Statement of Completion#6bbacc7f
Rachel Liang has successfully completed this project.
Intro to Pandas for Data Analysis
easy
Practicing Filtering and Selection with FIFA data
Resolution
Activities
Project.ipynb
In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
df = pd.read_csv('players_22.csv',dtype={25: str, 108: str})
Let's Analyze our DataFrame¶
we will see the first few rows of the dataframe using df.head(), if we want to check the last few rows then we will use df.tail(),
After that, we will check the names of columns using df.columns, check the datatype, and values in each column using df.info().
Lastly, to find the statistical features we use df.describe().
In [3]:
df.head()
Out[3]:
| sofifa_id | player_url | short_name | long_name | player_positions | overall | potential | value_eur | wage_eur | age | ... | lcb | cb | rcb | rb | gk | player_face_url | club_logo_url | club_flag_url | nation_logo_url | nation_flag_url | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 158023 | https://sofifa.com/player/158023/lionel-messi/... | L. Messi | Lionel Andrés Messi Cuccittini | RW, ST, CF | 93 | 93 | 78000000.0 | 320000.0 | 34 | ... | 50+3 | 50+3 | 50+3 | 61+3 | 19+3 | https://cdn.sofifa.net/players/158/023/22_120.png | https://cdn.sofifa.net/teams/73/60.png | https://cdn.sofifa.net/flags/fr.png | https://cdn.sofifa.net/teams/1369/60.png | https://cdn.sofifa.net/flags/ar.png |
| 1 | 188545 | https://sofifa.com/player/188545/robert-lewand... | R. Lewandowski | Robert Lewandowski | ST | 92 | 92 | 119500000.0 | 270000.0 | 32 | ... | 60+3 | 60+3 | 60+3 | 61+3 | 19+3 | https://cdn.sofifa.net/players/188/545/22_120.png | https://cdn.sofifa.net/teams/21/60.png | https://cdn.sofifa.net/flags/de.png | https://cdn.sofifa.net/teams/1353/60.png | https://cdn.sofifa.net/flags/pl.png |
| 2 | 20801 | https://sofifa.com/player/20801/c-ronaldo-dos-... | Cristiano Ronaldo | Cristiano Ronaldo dos Santos Aveiro | ST, LW | 91 | 91 | 45000000.0 | 270000.0 | 36 | ... | 53+3 | 53+3 | 53+3 | 60+3 | 20+3 | https://cdn.sofifa.net/players/020/801/22_120.png | https://cdn.sofifa.net/teams/11/60.png | https://cdn.sofifa.net/flags/gb-eng.png | https://cdn.sofifa.net/teams/1354/60.png | https://cdn.sofifa.net/flags/pt.png |
| 3 | 190871 | https://sofifa.com/player/190871/neymar-da-sil... | Neymar Jr | Neymar da Silva Santos Júnior | LW, CAM | 91 | 91 | 129000000.0 | 270000.0 | 29 | ... | 50+3 | 50+3 | 50+3 | 62+3 | 20+3 | https://cdn.sofifa.net/players/190/871/22_120.png | https://cdn.sofifa.net/teams/73/60.png | https://cdn.sofifa.net/flags/fr.png | NaN | https://cdn.sofifa.net/flags/br.png |
| 4 | 192985 | https://sofifa.com/player/192985/kevin-de-bruy... | K. De Bruyne | Kevin De Bruyne | CM, CAM | 91 | 91 | 125500000.0 | 350000.0 | 30 | ... | 69+3 | 69+3 | 69+3 | 75+3 | 21+3 | https://cdn.sofifa.net/players/192/985/22_120.png | https://cdn.sofifa.net/teams/10/60.png | https://cdn.sofifa.net/flags/gb-eng.png | https://cdn.sofifa.net/teams/1325/60.png | https://cdn.sofifa.net/flags/be.png |
5 rows × 110 columns
In [4]:
df.columns
Out[4]:
Index(['sofifa_id', 'player_url', 'short_name', 'long_name',
'player_positions', 'overall', 'potential', 'value_eur', 'wage_eur',
'age',
...
