Visualizing the Career Statistics of Lebron James

The goal of the project is to visualize of some of the statistics of the Lebron James career so far. Lebron James is reffered to as one of the best players in NBA history and has piled up an empressive stat line in his career so far.

import pandas as pd
import plotly.express as px
import plotly.graph_objs as go

import matplotlib.pyplot as plt

df_Playoff = pd.read_excel("PlayoffSeasonAverage.xlsx",skiprows=[1])
df_Season_avg = pd.read_excel("SeasonAverage.xlsx",skiprows=[1])
df_Season_team_avg = pd.read_excel("SeasonTeamAverage.xlsx",skiprows=[1])
df_Season_total = pd.read_excel("SeasonTotal.xlsx",skiprows=[1])
print ('Data read into a pandas dataframe!')

Data read into a pandas dataframe!

df_Playoff.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	2005-06	21	CLE	NBA	SF	13	13	46.5	11.2	23.6	...	0.737	1.7	6.4	8.1	5.8	1.4	0.7	5.0	3.4	30.8
1	2006-07	22	CLE	NBA	SF	20	20	44.7	8.3	20.0	...	0.755	1.3	6.8	8.1	8.0	1.7	0.5	3.3	2.0	25.1
2	2007-08	23	CLE	NBA	SF	13	13	42.5	8.7	21.2	...	0.731	1.2	6.6	7.8	7.6	1.8	1.3	4.2	2.5	28.2
3	2008-09	24	CLE	NBA	SF	14	14	41.4	11.4	22.3	...	0.749	1.4	7.8	9.1	7.3	1.6	0.9	2.7	2.1	35.3
4	2009-10	25	CLE	NBA	SF	11	11	41.8	9.6	19.2	...	0.733	1.4	7.9	9.3	7.6	1.7	1.8	3.8	2.1	29.1

5 rows × 30 columns

df_Season_avg.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	2003-04	19	CLE	NBA	SG	79	79	39.5	7.9	18.9	...	0.754	1.3	4.2	5.5	5.9	1.6	0.7	3.5	1.9	20.9
1	2004-05	20	CLE	NBA	SF	80	80	42.4	9.9	21.1	...	0.750	1.4	6.0	7.4	7.2	2.2	0.7	3.3	1.8	27.2
2	2005-06	21	CLE	NBA	SF	79	79	42.5	11.1	23.1	...	0.738	0.9	6.1	7.0	6.6	1.6	0.8	3.3	2.3	31.4
3	2006-07	22	CLE	NBA	SF	78	78	40.9	9.9	20.8	...	0.698	1.1	5.7	6.7	6.0	1.6	0.7	3.2	2.2	27.3
4	2007-08	23	CLE	NBA	SF	75	74	40.4	10.6	21.9	...	0.712	1.8	6.1	7.9	7.2	1.8	1.1	3.4	2.2	30.0

5 rows × 30 columns

df_Season_team_avg.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	11 seasons	NaN	CLE	NBA	NaN	849	848	39.0	9.9	20.0	...	0.733	1.2	6.1	7.3	7.3	1.6	0.8	3.5	1.9	27.2
1	4 seasons	NaN	MIA	NBA	NaN	294	294	38.0	9.9	18.2	...	0.758	1.2	6.4	7.6	6.7	1.7	0.7	3.4	1.7	26.9
2	4 seasons	NaN	LAL	NBA	NaN	223	223	35.2	10.1	19.9	...	0.701	1.0	7.1	8.0	8.2	1.2	0.7	3.7	1.8	27.0

