面对小白的pandas命令手册+练习题【三万字详解】

大家好，我是辣条。

Pandas 是Python的核心数据分析支持库，提供了快速、灵活、明确的数据结构，旨在简单、直观地处理关系型、标记型数据。Pandas常用于处理带行列标签的矩阵数据、与 SQL 或 Excel 表类似的表格数据，应用于金融、统计、社会科学、工程等领域里的数据整理与清洗、数据分析与建模、数据可视化与制表等工作。

练习题索引

习题编号	内容	相应数据集
练习1 - 开始了解你的数据	探索Chipotle快餐数据	chipotle.tsv
练习2 - 数据过滤与排序	探索2012欧洲杯数据	Euro2012_stats.csv
[练习3 - 数据分组]	探索酒类消费数据	drinks.csv
[练习4 -Apply函数]	探索1960 - 2014 美国犯罪数据	US_Crime_Rates_1960_2014.csv
[练习5 - 合并]	探索虚拟姓名数据	练习中手动内置的数据
[练习6 - 统计	探索风速数据	wind.data
[练习7 - 可视化]	探索泰坦尼克灾难数据	train.csv
[练习8 - 创建数据框	探索Pokemon数据	练习中手动内置的数据
[练习9 - 时间序列	探索Apple公司股价数据	Apple_stock.csv
[练习10 - 删除数据	探索Iris纸鸢花数据	iris.csv

练习1-开始了解你的数据

探索Chipotle快餐数据

image description

步骤1 导入必要的库

In [7]:

# 运行以下代码
import pandas as pd

步骤2 从如下地址导入数据集

In [5]:

# 运行以下代码

path1 = "./exercise_data/chipotle.tsv"    # chipotle.tsv

步骤3 将数据集存入一个名为chipo的数据框内

In [8]:

# 运行以下代码

chipo = pd.read_csv(path1, sep = '\t')

步骤4 查看前10行内容

In [9]:

# 运行以下代码

chipo.head(10)

Out[9]:

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	Izze	[Clementine]	$3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98
5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	$10.98
6	3	1	Side of Chips	NaN	$1.69
7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	$11.75
8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...	$9.25
9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...	$9.25

步骤6 数据集中有多少个列(columns)

In [236]：

# 运行以下代码

chipo.shape[1]

Out[236]:

步骤7 打印出全部的列名称

In [237]:

# 运行以下代码

chipo.columns

Out[237]:

Index(['order_id', 'quantity', 'item_name', 'choice_description',

       'item_price'],

      dtype='object')

步骤8 数据集的索引是怎样的

In [238]:

# 运行以下代码

chipo.index

Out[238]:

RangeIndex(start=0, stop=4622, step=1)

步骤9 被下单数最多商品(item)是什么?

In [239]:

# 运行以下代码，做了修正

c = chipo[['item_name','quantity']].groupby(['item_name'],as_index=False).agg({'quantity':sum})

c.sort_values(['quantity'],ascending=False,inplace=True)

c.head()

Out[239]:

	item_name	quantity
17	Chicken Bowl	761
18	Chicken Burrito	591
25	Chips and Guacamole	506
39	Steak Burrito	386
10	Canned Soft Drink	351

步骤10 在item_name这一列中，一共有多少种商品被下单？

In [240]:

# 运行以下代码

chipo['item_name'].nunique()

Out[240]:

步骤11 在choice_description中，下单次数最多的商品是什么？

In [241]:

# 运行以下代码，存在一些小问题

chipo['choice_description'].value_counts().head()

Out[241]:

[Diet Coke]                                                                          134

[Coke]                                                                               123

[Sprite]                                                                              77

[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]]                42

[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]]     40

Name: choice_description, dtype: int64

步骤12 一共有多少商品被下单？

In [242]:

# 运行以下代码

total_items_orders = chipo['quantity'].sum()

total_items_orders

Out[242]:

步骤13 将item_price转换为浮点数

In [243]:

# 运行以下代码
dollarizer = lambda x: float(x[1:-1])
chipo['item_price'] = chipo['item_price'].apply(dollarizer)

步骤14 在该数据集对应的时期内，收入(revenue)是多少

In [244]:

# 运行以下代码,已经做更正
chipo['sub_total'] = round(chipo['item_price'] * chipo['quantity'],2)
chipo['sub_total'].sum()

Out[244]:

39237.02

步骤15 在该数据集对应的时期内，一共有多少订单？

In [245]:

# 运行以下代码
chipo['order_id'].nunique()

Out[245]:

步骤16 每一单(order)对应的平均总价是多少？

In [246]:

# 运行以下代码，已经做过更正
chipo[['order_id','sub_total']].groupby(by=['order_id']
).agg({'sub_total':'sum'})['sub_total'].mean()

Out[246]:

21.39423118865867

步骤17 一共有多少种不同的商品被售出？

In [247]:

# 运行以下代码
chipo['item_name'].nunique()

Out[247]:

练习2-数据过滤与排序

探索2012欧洲杯数据

image description

步骤1 - 导入必要的库

In [248]:

# 运行以下代码
import pandas as pd

步骤2 - 从以下地址导入数据集

In [249]:

# 运行以下代码
path2 = "./exercise_data/Euro2012_stats.csv"      # Euro2012_stats.csv

步骤3 - 将数据集命名为euro12

In [250]:

# 运行以下代码
euro12 = pd.read_csv(path2)
euro12

Out[250]:

	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
0	Croatia	4	13	12	51.9%	16.0%	32	0	0	0	...	13	81.3%	41	62	2	9	0	9	9	16
1	Czech Republic	4	13	18	41.9%	12.9%	39	0	0	0	...	9	60.1%	53	73	8	7	0	11	11	19
2	Denmark	4	10	10	50.0%	20.0%	27	1	0	0	...	10	66.7%	25	38	8	4	0	7	7	15
3	England	5	11	18	50.0%	17.2%	40	0	0	0	...	22	88.1%	43	45	6	5	0	11	11	16
4	France	3	22	24	37.9%	6.5%	65	1	0	0	...	6	54.6%	36	51	5	6	0	11	11	19
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	13	65.1%	67	48	12	9	1	12	12	20
7	Italy	6	34	45	43.0%	7.5%	110	2	0	0	...	20	74.1%	101	89	16	16	0	18	18	19
8	Netherlands	2	12	36	25.0%	4.1%	60	2	0	0	...	12	70.6%	35	30	3	5	0	7	7	15
9	Poland	2	15	23	39.4%	5.2%	48	0	0	0	...	6	66.7%	48	56	3	7	1	7	7	17
10	Portugal	6	22	42	34.3%	9.3%	82	6	0	0	...	10	71.5%	73	90	10	12	0	14	14	16
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	77.0%	34	43	4	6	0	7	7	16
13	Spain	12	42	33	55.9%	16.0%	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%	6.0%	38	0	0	0	...	13	76.5%	48	31	4	5	0	9	9	18

16 rows × 35 columns

步骤4 只选取 `Goals` 这一列

In [251]:

# 运行以下代码
euro12.Goals

Out[251]:

0      4
1      4
2      4
3      5
4      3
5     10
6      5
7      6
8      2
9      2
10     6
11     1
12     5
13    12
14     5
15     2
Name: Goals, dtype: int64

步骤5 有多少球队参与了2012欧洲杯？

In [252]:

# 运行以下代码
euro12.shape[0]

Out[252]:

步骤6 该数据集中一共有多少列(columns)?

In [253]:

# 运行以下代码
euro12.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16 entries, 0 to 15
Data columns (total 35 columns):
Team                          16 non-null object
Goals                         16 non-null int64
Shots on target               16 non-null int64
Shots off target              16 non-null int64
Shooting Accuracy             16 non-null object
% Goals-to-shots              16 non-null object
Total shots (inc. Blocked)    16 non-null int64
Hit Woodwork                  16 non-null int64
Penalty goals                 16 non-null int64
Penalties not scored          16 non-null int64
Headed goals                  16 non-null int64
Passes                        16 non-null int64
Passes completed              16 non-null int64
Passing Accuracy              16 non-null object
Touches                       16 non-null int64
Crosses                       16 non-null int64
Dribbles                      16 non-null int64
Corners Taken                 16 non-null int64
Tackles                       16 non-null int64
Clearances                    16 non-null int64
Interceptions                 16 non-null int64
Clearances off line           15 non-null float64
Clean Sheets                  16 non-null int64
Blocks                        16 non-null int64
Goals conceded                16 non-null int64
Saves made                    16 non-null int64
Saves-to-shots ratio          16 non-null object
Fouls Won                     16 non-null int64
Fouls Conceded                16 non-null int64
Offsides                      16 non-null int64
Yellow Cards                  16 non-null int64
Red Cards                     16 non-null int64
Subs on                       16 non-null int64
Subs off                      16 non-null int64
Players Used                  16 non-null int64
dtypes: float64(1), int64(29), object(5)
memory usage: 4.5+ KB