'lcb', 'cb', 'rcb', 'rb', 'gk', 'player_face_url', 'club_logo_url',
'club_flag_url', 'nation_logo_url', 'nation_flag_url'],
dtype='object', length=110)
In [5]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 19239 entries, 0 to 19238 Columns: 110 entries, sofifa_id to nation_flag_url dtypes: float64(16), int64(44), object(50) memory usage: 16.1+ MB
In [6]:
df.describe()
Out[6]:
| sofifa_id | overall | potential | value_eur | wage_eur | age | height_cm | weight_kg | club_team_id | league_level | ... | mentality_composure | defending_marking_awareness | defending_standing_tackle | defending_sliding_tackle | goalkeeping_diving | goalkeeping_handling | goalkeeping_kicking | goalkeeping_positioning | goalkeeping_reflexes | goalkeeping_speed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 19239.000000 | 19239.000000 | 19239.000000 | 1.916500e+04 | 19178.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 19178.000000 | 19178.000000 | ... | 19239.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 19239.000000 | 2132.000000 |
| mean | 231468.086959 | 65.772182 | 71.079370 | 2.850452e+06 | 9017.989363 | 25.210822 | 181.299704 | 74.943032 | 50580.498123 | 1.354364 | ... | 57.929830 | 46.601746 | 48.045584 | 45.906700 | 16.406102 | 16.192474 | 16.055356 | 16.229274 | 16.491814 | 36.439962 |
| std | 27039.717497 | 6.880232 | 6.086213 | 7.613700e+06 | 19470.176724 | 4.748235 | 6.863179 | 7.069434 | 54401.868535 | 0.747865 | ... | 12.159326 | 20.200807 | 21.232718 | 20.755683 | 17.574028 | 16.839528 | 16.564554 | 17.059779 | 17.884833 | 10.751563 |
| min | 41.000000 | 47.000000 | 49.000000 | 9.000000e+03 | 500.000000 | 16.000000 | 155.000000 | 49.000000 | 1.000000 | 1.000000 | ... | 12.000000 | 4.000000 | 5.000000 | 5.000000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 | 15.000000 |
| 25% | 214413.500000 | 61.000000 | 67.000000 | 4.750000e+05 | 1000.000000 | 21.000000 | 176.000000 | 70.000000 | 479.000000 | 1.000000 | ... | 50.000000 | 29.000000 | 28.000000 | 25.000000 | 8.000000 | 8.000000 | 8.000000 | 8.000000 | 8.000000 | 27.000000 |
| 50% | 236543.000000 | 66.000000 | 71.000000 | 9.750000e+05 | 3000.000000 | 25.000000 | 181.000000 | 75.000000 | 1938.000000 | 1.000000 | ... | 59.000000 | 52.000000 | 56.000000 | 53.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 | 36.000000 |
| 75% | 253532.500000 | 70.000000 | 75.000000 | 2.000000e+06 | 8000.000000 | 29.000000 | 186.000000 | 80.000000 | 111139.000000 | 1.000000 | ... | 66.000000 | 63.000000 | 65.000000 | 63.000000 | 14.000000 | 14.000000 | 14.000000 | 14.000000 | 14.000000 | 45.000000 |
| max | 264640.000000 | 93.000000 | 95.000000 | 1.940000e+08 | 350000.000000 | 54.000000 | 206.000000 | 110.000000 | 115820.000000 | 5.000000 | ... | 96.000000 | 93.000000 | 93.000000 | 92.000000 | 91.000000 | 92.000000 | 93.000000 | 92.000000 | 90.000000 | 65.000000 |
8 rows × 60 columns
Some visualizations to better understand the dataset¶
In [7]:
df['age'].plot(kind='hist', figsize=(10, 8))
Out[7]:
<Axes: ylabel='Frequency'>
In [8]:
df['overall'].plot(kind='box', vert=False, figsize=(10, 5))
Out[8]:
<Axes: >
In [9]:
sns.distplot(df['age']);
/tmp/ipykernel_18/1462740167.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(df['age']);
In [10]:
df['international_reputation'].value_counts().plot(kind='pie', autopct='%1.1f%%', cmap='Set3', figsize=(10, 8))
Out[10]:
<Axes: ylabel='count'>
In [11]:
df['nationality_name'].value_counts().head(10).plot(kind='pie', autopct='%1.1f%%', cmap='Set3', figsize=(10, 8))
Out[11]:
<Axes: ylabel='count'>
In [12]:
df['work_rate'].value_counts().plot(kind='pie', autopct='%1.1f%%', cmap='Set3', figsize=(10, 8))
Out[12]:
<Axes: ylabel='count'>
Filtering¶
Below we will do some basic as well as advance filtering on our FIFA Players 22 dataset.
