American Indian or Alaska Native 70545.0
Asian 64731.0
Black or African American 71339.0
Hispanic 83667.0
Two or more races 61568.0
White 61809.0

7

[성별(Sex), 인종별(RaceDesc)] 별 연봉(Salary)의 중앙값(‘median’)과 근무인원수를 함께 조사하라. 연봉의 중앙값이 가장 높은 그룹이 혜택을 받는 그룹이라고 느껴지는가?

df.groupby(['Sex','RaceDesc']).agg({'Salary':['median','count']})
Salary
median count
Sex RaceDesc
F American Indian or Alaska Native 63436.5 2
Asian 63676.0 17
Black or African American 61584.0 47
Two or more races 57837.0 6
White 62405.0 104
M American Indian or Alaska Native 70545.0 1
Asian 64731.0 12
Black or African American 71339.0 33
Hispanic 83667.0 1
Two or more races 61568.0 5
White 61809.0 83