import pandas as pd
df=pd.read_excel('team.xlsx')
df

	name	team	Q1	Q2	Q3	Q4
0	Liver	E	89	21	24	64
1	Arry	C	36	37	37	57
2	Ack	A	57	60	18	84
3	Eorge	C	93	96	71	78
4	Oah	D	65	49	61	86
...	...	...	...	...	...	...
95	Gabriel	C	48	59	87	74
96	Austin7	C	21	31	30	43
97	Lincoln4	C	98	93	1	20
98	Eli	E	11	74	58	91
99	Ben	E	21	43	41	74

100 rows × 6 columns

df.head()

	name	team	Q1	Q2	Q3	Q4
0	Liver	E	89	21	24	64
1	Arry	C	36	37	37	57
2	Ack	A	57	60	18	84
3	Eorge	C	93	96	71	78
4	Oah	D	65	49	61	86

df.tail()

	name	team	Q1	Q2	Q3	Q4
95	Gabriel	C	48	59	87	74
96	Austin7	C	21	31	30	43
97	Lincoln4	C	98	93	1	20
98	Eli	E	11	74	58	91
99	Ben	E	21	43	41	74

df.sample(5)

	name	team	Q1	Q2	Q3	Q4
27	Finley	D	62	73	84	68
0	Liver	E	89	21	24	64
32	Alexander	C	91	76	26	79
16	Joshua	A	63	4	80	30
73	Elliot	C	15	17	76	22

df.shape

(100, 6)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 6 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   name    100 non-null    object
 1   team    100 non-null    object
 2   Q1      100 non-null    int64 
 3   Q2      100 non-null    int64 
 4   Q3      100 non-null    int64 
 5   Q4      100 non-null    int64 
dtypes: int64(4), object(2)
memory usage: 4.8+ KB

df.describe()

	Q1	Q2	Q3	Q4
count	100.000000	100.000000	100.000000	100.000000
mean	49.200000	52.550000	52.670000	52.780000
std	29.962603	29.845181	26.543677	27.818524
min	1.000000	1.000000	1.000000	2.000000
25%	19.500000	26.750000	29.500000	29.500000
50%	51.500000	49.500000	55.000000	53.000000
75%	74.250000	77.750000	76.250000	75.250000
max	98.000000	99.000000	99.000000	99.000000

df.dtypes

name    object
team    object
Q1       int64
Q2       int64
Q3       int64
Q4       int64
dtype: object

df.axes

[RangeIndex(start=0, stop=100, step=1),
 Index(['name', 'team', 'Q1', 'Q2', 'Q3', 'Q4'], dtype='object')]

df.columns

Index(['name', 'team', 'Q1', 'Q2', 'Q3', 'Q4'], dtype='object')

df.set_index('name',inplace=True)

df.head()

	team	Q1	Q2	Q3	Q4
name
Liver	E	89	21	24	64
Arry	C	36	37	37	57
Ack	A	57	60	18	84
Eorge	C	93	96	71	78
Oah	D	65	49	61	86

df['Q1']
df.Q1

name
Liver       89
Arry        36
Ack         57
Eorge       93
Oah         65
            ..
Gabriel     48
Austin7     21
Lincoln4    98
Eli         11
Ben         21
Name: Q1, Length: 100, dtype: int64

df[['team','Q1']]

	team	Q1
name
Liver	E	89
Arry	C	36
Ack	A	57
Eorge	C	93
Oah	D	65
...	...	...
Gabriel	C	48
Austin7	C	21
Lincoln4	C	98
Eli	E	11
Ben	E	21

100 rows × 2 columns

df.loc[:,['team','Q1']]

	team	Q1
name
Liver	E	89
Arry	C	36
Ack	A	57
Eorge	C	93
Oah	D	65
...	...	...
Gabriel	C	48
Austin7	C	21
Lincoln4	C	98
Eli	E	11
Ben	E	21

100 rows × 2 columns

df[df.index=='Liver']

	team	Q1	Q2	Q3	Q4
name
Liver	E	89	21	24	64

df[0:3]

	team	Q1	Q2	Q3	Q4
name
Liver	E	89	21	24	64
Arry	C	36	37	37	57
Ack	A	57	60	18	84

df[0:10:2]

	team	Q1	Q2	Q3	Q4
name
Liver	E	89	21	24	64
Ack	A	57	60	18	84
Oah	D	65	49	61	86
Acob	B	61	95	94	8
Reddie	D	64	93	57	72

df.iloc[:10,:]

	team	Q1	Q2	Q3	Q4
name
Liver	E	89	21	24	64
Arry	C	36	37	37	57
Ack	A	57	60	18	84
Eorge	C	93	96	71	78
Oah	D	65	49	61	86
Harlie	C	24	13	87	43
Acob	B	61	95	94	8
Lfie	A	9	10	99	37
Reddie	D	64	93	57	72
Oscar	A	77	9	26	67

df.loc['Ben','Q1':'Q4']