1. Select all the rows and the first 5 columns from the dataframe and store the result in the variable df_first_five_cols
In [19]:
df_first_five_cols = df.iloc[:, 0:5]
In [20]:
df_first_five_cols
Out[20]:
| sofifa_id | player_url | short_name | long_name | player_positions | |
|---|---|---|---|---|---|
| 0 | 158023 | https://sofifa.com/player/158023/lionel-messi/... | L. Messi | Lionel Andrés Messi Cuccittini | RW, ST, CF |
| 1 | 188545 | https://sofifa.com/player/188545/robert-lewand... | R. Lewandowski | Robert Lewandowski | ST |
| 2 | 20801 | https://sofifa.com/player/20801/c-ronaldo-dos-... | Cristiano Ronaldo | Cristiano Ronaldo dos Santos Aveiro | ST, LW |
| 3 | 190871 | https://sofifa.com/player/190871/neymar-da-sil... | Neymar Jr | Neymar da Silva Santos Júnior | LW, CAM |
| 4 | 192985 | https://sofifa.com/player/192985/kevin-de-bruy... | K. De Bruyne | Kevin De Bruyne | CM, CAM |
| ... | ... | ... | ... | ... | ... |
| 19234 | 261962 | https://sofifa.com/player/261962/defu-song/220002 | Song Defu | 宋德福 | CDM |
| 19235 | 262040 | https://sofifa.com/player/262040/caoimhin-port... | C. Porter | Caoimhin Porter | CM |
| 19236 | 262760 | https://sofifa.com/player/262760/nathan-logue/... | N. Logue | Nathan Logue-Cunningham | CM |
| 19237 | 262820 | https://sofifa.com/player/262820/luke-rudden/2... | L. Rudden | Luke Rudden | ST |
| 19238 | 264540 | https://sofifa.com/player/264540/emanuel-lalch... | E. Lalchhanchhuaha | Emanuel Lalchhanchhuaha | CAM |
19239 rows × 5 columns
2. Select Specific Columns in a Custom Order
In [23]:
df_order_cols = df.iloc[:, [3,6,4,1]]
df_order_cols
Out[23]:
| long_name | potential | player_positions | player_url | |
|---|---|---|---|---|
| 0 | Lionel Andrés Messi Cuccittini | 93 | RW, ST, CF | https://sofifa.com/player/158023/lionel-messi/... |
| 1 | Robert Lewandowski | 92 | ST | https://sofifa.com/player/188545/robert-lewand... |
| 2 | Cristiano Ronaldo dos Santos Aveiro | 91 | ST, LW | https://sofifa.com/player/20801/c-ronaldo-dos-... |
| 3 | Neymar da Silva Santos Júnior | 91 | LW, CAM | https://sofifa.com/player/190871/neymar-da-sil... |
| 4 | Kevin De Bruyne | 91 | CM, CAM | https://sofifa.com/player/192985/kevin-de-bruy... |
| ... | ... | ... | ... | ... |
| 19234 | 宋德福 | 52 | CDM | https://sofifa.com/player/261962/defu-song/220002 |
| 19235 | Caoimhin Porter | 59 | CM | https://sofifa.com/player/262040/caoimhin-port... |
| 19236 | Nathan Logue-Cunningham | 55 | CM | https://sofifa.com/player/262760/nathan-logue/... |
| 19237 | Luke Rudden | 60 | ST | https://sofifa.com/player/262820/luke-rudden/2... |
| 19238 | Emanuel Lalchhanchhuaha | 60 | CAM | https://sofifa.com/player/264540/emanuel-lalch... |
19239 rows × 4 columns
In [ ]:
3. Filter out 50 rows from the Colum number 3,6,4,1 into the variable fifty_rows
In [26]:
fifty_rows = df_order_cols.iloc[0:50, :]
fifty_rows
Out[26]:
| long_name | potential | player_positions | player_url | |
|---|---|---|---|---|
