Pandas exercises (ii)------data filtering and sorting

Data filtering and sorting------Explore 2012 Euro Cup data

Related data See (github)

Step 1-Import the Pandas library

import Pandas as PD

Step 2-Data set

" ./data/euro2012.csv "      # Euro2012.csv

Step 3-Name the dataset euro12

Euro12 = pd.read_csv (path2) euro12.tail ()

Output:

Total hit ... on

	Team	goals	Shots on target	Shots off target	Shooting accuracy	% goals-to-shots	Shots (inc. Blocked)	woodwork	penalty goals	Penalties not scored		Saves made	Saves-to-shots ratio	fouls Won	fouls conceded	offsides	Yellow Cards	Red Cards	Subs	Subs off	Players used
11	Republic of Ireland	1	7	12	36.8%	5.2%	28	0	0	0	...	17	65.4%	43	51	11	6	1	10	10	17
12	Russia	5	9	31	22.5%	12.5%	59	2	0	0	...	10	77%	34	43	4	6	0	7	7	16
13	Spain	12	42	33	55.9%	16%	100	0	1	0	...	15	93.8%	102	83	19	11	0	17	17	18
14	Sweden	5	17	19	47.2%	13.8%	39	3	0	0	...	8	61.6%	35	51	7	7	0	9	9	18
15	Ukraine	2	7	26	21.2%	6%	38	0	0	0	...	13	76.5%	48	31	4	5	0	9	9	18

5 rowsx35 Columns

Step 4 Select GoalsThis column

euro12. Goals  #   euro12[' goals ')

Output:

Step 5 How many teams are involved in the 2012 Euro Cup?

EURO12.SHAPE[0]

Output:

16

Step 6 How many columns (columns) are there in the data set?

Euro12.info ()

Output:

<class ' Pandas.core.frame.DataFrame ' >rangeindex:16 entries, 0 to 15Data columns (total columns): Team Non-null objectgoals non-null int64shots on target non-null i              Nt64shots off target non-null int64shooting accuracy non-null object% goals-to-shots Objecttotal Shots (inc Blocked) non-null non-null int64hit woodwork non-null Int64pen Alty goals Non-null Int64penalties not scored 1 Non-null int64headed goals 6 int64passes non-null int64passes completed non-null non-null int64passing ACCU Racy non-null objecttouches non-null int64crosses                       ll Int64dribbles non-null Int64corners taken Non-null int64tackles Non-null inT64clearances non-null int64interceptions non-null int64clearances off line Non-null Float64clean Sheets non-null int64blocks, Non-null int64goal  S conceded non-null Int64saves made-non-null int64saves-to-shots ratio 16                      Non-null objectfouls Won non-null Int64fouls conceded-non-null int64offsides Non-null int64yellow Cards non-null int64red Cards                  L int64subs on Non-null int64subs off Non-null int64players used Non-null Int64dtypes:float64 (1), Int64, Object (5) Memory usage:4.5+ KB

Step 7 Save the columns in the dataset team, Yellow cards, and red cards as a single data frame named discipline

Discipline = euro12[['Team'Yellow Cards'Red Cards']]discipline

Output:

Step 8 to the data frame discipline according to the first red cards and then yellow cards to sort

Discipline.sort_values (['Red Cards'Yellow Cards'], ascending = False)

Output:

Step 9 Calculate the average of the number of yellow cards each team gets

Round (discipline['Yellow Cards'].mean ())

Output:

7.0

Step 10 Find team data for the number of goals goals over 6

Euro12[euro12. Goals > 6]

Output:

	Team	goals	Shots on target	Shot s off target	Shooting accuracy	% goals-to-shots	Total Shots (inc. Blocked)	hit Woodwork	Penalty goals	penalties not scored	...	Saves made	Saves-to-shots ratio	fouls Won	fouls conceded	offsides	Yellow Cards	Red Cards	Subs on	Subs off	Players used
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
13	Spain	12	42	33	55.9%	16%	100	0	1	0	...	15	93.8%	102	83	19	11	0	17	17	18