3 rows × 30 columns

df_Season_total.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Unnamed: 30	Trp Dbl
0	2003-04	19	CLE	NBA	SG	79	79	3122	622	1492	...	333	432	465	130	58	273	149	1654	NaN	0
1	2004-05	20	CLE	NBA	SF	80	80	3388	795	1684	...	477	588	577	177	52	262	146	2175	NaN	4
2	2005-06	21	CLE	NBA	SF	79	79	3361	875	1823	...	481	556	521	123	66	260	181	2478	NaN	5
3	2006-07	22	CLE	NBA	SF	78	78	3190	772	1621	...	443	526	470	125	55	250	171	2132	NaN	1
4	2007-08	23	CLE	NBA	SF	75	74	3027	794	1642	...	459	592	539	138	81	255	165	2250	NaN	7

5 rows × 32 columns

1. Create a histogram showing the total number of points for each team Lebron James has played for. The X-axis is the teams (TM) and the Y-axis is the total number of points (PTS).

df_total_pts = df_Season_total.groupby(['Tm'],as_index=False).sum() #Using groupby function to create required pivot table
df_total_pts

	Tm	Age	G	GS	MP	FG	FGA	FG%	3P	3PA	...	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Unnamed: 30	Trp Dbl
0	CLE	280	849	848	33130	8369	17022	5.419	1251	3713	...	5156	6190	6228	1376	695	2973	1618	23119	0.0	64
1	LAL	142	223	223	7841	2263	4442	2.040	524	1485	...	1580	1795	1837	271	153	822	403	6024	0.0	32
2	MIA	110	294	294	11168	2911	5361	2.173	365	988	...	1873	2225	1980	489	193	993	495	7919	0.0	9

3 rows × 29 columns

fig = px.histogram(df_total_pts, x="Tm",y="PTS",color="Tm",title="Total Points for Each Team")
fig.update_layout(yaxis_title="Total Points",xaxis_title="Team")

fig.show()

1. Create a dot plot showing the total number of 3 points shots made compared to the total number of 3 points shots attempted by Lebron James. When you hover over the points you should see the season (Season) and team (Tm) he is playing on. The X-axis is the total number of 3 point shot attempted (3PA) and the Y-axis is the total number of 3 point shots made (3P).

df_total_3pts = df_Season_total.groupby(['Season','Tm'],as_index=False).sum() #Using groupby function to create required pivot table
df_total_3pts

	Season	Tm	Age	G	GS	MP	FG	FGA	FG%	3P	...	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Unnamed: 30	Trp Dbl
0	2003-04	CLE	19	79	79	3122	622	1492	0.417	63	...	333	432	465	130	58	273	149	1654	0.0	0
1	2004-05	CLE	20	80	80	3388	795	1684	0.472	108	...	477	588	577	177	52	262	146	2175	0.0	4
2	2005-06	CLE	21	79	79	3361	875	1823	0.480	127	...	481	556	521	123	66	260	181	2478	0.0	5
3	2006-07	CLE	22	78	78	3190	772	1621	0.476	99	...	443	526	470	125	55	250	171	2132	0.0	1
4	2007-08	CLE	23	75	74	3027	794	1642	0.484	113	...	459	592	539	138	81	255	165	2250	0.0	7
5	2008-09	CLE	24	81	81	3054	789	1613	0.489	132	...	507	613	587	137	93	241	139	2304	0.0	7
6	2009-10	CLE	25	76	76	2966	768	1528	0.503	129	...	483	554	651	125	77	261	119	2258	0.0	4
7	2010-11	MIA	26	79	79	3063	758	1485	0.510	92	...	510	590	554	124	50	284	163	2111	0.0	4
8	2011-12	MIA	27	62	62	2326	621	1169	0.531	54	...	398	492	387	115	50	213	96	1683	0.0	0
9	2012-13	MIA	28	76	76	2877	765	1354	0.565	103	...	513	610	551	129	67	226	110	2036	0.0	4
10	2013-14	MIA	29	77	77	2902	767	1353	0.567	116	...	452	533	488	121	26	270	126	2089	0.0	1
11	2014-15	CLE	30	69	69	2493	624	1279	0.488	120	...	365	416	511	109	49	272	135	1743	0.0	2
12	2015-16	CLE	31	76	76	2709	737	1416	0.520	87	...	454	565	514	104	49	249	143	1920	0.0	3
13	2016-17	CLE	32	74	74	2794	736	1344	0.548	124	...	542	639	646	92	44	303	134	1954	0.0	13
14	2017-18	CLE	33	82	82	3026	857	1580	0.542	149	...	612	709	747	116	71	347	136	2251	0.0	18
15	2018-19	LAL	34	55	55	1937	558	1095	0.510	111	...	408	465	454	72	33	197	94	1505	0.0	8
16	2019-20	LAL	35	67	67	2316	643	1303	0.493	148	...	459	525	684	78	36	261	118	1698	0.0	13
17	2020-21	LAL	36	45	45	1504	422	823	0.513	104	...	317	346	350	48	25	168	70	1126	0.0	5
18	2021-22	LAL	37	56	56	2084	640	1221	0.524	161	...	396	459	349	73	59	196	121	1695	0.0	6