| 0 | Lionel Andrés Messi Cuccittini | 93 | RW, ST, CF | https://sofifa.com/player/158023/lionel-messi/... |
| 1 | Robert Lewandowski | 92 | ST | https://sofifa.com/player/188545/robert-lewand... |
| 2 | Cristiano Ronaldo dos Santos Aveiro | 91 | ST, LW | https://sofifa.com/player/20801/c-ronaldo-dos-... |
| 3 | Neymar da Silva Santos Júnior | 91 | LW, CAM | https://sofifa.com/player/190871/neymar-da-sil... |
| 4 | Kevin De Bruyne | 91 | CM, CAM | https://sofifa.com/player/192985/kevin-de-bruy... |
| 5 | Jan Oblak | 93 | GK | https://sofifa.com/player/200389/jan-oblak/220002 |
| 6 | Kylian Mbappé Lottin | 95 | ST, LW | https://sofifa.com/player/231747/kylian-mbappe... |
| 7 | Manuel Peter Neuer | 90 | GK | https://sofifa.com/player/167495/manuel-neuer/... |
| 8 | Marc-André ter Stegen | 92 | GK | https://sofifa.com/player/192448/marc-andre-te... |
| 9 | Harry Kane | 90 | ST | https://sofifa.com/player/202126/harry-kane/22... |
| 10 | N'Golo Kanté | 90 | CDM, CM | https://sofifa.com/player/215914/ngolo-kante/2... |
| 11 | Karim Benzema | 89 | CF, ST | https://sofifa.com/player/165153/karim-benzema... |
| 12 | Thibaut Courtois | 91 | GK | https://sofifa.com/player/192119/thibaut-court... |
| 13 | 손흥민 孙兴慜 | 89 | LM, CF, LW | https://sofifa.com/player/200104/heung-min-son... |
| 14 | Carlos Henrique Venancio Casimiro | 89 | CDM | https://sofifa.com/player/200145/carlos-henriq... |
| 15 | Virgil van Dijk | 89 | CB | https://sofifa.com/player/203376/virgil-van-di... |
| 16 | Sadio Mané | 89 | LW | https://sofifa.com/player/208722/sadio-mane/22... |
| 17 | Mohamed Salah Ghaly | 89 | RW | https://sofifa.com/player/209331/mohamed-salah... |
| 18 | Ederson Santana de Moraes | 91 | GK | https://sofifa.com/player/210257/ederson-santa... |
| 19 | Joshua Walter Kimmich | 90 | CDM, RB | https://sofifa.com/player/212622/joshua-kimmic... |
| 20 | Alisson Ramsés Becker | 90 | GK | https://sofifa.com/player/212831/alisson-ramse... |
| 21 | Gianluigi Donnarumma | 93 | GK | https://sofifa.com/player/230621/gianluigi-don... |
| 22 | Sergio Ramos García | 88 | CB | https://sofifa.com/player/155862/sergio-ramos-... |
| 23 | Luis Alberto Suárez Díaz | 88 | ST | https://sofifa.com/player/176580/luis-suarez/2... |
| 24 | Toni Kroos | 88 | CM | https://sofifa.com/player/182521/toni-kroos/22... |
| 25 | Romelu Lukaku Menama | 88 | ST | https://sofifa.com/player/192505/romelu-lukaku... |
| 26 | Keylor Navas Gamboa | 88 | GK | https://sofifa.com/player/193041/keylor-navas/... |
| 27 | Raheem Sterling | 89 | LW, RW | https://sofifa.com/player/202652/raheem-sterli... |
| 28 | Bruno Miguel Borges Fernandes | 89 | CAM | https://sofifa.com/player/212198/bruno-miguel-... |
| 29 | Erling Braut Haaland | 93 | ST | https://sofifa.com/player/239085/erling-haalan... |
| 30 | Sergio Leonel Agüero del Castillo | 87 | ST | https://sofifa.com/player/153079/sergio-aguero... |
| 31 | Hugo Lloris | 87 | GK | https://sofifa.com/player/167948/hugo-lloris/2... |