Q1    21
Q2    43
Q3    41
Q4    74
Name: Ben, dtype: object

df.loc['Eorge':'Alexander','team':'Q4']

	team	Q1	Q2	Q3	Q4
name
Eorge	C	93	96	71	78
Oah	D	65	49	61	86
Harlie	C	24	13	87	43
Acob	B	61	95	94	8
Lfie	A	9	10	99	37
Reddie	D	64	93	57	72
Oscar	A	77	9	26	67
Leo	B	17	4	33	79
Logan	B	9	89	35	65
Archie	C	83	89	59	68
Theo	C	51	86	87	27
Thomas	B	80	48	56	41
James	E	48	77	52	11
Joshua	A	63	4	80	30
Henry	A	91	15	75	17
William	C	80	68	3	26
Max	E	97	75	41	3
Lucas	A	60	41	77	62
Ethan	D	79	45	89	88
Arthur	A	44	53	42	40
Mason	D	80	96	26	49
Isaac	E	74	23	28	65
Harrison	B	89	13	18	75
Teddy	E	71	91	21	48
Finley	D	62	73	84	68
Daniel	C	50	50	72	61
Riley	E	35	26	59	83
Edward	B	57	38	86	87
Joseph	E	67	87	87	93
Alexander	C	91	76	26	79

df[df.Q1>90]

	team	Q1	Q2	Q3	Q4
name
Eorge	C	93	96	71	78
Henry	A	91	15	75	17
Max	E	97	75	41	3
Alexander	C	91	76	26	79
Elijah	B	97	89	15	46
Ryan	E	92	70	64	31
Aaron	A	96	75	55	8
Lincoln4	C	98	93	1	20

df[df.team=='C']

	team	Q1	Q2	Q3	Q4
name
Arry	C	36	37	37	57
Eorge	C	93	96	71	78
Harlie	C	24	13	87	43
Archie	C	83	89	59	68
Theo	C	51	86	87	27
William	C	80	68	3	26
Daniel	C	50	50	72	61
Alexander	C	91	76	26	79
Adam	C	90	32	47	39
Sebastian	C	1	14	68	48
Tommy	C	29	44	28	76
Jake3	C	69	23	11	40
Ollie3	C	10	76	30	36
Matthew	C	44	33	41	98
Elliot	C	15	17	76	22
Ellis	C	34	34	77	42
Calum	C	14	91	16	82
Louis2	C	13	94	51	22
Connor	C	62	38	63	46
Gabriel	C	48	59	87	74
Austin7	C	21	31	30	43
Lincoln4	C	98	93	1	20

df[df.index=='Oscar']

	team	Q1	Q2	Q3	Q4
name
Oscar	A	77	9	26	67

df[(df['Q1']>90)&(df['team']=='C')]

	team	Q1	Q2	Q3	Q4
name
Eorge	C	93	96	71	78
Alexander	C	91	76	26	79
Lincoln4	C	98	93	1	20

df[df['team']=='C'].loc[df.Q1>90]

	team	Q1	Q2	Q3	Q4
name
Eorge	C	93	96	71	78
Alexander	C	91	76	26	79
Lincoln4	C	98	93	1	20

df.sort_values(by='Q1')

	team	Q1	Q2	Q3	Q4
name
Sebastian	C	1	14	68	48
Harley	B	2	99	12	13
Liam	B	2	80	24	25
Lewis	B	4	34	77	28
Finn	E	4	1	55	32
...	...	...	...	...	...
Eorge	C	93	96	71	78
Aaron	A	96	75	55	8
Max	E	97	75	41	3
Elijah	B	97	89	15	46
Lincoln4	C	98	93	1	20

100 rows × 5 columns

df.sort_values(by='Q1', ascending=False)

	team	Q1	Q2	Q3	Q4
name
Lincoln4	C	98	93	1	20
Max	E	97	75	41	3
Elijah	B	97	89	15	46
Aaron	A	96	75	55	8
Eorge	C	93	96	71	78
...	...	...	...	...	...
Lewis	B	4	34	77	28
Finn	E	4	1	55	32
Harley	B	2	99	12	13
Liam	B	2	80	24	25
Sebastian	C	1	14	68	48

100 rows × 5 columns

df.sort_values(['team','Q1'],ascending=[True,False])

	team	Q1	Q2	Q3	Q4
name
Aaron	A	96	75	55	8
Henry	A	91	15	75	17
Nathan	A	87	77	62	13
Dylan	A	86	87	65	20
Blake	A	78	23	93	9
...	...	...	...	...	...
Eli	E	11	74	58	91
Jude	E	8	45	13	65
Rory9	E	8	12	58	27
Jackson5	E	6	10	15	33
Finn	E	4	1	55	32

100 rows × 5 columns

df.groupby('team').sum()

	Q1	Q2	Q3	Q4
team
A	1066	639	875	783
B	975	1218	1202	1136
C	1056	1194	1068	1127
D	860	1191	1241	1199
E	963	1013	881	1033

df.groupby('team').mean()

	Q1	Q2	Q3	Q4
team
A	62.705882	37.588235	51.470588	46.058824
B	44.318182	55.363636	54.636364	51.636364
C	48.000000	54.272727	48.545455	51.227273
D	45.263158	62.684211	65.315789	63.105263
E	48.150000	50.650000	44.050000	51.650000

df.groupby('team').agg({'Q1':'sum','Q2':'count','Q3':'mean','Q4':'max'})

	Q1	Q2	Q3	Q4
team
A	1066	17	51.470588	97
B	975	22	54.636364	99
C	1056	22	48.545455	98
D	860	19	65.315789	99
E	963	20	44.050000	98

df.groupby('team').sum().T

team	A	B	C	D	E
Q1	1066	975	1056	860	963
Q2	639	1218	1194	1191	1013
Q3	875	1202	1068	1241	881
Q4	783	1136	1127	1199	1033

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