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'>

No description has been provided for this image

In [8]:

df['overall'].plot(kind='box', vert=False, figsize=(10, 5))

Out[8]:

<Axes: >

In [9]:

sns.distplot(df['age']);

/tmp/ipykernel_17/1462740167.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 [13]:

df_first_five_cols = df.iloc[:,:5]

In [ ]:

2. Select Specific Columns in a Custom Order

In [21]:

df_order_cols = df.iloc[:,[3,6,4,1]]

In [ ]:

3. Filter out 50 rows from the Colum number 3,6,4,1 into the variable fifty_rows

In [23]:

fifty_rows = df.iloc[:50,[3,6,4,1]]

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 [25]:

named_cols= df[["short_name","age","player_positions","overall","value_eur"]]

In [ ]:

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 [27]:

named_cols_rows = df.iloc[:14][['short_name',"age","player_positions","overall","value_eur"]]

In [ ]:

6. Find out how many players of overall rating greater than 90 exists in the dataset?

In [36]:

df[df['overall'] > 90].shape

Out[36]:

(7, 110)

7. Find out how many players of value_eur of less than or equal to 20000 in the dataset?

In [37]:

df[df['value_eur']<=20000].shape

Out[37]:

(10, 110)

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 [43]:

long_name_age = df[df['club_name']=='Manchester City'][['long_name','age']]

In [44]:

long_name_age

Out[44]:

	long_name	age
4	Kevin De Bruyne	30
18	Ederson Santana de Moraes	27
27	Raheem Sterling	26
46	Rúben dos Santos Gato Alves Dias	24
58	Riyad Mahrez	30
62	João Pedro Cavaco Cancelo	27
63	Aymeric Laporte	27
65	Bernardo Mota Veiga de Carvalho e Silva	26
67	Rodrigo Hernández Cascante	25
78	İlkay Gündoğan	30
80	Kyle Walker	31
118	Jack Grealish	25
139	Philip Foden	21
140	Fernando Luiz Rosa	36
166	John Stones	27
191	Gabriel Fernando de Jesus	24
280	Ferran Torres García	21
477	Oleksandr Zinchenko	24
569	Benjamin Mendy	26
745	Nathan Aké	26
1075	Zack Thomas Steffen	26
7487	Scott Carson	35
9771	Kayky da Silva Chagas	18
11543	Luke Bolton	21
12055	Cole Palmer	19
12095	Liam Delap	18
12113	Jayden Jezairo Braaf	18
14275	Luke Mbete	17
14349	Romeo Lavia	17
14364	Samuel Edozie	18
15157	James McAtee	18
17084	Iker Pozo La Rosa	20

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[(df['club_name'] == 'Liverpool') & (df['nationality_name'] == 'Brazil')]

In [50]:

liverpool_brazil

Out[50]:

	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
20	212831	https://sofifa.com/player/212831/alisson-ramse...	Alisson	Alisson Ramsés Becker	GK	89	90	82000000.0	190000.0	28	...	31+3	31+3	31+3	30+3	87+3	https://cdn.sofifa.net/players/212/831/22_120.png	https://cdn.sofifa.net/teams/9/60.png	https://cdn.sofifa.net/flags/gb-eng.png	NaN	https://cdn.sofifa.net/flags/br.png
61	209499	https://sofifa.com/player/209499/fabio-henriqu...	Fabinho	Fábio Henrique Tavares	CDM, CB	86	88	73500000.0	165000.0	27	...	84+3	84+3	84+3	82+3	18+3	https://cdn.sofifa.net/players/209/499/22_120.png	https://cdn.sofifa.net/teams/9/60.png	https://cdn.sofifa.net/flags/gb-eng.png	NaN	https://cdn.sofifa.net/flags/br.png
85	201942	https://sofifa.com/player/201942/roberto-firmi...	Roberto Firmino	Roberto Firmino Barbosa de Oliveira	CF	85	85	54000000.0	185000.0	29	...	67+3	67+3	67+3	69+3	17+3	https://cdn.sofifa.net/players/201/942/22_120.png	https://cdn.sofifa.net/teams/9/60.png	https://cdn.sofifa.net/flags/gb-eng.png	NaN	https://cdn.sofifa.net/flags/br.png
17275	259430	https://sofifa.com/player/259430/marcelo-pital...	Marcelo Pitaluga	Marcelo Pitaluga	GK	57	78	475000.0	1000.0	18	...	21+2	21+2	21+2	18+2	56+2	https://cdn.sofifa.net/players/259/430/22_120.png	https://cdn.sofifa.net/teams/9/60.png	https://cdn.sofifa.net/flags/gb-eng.png	NaN	https://cdn.sofifa.net/flags/br.png

4 rows × 110 columns

10. How many players are there who have either mentality_aggression > 91 or power_stamina < 80
Note: Using query method will be easy

In [53]:

df[(df['mentality_aggression']>91) | (df['power_stamina']<80)].shape

Out[53]:

(16919, 110)

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 [54]:

france_data = df[df['nationality_name']=='France']

In [55]:

france_player = france_data[(france_data['mentality_aggression']>91) | (france_data['power_stamina']<80)]

In [ ]:

12. Identify Left-Footed Players and Display Their Details

In [61]:

left_foot_players = df[df['preferred_foot']=='Left'][['short_name','age','preferred_foot']]

In [ ]:

Statement of Completion#4c2b767f

Intro to Pandas for Data Analysis

Practicing Filtering and Selection with FIFA data

Let's Analyze our DataFrame¶

Some visualizations to better understand the dataset¶

Filtering¶