| 32 | Luka Modrić | 87 | CM | https://sofifa.com/player/177003/luka-modric/2... |
| 33 | Ángel Fabián Di María Hernández | 87 | RW, LW | https://sofifa.com/player/183898/angel-di-mari... |
| 34 | Wojciech Tomasz Szczęsny | 87 | GK | https://sofifa.com/player/186153/wojciech-szcz... |
| 35 | Thomas Müller | 87 | CAM, RM, RW | https://sofifa.com/player/189596/thomas-muller... |
| 36 | Ciro Immobile | 87 | ST | https://sofifa.com/player/192387/ciro-immobile... |
| 37 | Paul Pogba | 87 | CM, LM | https://sofifa.com/player/195864/paul-pogba/22... |
| 38 | Marco Verratti | 87 | CM, CAM | https://sofifa.com/player/199556/marco-verratt... |
| 39 | Marcos Aoás Corrêa | 90 | CB, CDM | https://sofifa.com/player/207865/marcos-aoas-c... |
| 40 | Leon Christoph Goretzka | 88 | CM, CDM | https://sofifa.com/player/209658/leon-goretzka... |
| 41 | Paulo Bruno Exequiel Dybala | 88 | CF, CAM | https://sofifa.com/player/211110/paulo-dybala/... |
| 42 | Andrew Robertson | 88 | LB | https://sofifa.com/player/216267/andrew-robert... |
| 43 | Frenkie de Jong | 92 | CM, CDM, CB | https://sofifa.com/player/228702/frenkie-de-jo... |
| 44 | Trent Alexander-Arnold | 92 | RB | https://sofifa.com/player/231281/trent-alexand... |
| 45 | Jadon Sancho | 91 | RM, CF, LM | https://sofifa.com/player/233049/jadon-sancho/... |
| 46 | Rúben dos Santos Gato Alves Dias | 91 | CB | https://sofifa.com/player/239818/ruben-santos-... |
| 47 | Giorgio Chiellini | 86 | CB | https://sofifa.com/player/138956/giorgio-chiel... |
| 48 | Samir Handanovič | 86 | GK | https://sofifa.com/player/162835/samir-handano... |
| 49 | Mats Hummels | 86 | CB | https://sofifa.com/player/178603/mats-hummels/... |
In [ ]:
4. Select the all rows and the Columns ['short_name','age','player_positions', 'overall', 'value_eur'] from the dataframe and store your selection in the variable named_cols
In [28]:
named_cols= df.loc[:, ['short_name','age','player_positions', 'overall', 'value_eur']]
In [29]:
named_cols
Out[29]:
| short_name | age | player_positions | overall | value_eur | |
|---|---|---|---|---|---|
| 0 | L. Messi | 34 | RW, ST, CF | 93 | 78000000.0 |
| 1 | R. Lewandowski | 32 | ST | 92 | 119500000.0 |
| 2 | Cristiano Ronaldo | 36 | ST, LW | 91 | 45000000.0 |
| 3 | Neymar Jr | 29 | LW, CAM | 91 | 129000000.0 |
| 4 | K. De Bruyne | 30 | CM, CAM | 91 | 125500000.0 |
| ... | ... | ... | ... | ... | ... |
| 19234 | Song Defu | 22 | CDM | 47 | 70000.0 |
| 19235 | C. Porter | 19 | CM | 47 | 110000.0 |
| 19236 | N. Logue | 21 | CM | 47 | 100000.0 |
| 19237 | L. Rudden | 19 | ST | 47 | 110000.0 |
| 19238 | E. Lalchhanchhuaha | 19 | CAM | 47 | 110000.0 |
19239 rows × 5 columns
5. Filter out the first 14 rows and the Columns ['short_name','age','player_positions', 'overall', 'value_eur'] from the dataframe and store the result in the variable named_cols_rows
In [31]:
named_cols_rows = named_cols.iloc[0:14, :]
In [32]:
named_cols_rows
Out[32]:
| short_name | age | player_positions | overall | value_eur | |
|---|---|---|---|---|---|
| 0 | L. Messi | 34 | RW, ST, CF | 93 | 78000000.0 |
| 1 | R. Lewandowski | 32 | ST | 92 | 119500000.0 |
| 2 | Cristiano Ronaldo | 36 | ST, LW | 91 | 45000000.0 |
| 3 | Neymar Jr | 29 | LW, CAM | 91 | 129000000.0 |
| 4 | K. De Bruyne | 30 | CM, CAM | 91 | 125500000.0 |
| 5 | J. Oblak | 28 | GK | 91 | 112000000.0 |
| 6 | K. Mbappé | 22 | ST, LW | 91 | 194000000.0 |
| 7 | M. Neuer | 35 | GK | 90 | 13500000.0 |
| 8 | M. ter Stegen | 29 | GK | 90 | 99000000.0 |
| 9 | H. Kane | 27 | ST | 90 | 129500000.0 |
| 10 | N. Kanté | 30 | CDM, CM | 90 | 100000000.0 |
| 11 | K. Benzema | 33 | CF, ST | 89 | 66000000.0 |
| 12 | T. Courtois | 29 | GK | 89 | 85500000.0 |
| 13 | H. Son | 28 | LM, CF, LW | 89 | 104000000.0 |
6. Find out how many players of overall rating greater than 90 exists in the dataset?
In [35]:
(df['overall'] > 90).sum()
Out[35]:
7
7. Find out how many players of value_eur of less than or equal to 20000 in the dataset?
In [37]:
(df['value_eur'] <= 20_000).sum()
Out[37]:
10
8. Select the long_name and age of the players having the club_name='Manchester City' and store your selection in the variable name long_name_age
In [46]:
long_name_age = df.loc[ df['club_name'] == 'Manchester City', ['long_name', 'age']];
In [ ]:
9. Filter out the players of Liverpool club and who are from Brazil and store them in the variable liverpool_brazil
Note: club_name & nationality_name are the column names
In [49]:
liverpool_brazil = df.loc[(df['club_name'] =="Liverpool") & (df['nationality_name'] == 'Brazil') ]
In [ ]:
10. How many players are there who have either mentality_aggression > 91 or power_stamina < 80
Note: Using query method will be easy
In [54]:
((df['mentality_aggression'] > 91) | (df['power_stamina'] < 80)).sum()
Out[54]:
16919
11. Select all players from France who have either mentality_aggression > 91 or power_stamina < 80 and store the filtered data in the variable france_player
In [57]:
france_player = df.loc[((df['mentality_aggression'] > 91) | (df['power_stamina'] < 80)) & (df['nationality_name'] == "France")]
In [58]:
france_player
Out[58]:
| sofifa_id | player_url | short_name | long_name | player_positions | overall | potential | value_eur | wage_eur | age | ... | lcb | cb | rcb | rb | gk | player_face_url | club_logo_url | club_flag_url | nation_logo_url | nation_flag_url | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | 215914 | https://sofifa.com/player/215914/ngolo-kante/2... | N. Kanté | N'Golo Kanté | CDM, CM | 90 | 90 | 100000000.0 | 230000.0 | 30 | ... | 84+3 | 84+3 | 84+3 | 85+3 | 20+3 | https://cdn.sofifa.net/players/215/914/22_120.png | https://cdn.sofifa.net/teams/5/60.png | https://cdn.sofifa.net/flags/gb-eng.png | https://cdn.sofifa.net/teams/1335/60.png | https://cdn.sofifa.net/flags/fr.png |
| 11 | 165153 | https://sofifa.com/player/165153/karim-benzema... | K. Benzema | Karim Benzema | CF, ST | 89 | 89 | 66000000.0 | 350000.0 | 33 | ... | 55+3 | 55+3 | 55+3 | 59+3 | 18+3 | https://cdn.sofifa.net/players/165/153/22_120.png | https://cdn.sofifa.net/teams/243/60.png | https://cdn.sofifa.net/flags/es.png | https://cdn.sofifa.net/teams/1335/60.png | https://cdn.sofifa.net/flags/fr.png |
| 31 | 167948 | https://sofifa.com/player/167948/hugo-lloris/2... | H. Lloris | Hugo Lloris | GK | 87 | 87 | 13500000.0 | 125000.0 | 34 | ... | 30+3 | 30+3 | 30+3 | 34+3 | 85+2 | https://cdn.sofifa.net/players/167/948/22_120.png | https://cdn.sofifa.net/teams/18/60.png | https://cdn.sofifa.net/flags/gb-eng.png | https://cdn.sofifa.net/teams/1335/60.png | https://cdn.sofifa.net/flags/fr.png |
