第一章Pandas快速入门

《深入浅出Pandas》第一章代码
数据来源：https://www.gairuo.com/file/data/dataset/team.xlsx

import numpy as np
import pandas as pd

file_path = 'E:\\Data_python\\anconda_code\\Dive_into_Pandas\\data_files\\'

team_path = 'team.xlsx'
team_df = pd.read_excel(file_path + team_path)
team_df.head(5)

	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

team_df.tail(5)

	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

team_df.sample(6)

	name	team	Q1	Q2	Q3	Q4
33	Adam	C	90	32	47	39
54	Ollie3	C	10	76	30	36
82	Finn	E	4	1	55	32
85	Liam	B	2	80	24	25
58	Lewis	B	4	34	77	28
27	Finley	D	62	73	84	68

team_df.shape

(100, 6)

team_df.info()

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

team_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

team_df.dtypes

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

team_df.axes

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

team_df.columns

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

# 建立索引（将team的name字段作为索引)
team_df.set_index('name', inplace=True)

team_df

	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
...	...	...	...	...	...
Gabriel	C	48	59	87	74
Austin7	C	21	31	30	43
Lincoln4	C	98	93	1	20
Eli	E	11	74	58	91
Ben	E	21	43	41	74

100 rows × 5 columns

team_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

team_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

team_df[['Q1','Q3']]

	Q1	Q3
name
Liver	89	24
Arry	36	37
Ack	57	18
Eorge	93	71
Oah	65	61
...	...	...
Gabriel	48	87
Austin7	21	30
Lincoln4	98	1
Eli	11	58
Ben	21	41

100 rows × 2 columns

team_df.loc[:,['Q1','Q2']]

	Q1	Q2
name
Liver	89	21
Arry	36	37
Ack	57	60
Eorge	93	96
Oah	65	49
...	...	...
Gabriel	48	59
Austin7	21	31
Lincoln4	98	93
Eli	11	74
Ben	21	43

100 rows × 2 columns

# 获取行
team_df[team_df.index == 'Liver']  #输出name==Liver的这行                                                                                                          

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

team_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

team_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

team_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

# 指定行和列
team_df.loc['Ben', 'Q1':'Q4'] # 只看Ben的四个季度成绩
team_df.loc['Eorge':'Alexander', 'team':'Q4'] # 指定行区间

	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

# 条件选择
# 单一条件
team_df[team_df.team == 'C']
team_df[team_df.Q1 > 90]
team_df[team_df.index=='Qsar']

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

# 多条件
team_df[(team_df.Q1 > 90) & (team_df.team == 'C')] # and 关系

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

team_df[team_df['team']=='C'].loc[team_df.Q1 > 90] # 多重条件筛选

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

排序

team_df.sort_values(by='Q1') # 按Q1列排序

	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
Elijah	B	97	89	15	46
Max	E	97	75	41	3
Lincoln4	C	98	93	1	20

100 rows × 5 columns

team_df.sort_values(by='Q3', ascending=False) # 降序

	team	Q1	Q2	Q3	Q4
name
David	B	21	47	99	2
Lfie	A	9	10	99	37
Luke6	D	15	97	95	99
Acob	B	61	95	94	8
Blake	A	78	23	93	9
...	...	...	...	...	...
Harley	B	2	99	12	13
Jake3	C	69	23	11	40
Jayden6	D	64	21	10	21
William	C	80	68	3	26
Lincoln4	C	98	93	1	20

100 rows × 5 columns

team_df.sort_values(['team','Q2'], ascending=[True, False]) # team升序，Q2降序

	team	Q1	Q2	Q3	Q4
name
Dylan	A	86	87	65	20
Nathan	A	87	77	62	13
Aaron	A	96	75	55	8
Stanley	A	69	71	39	97
Ack	A	57	60	18	84
...	...	...	...	...	...
Liver	E	89	21	24	64
Rory9	E	8	12	58	27
Jackson5	E	6	10	15	33
Roman	E	73	1	25	44
Finn	E	4	1	55	32

100 rows × 5 columns

分组聚合

# 按团队分组求和
team_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

team_df.groupby('team').mean() # 分组求平均

	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

# 不同列不同计算方法
team_df.groupby('team').agg({'Q1': sum,  # 求和
                             'Q2': 'count', # 计数
                             'Q3': 'mean', # 平均
                             'Q4': max}) # 最大值

	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

数据转换

# 转置
team_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

team_df.groupby('team').sum().T  # 二维数组可认为行转列

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

team_df.groupby('team').sum().stack()  # 分组透视到行

team    
A     Q1    1066
      Q2     639
      Q3     875
      Q4     783
B     Q1     975
      Q2    1218
      Q3    1202
      Q4    1136
C     Q1    1056
      Q2    1194
      Q3    1068
      Q4    1127
D     Q1     860
      Q2    1191
      Q3    1241
      Q4    1199
E     Q1     963
      Q2    1013
      Q3     881
      Q4    1033
dtype: int64

team_df.groupby('team').sum().unstack() # 分组后透视到列

    team
Q1  A       1066
    B        975
    C       1056
    D        860
    E        963
Q2  A        639
    B       1218
    C       1194
    D       1191
    E       1013
Q3  A        875
    B       1202
    C       1068
    D       1241
    E        881
Q4  A        783
    B       1136
    C       1127
    D       1199
    E       1033
dtype: int64

添加列

# 添加一个固定值的列
team_df['grade'] = 'Grade 1'

team_df.head(5)

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

# 添加汇总列
team_df['total'] = team_df['Q1'] + team_df['Q2'] + team_df['Q3'] + team_df['Q4']

team_df.head(5)

	team	Q1	Q2	Q3	Q4	grade	total
name
Liver	E	89	21	24	64	Grade 1	198
Arry	C	36	37	37	57	Grade 1	167
Ack	A	57	60	18	84	Grade 1	219
Eorge	C	93	96	71	78	Grade 1	338
Oah	D	65	49	61	86	Grade 1	261

# 将计算结果赋给新列
team_df['total1'] = team_df.loc[:,'Q1':'Q4'].apply(lambda x : sum(x), axis=1)
team_df['total2'] = team_df.sum(axis=1) # 把所有为数字的列相加
team_df['avg'] = team_df.total/4 # 添加平均值列

team_df

	team	Q1	Q2	Q3	Q4	grade	total	total1	total2	avg
name
Liver	E	89	21	24	64	Grade 1	198	198	1188	49.50
Arry	C	36	37	37	57	Grade 1	167	167	1002	41.75
Ack	A	57	60	18	84	Grade 1	219	219	1314	54.75
Eorge	C	93	96	71	78	Grade 1	338	338	2028	84.50
Oah	D	65	49	61	86	Grade 1	261	261	1566	65.25
...	...	...	...	...	...	...	...	...	...	...
Gabriel	C	48	59	87	74	Grade 1	268	268	1608	67.00
Austin7	C	21	31	30	43	Grade 1	125	125	750	31.25
Lincoln4	C	98	93	1	20	Grade 1	212	212	1272	53.00
Eli	E	11	74	58	91	Grade 1	234	234	1404	58.50
Ben	E	21	43	41	74	Grade 1	179	179	1074	44.75

100 rows × 10 columns