2 rowsx35 Columns

Step 11 Select Team data that begins with the letter G or ends with E

# euro12[euro12. Team.str.startswith (' G ')]euro12[euro12. Team.str.endswith ('e')]  #  team ending with the letter e

Output:

td>65

	Team	goals	Shots on target	Shots off target	Shooting Accuracy	% goals-to-shots	Total Shots (inc Blocked)	hit Woodwork	Penalty Goals	penalties not scored	...	Saves made	Saves-to-shots ratio	fouls Won	fouls conceded	offsides	Yellow Cards	Red Cards	Subs on	Subs off	Players used
4	France	3	[	]	37.9%	6.5%	1	0	0	...	6	54.6%	6		5		0	one	one
6	Greece	5	8	/	30.7%	19.2%		1	1	1	...		65.1%	9	1		+	/	+	12
7	Ukraine	2		up	21.2%	6.0%		0	0	0	...		76.5%	4	5	9		0	9	+

3 rowsx35 Columns

Step 12 Select the first 7 columns

euro12.iloc[:, 0:7]

Output:

Step 13 Select all columns except the last 3 columns

euro12.iloc[:,:-3]

Output:

Total Hit Clean

	Team	goals	Shots on target	Shots off target	Shooting accuracy	% goals-to-shots	Shots (inc. Blocked)	woodwork	Penalty Goals	penalties not scored	...	Sheets	Blocks	goals conceded	Saves made	Saves-to-shots ratio	fouls Won	fouls conceded	offsides	Yellow Cards	Red Cards
0	Croatia	4	13	12	51.9%	16%	32	0	0	0	...	0	10	3	13	81.3%	41	62	2	9	0
1	Czech Republic	4	13	18	41.9%	12.9%	39	0	0	0	...	1	10	6	9	60.1%	53	73	8	7	0
2	Denmark	4	10	10	50%	20%	27	1	0	0	...	1	10	5	10	66.7%	25	38	8	4	0
3	England	5	11	18	50%	17.2%	40	0	0	0	...	2	29	3	22	88.1%	43	45	6	5	0
4	France	3	22	24	37.9%	6.5%	65	1	0	0	...	1	7	5	6	54.6%	36	51	5	6	0
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	1	11	6	10	62.6%	63	49	12	4	0
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	1	23	7	13	65.1%	67	48	12	9	1
7	Italy	6	34	45	43%	7.5%	110	2	0	0	...	2	18	7	20	74.1%	101	89	16	16	0
8	Netherlands	2	12	36	25%	4.1%	60	2	0	0	...	0	9	5	12	70.6%	35	30	3	5	0
9	Poland	2	15	23	39.4%	5.2%	48	0	0	0	...	0	8	3	6	66.7%	48	56	3	7	1
10	Portugal	6	22	42	34.3%	9.3%	82	6	0	0	...	2	11	4	10	71.5%	73	90	10	12	0
11	Republic of Ireland	1	7	12	36.8%	5.2%	28	0	0	0	...	0	23	9	17	65.4%	43	51	11	6	1
12	Russia	5	9	31	22.5%	12.5%	59	2	0	0	...	0	8	3	10	77%	34	43	4	6	0
13	Spain	12	42	33	55.9%	16%	100	0	1	0	...	5	8	1	15	93.8%	102	83	19	11	0
14	Sweden	5	17	19	47.2%	13.8%	39	3	0	0	...	1	12	5	8	61.6%	35	51	7	7	0
15	Ukraine	2	7	26	21.2%	6%	38	0	0	0	...	0	4	4	13	76.5%	48	31	4	5	0

rowsx32 columns

Step 14 Find the hit ratio in England (England), Italy (Italy) and Russia (Russia) (Shooting accuracy)

Euro12.loc[euro12. Team.isin (['England'Italy'Russia  ']), ['Team','Shooting accuracy']

Output:

Pandas exercises (ii)------data filtering and sorting