| 37 | 195864 | https://sofifa.com/player/195864/paul-pogba/22... | P. Pogba | Paul Pogba | CM, LM | 87 | 87 | 79500000.0 | 220000.0 | 28 | ... | 72+3 | 72+3 | 72+3 | 72+3 | 13+3 | https://cdn.sofifa.net/players/195/864/22_120.png | https://cdn.sofifa.net/teams/11/60.png | https://cdn.sofifa.net/flags/gb-eng.png | https://cdn.sofifa.net/teams/1335/60.png | https://cdn.sofifa.net/flags/fr.png |
| 57 | 201535 | https://sofifa.com/player/201535/raphael-varan... | R. Varane | Raphaël Varane | CB | 86 | 88 | 68500000.0 | 180000.0 | 28 | ... | 85+3 | 85+3 | 85+3 | 80+3 | 18+3 | https://cdn.sofifa.net/players/201/535/22_120.png | https://cdn.sofifa.net/teams/11/60.png | https://cdn.sofifa.net/flags/gb-eng.png | https://cdn.sofifa.net/teams/1335/60.png | https://cdn.sofifa.net/flags/fr.png |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18375 | 263276 | https://sofifa.com/player/263276/steven-goma/2... | S. Goma | Steven Goma | ST | 54 | 65 | 230000.0 | 750.0 | 19 | ... | 32+2 | 32+2 | 32+2 | 36+2 | 12+2 | https://cdn.sofifa.net/players/263/276/22_120.png | https://cdn.sofifa.net/teams/113391/60.png | https://cdn.sofifa.net/flags/ro.png | NaN | https://cdn.sofifa.net/flags/fr.png |
| 18381 | 263425 | https://sofifa.com/player/263425/adrien-delphi... | A. Delphis | Adrien Delphis | CM | 54 | 66 | 250000.0 | 500.0 | 18 | ... | 46+2 | 46+2 | 46+2 | 50+2 | 16+2 | https://cdn.sofifa.net/players/263/425/22_120.png | https://cdn.sofifa.net/teams/226/60.png | https://cdn.sofifa.net/flags/fr.png | NaN | https://cdn.sofifa.net/flags/fr.png |
| 18424 | 264501 | https://sofifa.com/player/264501/ilyes-hamache... | I. Hamache | Ilyes Hamache | RW | 54 | 69 | 250000.0 | 500.0 | 18 | ... | 27+2 | 27+2 | 27+2 | 34+2 | 10+2 | https://cdn.sofifa.net/players/264/501/22_120.png | https://cdn.sofifa.net/teams/110456/60.png | https://cdn.sofifa.net/flags/fr.png | NaN | https://cdn.sofifa.net/flags/fr.png |
| 18435 | 233645 | https://sofifa.com/player/233645/beni-nkololo/... | B. Nkololo | Béni Nkololo | CAM | 53 | 60 | 190000.0 | 500.0 | 24 | ... | 39+2 | 39+2 | 39+2 | 44+2 | 14+2 | https://cdn.sofifa.net/players/233/645/22_120.png | https://cdn.sofifa.net/teams/111396/60.png | https://cdn.sofifa.net/flags/au.png | NaN | https://cdn.sofifa.net/flags/fr.png |
| 18837 | 263712 | https://sofifa.com/player/263712/melvin-mastil... | M. Mastil | Melvin Mastil | GK | 52 | 62 | 160000.0 | 1000.0 | 21 | ... | 20+2 | 20+2 | 20+2 | 15+2 | 51+2 | https://cdn.sofifa.net/players/263/712/22_120.png | https://cdn.sofifa.net/teams/1862/60.png | https://cdn.sofifa.net/flags/ch.png | NaN | https://cdn.sofifa.net/flags/fr.png |
862 rows × 110 columns
12. Identify Left-Footed Players and Display Their Details
In [63]:
left_foot_players = df.loc[df['preferred_foot'] == 'Left', ['short_name', 'age', 'preferred_foot']]
In [64]:
left_foot_players
Out[64]:
| short_name | age | preferred_foot | |
|---|---|---|---|
| 0 | L. Messi | 34 | Left |
| 12 | T. Courtois | 29 | Left |
| 17 | M. Salah | 29 | Left |
| 18 | Ederson | 27 | Left |
| 25 | R. Lukaku | 28 | Left |
| ... | ... | ... | ... |
| 19198 | N. Rabha | 24 | Left |
| 19202 | J. Bradley-Walsh | 20 | Left |
| 19204 | Jiang Wenhao | 21 | Left |
| 19214 | R. Mahon | 18 | Left |
| 19219 | E. Kwelele | 19 | Left |
4565 rows × 3 columns
In [ ]:
