Exam 3 Solutions Data Visualization and Preprocessing

import numpy as np
import pandas as pa
import seaborn as sns
import matplotlib.pyplot as plt

df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/worldCupPlayer.csv')

df.head()

	Unnamed: 0	Rank	player	club	age	Apps	Mins	Goals	Assists	Yel	Red	SpG	PS	AerialsWon	MotM	Rating	nationality
0	0	1	Hakim Ziyech	Ajax	25	34	3043	9	15	4	0	4.9	75.4	0.2	9	8.21	Morocco
1	2	2	Alireza Jahanbakhsh	AZ Alkmaar	24	33	2840	21	12	3	0	4.3	73.4	0.7	14	8.20	Iran
2	4	3	Hirving Lozano	PSV Eindhoven	22	29	2350	17	8	4	2	3.4	75.3	0.6	8	7.90	Mexico
3	6	11	Lasse Schone	Ajax	32	27(3)	2350	10	3	6	0	1.9	87.0	1.9	2	7.45	Denmark
4	10	25	Santiago Arias	PSV Eindhoven	26	30	2664	3	6	5	0	1.5	82.3	1	2	7.25	Colombia

COYS!

df.query('club == "Tottenham"')

	Unnamed: 0	Rank	player	club	age	Apps	Mins	Goals	Assists	Yel	Red	SpG	PS	AerialsWon	MotM	Rating	nationality
414	795	5	Harry Kane	Tottenham	24	35(2)	3083	30	2	5	0	5	71.2	1.6	9	7.60	England
415	796	2	Harry Kane	Tottenham	24	7	597	7	2	0	0	4.6	78.6	1.6	2	8.34	England
422	806	11	Christian Eriksen	Tottenham	26	37	3226	10	10	0	0	2.6	82.4	0.5	4	7.40	Denmark
423	808	95	Christian Eriksen	Tottenham	26	6	535	2	1	0	0	3.3	83.1	0.5	0	7.08	Denmark
430	820	22	Jan Vertonghen	Tottenham	31	36	3205	0	0	4	0	0.7	86.3	3.3	3	7.26	Belgium
431	822	231	Jan Vertonghen	Tottenham	31	6	540	0	1	1	1	0.3	86.5	2	0	6.75	Belgium
446	852	44	Dele Alli	Tottenham	22	34(2)	2972	9	10	7	0	1.9	77.2	0.8	1	7.09	England
447	854	13	Dele Alli	Tottenham	22	5	427	2	4	1	0	1.6	80.7	0.6	1	7.73	England
455	870	57	Kieran Trippier	Tottenham	27	21(3)	1914	0	5	1	0	0.3	81.4	1.5	0	7.04	England
464	888	75	Eric Dier	Tottenham	24	32(2)	2827	0	2	4	0	0.9	86.3	2.4	0	6.96	England
465	890	179	Eric Dier	Tottenham	24	7	614	0	1	1	0	0.4	80.9	1.6	0	6.86	England
468	896	78	Davinson Sanchez	Tottenham	21	29(2)	2535	0	0	1	1	0.3	89.4	2.4	0	6.94	Colombia
469	898	236	Davinson Sanchez	Tottenham	21	8	720	0	0	0	0	0.4	89.7	1.3	0	6.74	Colombia
475	910	94	Mousa Dembele	Tottenham	30	21(7)	1886	0	0	6	0	0.4	92.2	0.3	0	6.90	Belgium
476	912	268	Mousa Dembele	Tottenham	30	3(3)	328	0	0	2	0	-	92.7	0.2	0	6.66	Belgium
495	950	171	Hugo Lloris	Tottenham	31	36	3240	0	0	1	0	-	75.2	0.3	1	6.74	France
496	952	100	Hugo Lloris	Tottenham	31	7	630	0	0	0	0	-	60.2	0.4	0	7.07	France
558	1072	134	Toby Alderweireld	Tottenham	29	4	294	0	1	0	0	0.5	81.3	1.3	0	7.00	Belgium

ax = df.Goals.plot.hist(title = 'Histogram of Goals', bins = 25)
ax.set(xlabel = 'Goals')
plt.show()

ax = sns.histplot(data = df, x = 'Goals')
ax.set(title = 'Histogram of Goals')

[Text(0.5, 1.0, 'Histogram of Goals')]

ax = df[['Goals','Assists']].plot.hist(alpha = .7, bins = 25)
ax.set(title = 'Goals and Assists Histogram')
plt.show()

ax = df.groupby('nationality').Goals.agg('max').reset_index().plot(kind = 'bar', x = 'nationality', legend = False)
ax.set(title = 'Most Goals Scored by Nationality')
ax.set(ylabel = 'Max Goals')
plt.show()

df.boxplot(by = 'nationality', column = 'Yel', rot = 45)

/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))

<matplotlib.axes._subplots.AxesSubplot at 0x7ff1c9c30090>

df.club == 'Tottenham'

df['Tottenham'] = df.club == 'Tottenham'

sns.scatterplot(data = df, x = 'Goals', y = 'Assists', hue = 'Tottenham', alpha = .7)

<matplotlib.axes._subplots.AxesSubplot at 0x7ff1c9584610>

plt.rcParams["figure.figsize"] = (5,15)
ax = df[df.nationality == 'France'].plot.barh(x = 'player', y = ['Goals','Assists'], stacked = True, color = ['blue','red'])
ax.set(title = 'Goals and Assists of the French National Team')
plt.show()

plt.rcParams["figure.figsize"] = (15,15)
ax = sns.heatmap(df.corr(),vmin = -1)
ax.set(title = 'Heatmap of the World Cup Players')
plt.show()