19 rows × 30 columns

fig = go.Figure()

fig = px.scatter(df_total_3pts, x="3PA", y="3P", log_x=True,
                 hover_name= "Tm", hover_data=["Season"])

fig.update_layout(title="3 Pointers vs 3 Pointer Attempts",
                  xaxis_title="3 Point Attempts",
                  yaxis_title="3 Pointers")

fig.show()

Use SeasonAverage dataset for questions 3 and 4.

df_Season_avg.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	2003-04	19	CLE	NBA	SG	79	79	39.5	7.9	18.9	...	0.754	1.3	4.2	5.5	5.9	1.6	0.7	3.5	1.9	20.9
1	2004-05	20	CLE	NBA	SF	80	80	42.4	9.9	21.1	...	0.750	1.4	6.0	7.4	7.2	2.2	0.7	3.3	1.8	27.2
2	2005-06	21	CLE	NBA	SF	79	79	42.5	11.1	23.1	...	0.738	0.9	6.1	7.0	6.6	1.6	0.8	3.3	2.3	31.4
3	2006-07	22	CLE	NBA	SF	78	78	40.9	9.9	20.8	...	0.698	1.1	5.7	6.7	6.0	1.6	0.7	3.2	2.2	27.3
4	2007-08	23	CLE	NBA	SF	75	74	40.4	10.6	21.9	...	0.712	1.8	6.1	7.9	7.2	1.8	1.1	3.4	2.2	30.0

5 rows × 30 columns

df_FG = df_Season_avg.groupby(['Season','Age','Tm'],as_index=False).mean() #Using groupby function to create required pivot table
df_FG

	Season	Age	Tm	G	GS	MP	FG	FGA	FG%	3P	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	2003-04	19	CLE	79.0	79.0	39.5	7.9	18.9	0.417	0.8	...	0.754	1.3	4.2	5.5	5.9	1.6	0.7	3.5	1.9	20.9
1	2004-05	20	CLE	80.0	80.0	42.4	9.9	21.1	0.472	1.4	...	0.750	1.4	6.0	7.4	7.2	2.2	0.7	3.3	1.8	27.2
2	2005-06	21	CLE	79.0	79.0	42.5	11.1	23.1	0.480	1.6	...	0.738	0.9	6.1	7.0	6.6	1.6	0.8	3.3	2.3	31.4
3	2006-07	22	CLE	78.0	78.0	40.9	9.9	20.8	0.476	1.3	...	0.698	1.1	5.7	6.7	6.0	1.6	0.7	3.2	2.2	27.3
4	2007-08	23	CLE	75.0	74.0	40.4	10.6	21.9	0.484	1.5	...	0.712	1.8	6.1	7.9	7.2	1.8	1.1	3.4	2.2	30.0
5	2008-09	24	CLE	81.0	81.0	37.7	9.7	19.9	0.489	1.6	...	0.780	1.3	6.3	7.6	7.2	1.7	1.1	3.0	1.7	28.4
6	2009-10	25	CLE	76.0	76.0	39.0	10.1	20.1	0.503	1.7	...	0.767	0.9	6.4	7.3	8.6	1.6	1.0	3.4	1.6	29.7
7	2010-11	26	MIA	79.0	79.0	38.8	9.6	18.8	0.510	1.2	...	0.759	1.0	6.5	7.5	7.0	1.6	0.6	3.6	2.1	26.7
8	2011-12	27	MIA	62.0	62.0	37.5	10.0	18.9	0.531	0.9	...	0.771	1.5	6.4	7.9	6.2	1.9	0.8	3.4	1.5	27.1
9	2012-13	28	MIA	76.0	76.0	37.9	10.1	17.8	0.565	1.4	...	0.753	1.3	6.8	8.0	7.3	1.7	0.9	3.0	1.4	26.8
10	2013-14	29	MIA	77.0	77.0	37.7	10.0	17.6	0.567	1.5	...	0.750	1.1	5.9	6.9	6.3	1.6	0.3	3.5	1.6	27.1
11	2014-15	30	CLE	69.0	69.0	36.1	9.0	18.5	0.488	1.7	...	0.710	0.7	5.3	6.0	7.4	1.6	0.7	3.9	2.0	25.3
12	2015-16	31	CLE	76.0	76.0	35.6	9.7	18.6	0.520	1.1	...	0.731	1.5	6.0	7.4	6.8	1.4	0.6	3.3	1.9	25.3
13	2016-17	32	CLE	74.0	74.0	37.8	9.9	18.2	0.548	1.7	...	0.674	1.3	7.3	8.6	8.7	1.2	0.6	4.1	1.8	26.4
14	2017-18	33	CLE	82.0	82.0	36.9	10.5	19.3	0.542	1.8	...	0.731	1.2	7.5	8.6	9.1	1.4	0.9	4.2	1.7	27.5
15	2018-19	34	LAL	55.0	55.0	35.2	10.1	19.9	0.510	2.0	...	0.665	1.0	7.4	8.5	8.3	1.3	0.6	3.6	1.7	27.4
16	2019-20	35	LAL	67.0	67.0	34.6	9.6	19.4	0.493	2.2	...	0.693	1.0	6.9	7.8	10.2	1.2	0.5	3.9	1.8	25.3
17	2020-21	36	LAL	45.0	45.0	33.4	9.4	18.3	0.513	2.3	...	0.698	0.6	7.0	7.7	7.8	1.1	0.6	3.7	1.6	25.0
18	2021-22	37	LAL	56.0	56.0	37.2	11.4	21.8	0.524	2.9	...	0.756	1.1	7.1	8.2	6.2	1.3	1.1	3.5	2.2	30.3

19 rows × 28 columns

1. Create a dot plot showing the field goal percentage averaged by Lebron james compared to his age. The X-axis is the age (Age) and the Y-axis is the field goal percentage average(FG%).

fig = go.Figure()

fig = px.scatter(df_FG, x="Age", y="FG%", log_x=True,
                 hover_name= "Tm", hover_data=["Season"])

fig.update_layout(title="Average Field Goal % according to Age",
                  xaxis_title="Field Goal %",
                  yaxis_title="Age")

fig.data[0].marker.color = "blue"

fig.show()

1. Create a line chart show the number of points averaged by Lebron James per season. The X-axis is the season (Season) and the Y-axis is the average number of points (PTS).

fig = go.Figure()

fig = px.line(df_FG, x="Season", y="PTS")

fig.update_layout(title="Average Points Scored Per Season",
                  xaxis_title="Season",
                  yaxis_title="Avg Points")
fig.data[0].line.color = "red"
fig.update_xaxes(type='category')

fig.show()

Use PlayoffSeasonAverage dataset for questions 5 and 6.

df_Playoff.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	2005-06	21	CLE	NBA	SF	13	13	46.5	11.2	23.6	...	0.737	1.7	6.4	8.1	5.8	1.4	0.7	5.0	3.4	30.8
1	2006-07	22	CLE	NBA	SF	20	20	44.7	8.3	20.0	...	0.755	1.3	6.8	8.1	8.0	1.7	0.5	3.3	2.0	25.1
2	2007-08	23	CLE	NBA	SF	13	13	42.5	8.7	21.2	...	0.731	1.2	6.6	7.8	7.6	1.8	1.3	4.2	2.5	28.2
3	2008-09	24	CLE	NBA	SF	14	14	41.4	11.4	22.3	...	0.749	1.4	7.8	9.1	7.3	1.6	0.9	2.7	2.1	35.3
4	2009-10	25	CLE	NBA	SF	11	11	41.8	9.6	19.2	...	0.733	1.4	7.9	9.3	7.6	1.7	1.8	3.8	2.1	29.1

5 rows × 30 columns

df_avg_playoff = df_Playoff.groupby(['Season','Age','Tm'],as_index=False).mean() #Using groupby function to create required pivot table
df_avg_playoff

	Season	Age	Tm	G	GS	MP	FG	FGA	FG%	3P	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	2005-06	21	CLE	13.0	13.0	46.5	11.2	23.6	0.476	1.6	...	0.737	1.7	6.4	8.1	5.8	1.4	0.7	5.0	3.4	30.8
1	2006-07	22	CLE	20.0	20.0	44.7	8.3	20.0	0.416	1.1	...	0.755	1.3	6.8	8.1	8.0	1.7	0.5	3.3	2.0	25.1
2	2007-08	23	CLE	13.0	13.0	42.5	8.7	21.2	0.411	1.4	...	0.731	1.2	6.6	7.8	7.6	1.8	1.3	4.2	2.5	28.2
3	2008-09	24	CLE	14.0	14.0	41.4	11.4	22.3	0.510	1.9	...	0.749	1.4	7.8	9.1	7.3	1.6	0.9	2.7	2.1	35.3
4	2009-10	25	CLE	11.0	11.0	41.8	9.6	19.2	0.502	1.8	...	0.733	1.4	7.9	9.3	7.6	1.7	1.8	3.8	2.1	29.1
5	2010-11	26	MIA	21.0	21.0	43.9	8.3	17.8	0.466	1.4	...	0.763	1.6	6.8	8.4	5.9	1.7	1.2	3.1	2.7	23.7
6	2011-12	27	MIA	23.0	23.0	42.7	10.9	21.8	0.500	1.0	...	0.739	2.3	7.4	9.7	5.6	1.9	0.7	3.5	2.0	30.3
7	2012-13	28	MIA	23.0	23.0	41.7	9.2	18.8	0.491	1.6	...	0.777	1.6	6.8	8.4	6.6	1.8	0.8	3.0	1.9	25.9
8	2013-14	29	MIA	20.0	20.0	38.2	9.6	17.0	0.565	1.8	...	0.806	0.7	6.4	7.1	4.8	1.8	0.6	3.1	2.3	27.4
9	2014-15	30	CLE	20.0	20.0	42.2	11.4	27.2	0.417	1.3	...	0.731	1.9	9.5	11.3	8.5	1.7	1.1	4.1	2.9	30.1
10	2015-16	31	CLE	21.0	21.0	39.1	10.4	19.9	0.525	1.5	...	0.661	2.0	7.5	9.5	7.6	2.3	1.3	3.6	2.6	26.3
11	2016-17	32	CLE	18.0	18.0	41.3	12.1	21.3	0.565	2.4	...	0.698	1.1	8.1	9.1	7.8	1.9	1.3	4.0	2.4	32.8
12	2017-18	33	CLE	22.0	22.0	41.9	12.5	23.2	0.539	1.8	...	0.746	1.4	7.7	9.1	9.0	1.4	1.0	4.3	2.4	34.0
13	2019-20	35	LAL	21.0	21.0	36.3	10.2	18.2	0.560	2.1	...	0.720	1.3	9.4	10.8	8.8	1.2	0.9	4.0	1.9	27.6
14	2020-21	36	LAL	6.0	6.0	37.3	9.0	19.0	0.474	3.0	...	0.609	1.2	6.0	7.2	8.0	1.5	0.3	4.2	1.2	23.3

15 rows × 28 columns

1. Create a bubble chart showing the different playoff seasons for Lebron James. The X-axis is the average number of assist (AST) and the Y-axis should be the average number of points (PTS). The color should be based on the team (Tm), the size should be based on number of games played (G), and the hover data should be the season (Season).

fig = px.scatter(df_avg_playoff, x="AST", y="PTS",size="G", color="Tm",hover_name="Season",log_x=True,size_max=60,title="Points Scored In Playoff Seasons")
fig.show()

6. Create a bar chart of the 4 playoff seasons lebron James had won a championship based on the average number of minutes played (2011-2012),(2012-2013),(2015-2016),(2019-2020). The X-axis is the season (Season) and the Y-axis is the average number of minutes played (MP).

championships = ['2011-12','2012-13', '2015-16', '2019-20']
df_championship = df_avg_playoff[df_avg_playoff.Season.isin(championships)]
df_championship = df_championship.astype({"Season": str})
df_championship

	Season	Age	Tm	G	GS	MP	FG	FGA	FG%	3P	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
6	2011-12	27	MIA	23.0	23.0	42.7	10.9	21.8	0.500	1.0	...	0.739	2.3	7.4	9.7	5.6	1.9	0.7	3.5	2.0	30.3
7	2012-13	28	MIA	23.0	23.0	41.7	9.2	18.8	0.491	1.6	...	0.777	1.6	6.8	8.4	6.6	1.8	0.8	3.0	1.9	25.9
10	2015-16	31	CLE	21.0	21.0	39.1	10.4	19.9	0.525	1.5	...	0.661	2.0	7.5	9.5	7.6	2.3	1.3	3.6	2.6	26.3
13	2019-20	35	LAL	21.0	21.0	36.3	10.2	18.2	0.560	2.1	...	0.720	1.3	9.4	10.8	8.8	1.2	0.9	4.0	1.9	27.6

4 rows × 28 columns

import plotly

fig = px.bar(df_championship, x="Season",y="MP",color="Tm",title="Avg Minutes Played in Winning Playoffs")
fig.update_layout(yaxis_title="Avg Minutes",xaxis_title="Season")
fig.update_xaxes(type='category')
fig.show()

Use SeasonTeamAverage dataset for question 7.

df_Season_team_avg.head()

	Season	Age	Tm	Lg	Pos	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	11 seasons	NaN	CLE	NBA	NaN	849	848	39.0	9.9	20.0	...	0.733	1.2	6.1	7.3	7.3	1.6	0.8	3.5	1.9	27.2
1	4 seasons	NaN	MIA	NBA	NaN	294	294	38.0	9.9	18.2	...	0.758	1.2	6.4	7.6	6.7	1.7	0.7	3.4	1.7	26.9
2	4 seasons	NaN	LAL	NBA	NaN	223	223	35.2	10.1	19.9	...	0.701	1.0	7.1	8.0	8.2	1.2	0.7	3.7	1.8	27.0

3 rows × 30 columns

1. Create a bar chart comparing the number of points averaged by Lebron James on each team he has played for. The X-axis is the Team (Tm) and the Y-Axis is the average number of points (PTS).

fig = px.bar(df_Season_team_avg, x="Tm",y="PTS",color="Tm",title="Avg Number of Points Scored For Teams Played For")
fig.update_layout(yaxis_title="Avg Points",xaxis_title="Team")
fig.update_xaxes(type='category')
fig.show()