步骤7 将数据集中的列Team, Yellow Cards和Red Cards单独存为一个名叫discipline的数据框

In [254]:

# 运行以下代码
discipline = euro12[['Team', 'Yellow Cards', 'Red Cards']]
discipline

Out[254]:

	Team	Yellow Cards	Red Cards
0	Croatia	9	0
1	Czech Republic	7	0
2	Denmark	4	0
3	England	5	0
4	France	6	0
5	Germany	4	0
6	Greece	9	1
7	Italy	16	0
8	Netherlands	5	0
9	Poland	7	1
10	Portugal	12	0
11	Republic of Ireland	6	1
12	Russia	6	0
13	Spain	11	0
14	Sweden	7	0
15	Ukraine	5	0

步骤8 对数据框discipline按照先Red Cards再Yellow Cards进行排序

In [255]:

# 运行以下代码
discipline.sort_values(['Red Cards', 'Yellow Cards'], ascending = False)

Out[255]:

	Team	Yellow Cards	Red Cards
6	Greece	9	1
9	Poland	7	1
11	Republic of Ireland	6	1
7	Italy	16	0
10	Portugal	12	0
13	Spain	11	0
0	Croatia	9	0
1	Czech Republic	7	0
14	Sweden	7	0
4	France	6	0
12	Russia	6	0
3	England	5	0
8	Netherlands	5	0
15	Ukraine	5	0
2	Denmark	4	0
5	Germany	4	0

步骤9 计算每个球队拿到的黄牌数的平均值

In [256]:

# 运行以下代码
round(discipline['Yellow Cards'].mean())

Out[256]:

7.0

步骤10 找到进球数Goals超过6的球队数据

In [257]:

# 运行以下代码
euro12[euro12.Goals > 6]

Out[257]:

	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
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%	16.0%	100	0	1	0	...	15	93.8%	102	83	19	11	0	17	17	18

2 rows × 35 columns

步骤11 选取以字母G开头的球队数据

In [258]:

# 运行以下代码
euro12[euro12.Team.str.startswith('G')]

Out[258]:

	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
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	13	65.1%	67	48	12	9	1	12	12	20

2 rows × 35 columns

步骤12 选取前7列

In [259]:

# 运行以下代码
euro12.iloc[: , 0:7]

Out[259]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)
0	Croatia	4	13	12	51.9%	16.0%	32
1	Czech Republic	4	13	18	41.9%	12.9%	39
2	Denmark	4	10	10	50.0%	20.0%	27
3	England	5	11	18	50.0%	17.2%	40
4	France	3	22	24	37.9%	6.5%	65
5	Germany	10	32	32	47.8%	15.6%	80
6	Greece	5	8	18	30.7%	19.2%	32
7	Italy	6	34	45	43.0%	7.5%	110
8	Netherlands	2	12	36	25.0%	4.1%	60
9	Poland	2	15	23	39.4%	5.2%	48
10	Portugal	6	22	42	34.3%	9.3%	82
11	Republic of Ireland	1	7	12	36.8%	5.2%	28
12	Russia	5	9	31	22.5%	12.5%	59
13	Spain	12	42	33	55.9%	16.0%	100
14	Sweden	5	17	19	47.2%	13.8%	39
15	Ukraine	2	7	26	21.2%	6.0%	38

步骤13 选取除了最后3列之外的全部列

In [260]:

# 运行以下代码
euro12.iloc[: , :-3]

Out[260]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Clean 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%	16.0%	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	50.0%	20.0%	27	1	0	0	...	1	10	5	10	66.7%	25	38	8	4	0
3	England	5	11	18	50.0%	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	43.0%	7.5%	110	2	0	0	...	2	18	7	20	74.1%	101	89	16	16	0
8	Netherlands	2	12	36	25.0%	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	77.0%	34	43	4	6	0
13	Spain	12	42	33	55.9%	16.0%	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%	6.0%	38	0	0	0	...	0	4	4	13	76.5%	48	31	4	5	0

16 rows × 32 columns

步骤14 找到英格兰(England)、意大利(Italy)和俄罗斯(Russia)的射正率(Shooting Accuracy)

In [261]:

# 运行以下代码
euro12.loc[euro12.Team.isin(['England', 'Italy', 'Russia']), ['Team','Shooting Accuracy']]

Out[261]:

	Team	Shooting Accuracy
3	England	50.0%
7	Italy	43.0%
12	Russia	22.5%

练习3-数据分组

探索酒类消费数据

image description

步骤1 导入必要的库

In [262]:

# 运行以下代码
import pandas as pd

步骤2 从以下地址导入数据

In [10]:

# 运行以下代码
path3 ='./exercise_data/drinks.csv'    #'drinks.csv'

步骤3 将数据框命名为drinks

In [11]:

# 运行以下代码
drinks = pd.read_csv(path3)
drinks.head()

Out[11]:

	country	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol	continent
0	Afghanistan	0	0	0	0.0	AS
1	Albania	89	132	54	4.9	EU
2	Algeria	25	0	14	0.7	AF
3	Andorra	245	138	312	12.4	EU
4	Angola	217	57	45	5.9	AF

步骤4 哪个大陆(continent)平均消耗的啤酒(beer)更多？

In [12]:

# 运行以下代码
drinks.groupby('continent').beer_servings.mean()

Out[12]:

continent
AF     61.471698
AS     37.045455
EU    193.777778
OC     89.687500
SA    175.083333
Name: beer_servings, dtype: float64

步骤5 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值

In [13]:

# 运行以下代码
drinks.groupby('continent').wine_servings.describe()

Out[13]:

	count	mean	std	min	25%	50%	75%	max
continent
AF	53.0	16.264151	38.846419	0.0	1.0	2.0	13.00	233.0
AS	44.0	9.068182	21.667034	0.0	0.0	1.0	8.00	123.0
EU	45.0	142.222222	97.421738	0.0	59.0	128.0	195.00	370.0
OC	16.0	35.625000	64.555790	0.0	1.0	8.5	23.25	212.0
SA	12.0	62.416667	88.620189	1.0	3.0	12.0	98.50	221.0

步骤6 打印出每个大陆每种酒类别的消耗平均值

In [15]:

# 运行以下代码
drinks.groupby('continent').mean()

Out[15]:

	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol
continent
AF	61.471698	16.339623	16.264151	3.007547
AS	37.045455	60.840909	9.068182	2.170455
EU	193.777778	132.555556	142.222222	8.617778
OC	89.687500	58.437500	35.625000	3.381250
SA	175.083333	114.750000	62.416667	6.308333

步骤7 打印出每个大陆每种酒类别的消耗中位数

In [268]:

# 运行以下代码
drinks.groupby('continent').median()

Out[268]:

	beer_servings	spirit_servings	wine_servings	total_litres_of_pure_alcohol
continent
AF	32.0	3.0	2.0	2.30
AS	17.5	16.0	1.0	1.20
EU	219.0	122.0	128.0	10.00
OC	52.5	37.0	8.5	1.75
SA	162.5	108.5	12.0	6.85

步骤8 打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值

In [269]:

# 运行以下代码
drinks.groupby('continent').spirit_servings.agg(['mean', 'min', 'max'])

Out[269]:

	mean	min	max
continent
AF	16.339623	0	152
AS	60.840909	0	326
EU	132.555556	0	373
OC	58.437500	0	254
SA	114.750000	25	302

练习4-Apply函数

探索1960 - 2014 美国犯罪数据

image description

步骤1 导入必要的库

In [16]:

# 运行以下代码
import numpy as np
import pandas as pd

步骤2 从以下地址导入数据集

In [27]:

# 运行以下代码
path4 = './exercise_data/US_Crime_Rates_1960_2014.csv'    # "US_Crime_Rates_1960_2014.csv"

步骤3 将数据框命名为crime

In [28]:

# 运行以下代码
crime = pd.read_csv(path4)
crime.head()

Out[28]:

	Year	Population	Total	Violent	Property	Murder	Forcible_Rape	Robbery	Aggravated_assault	Burglary	Larceny_Theft	Vehicle_Theft
0	1960	179323175	3384200	288460	3095700	9110	17190	107840	154320	912100	1855400	328200
1	1961	182992000	3488000	289390	3198600	8740	17220	106670	156760	949600	1913000	336000
2	1962	185771000	3752200	301510	3450700	8530	17550	110860	164570	994300	2089600	366800
3	1963	188483000	4109500	316970	3792500	8640	17650	116470	174210	1086400	2297800	408300
4	1964	191141000	4564600	364220	4200400	9360	21420	130390	203050	1213200	2514400	472800

步骤4 每一列(column)的数据类型是什么样的？

In [29]:

# 运行以下代码
crime.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 55 entries, 0 to 54
Data columns (total 12 columns):
Year                  55 non-null int64
Population            55 non-null int64
Total                 55 non-null int64
Violent               55 non-null int64
Property              55 non-null int64
Murder                55 non-null int64
Forcible_Rape         55 non-null int64
Robbery               55 non-null int64
Aggravated_assault    55 non-null int64
Burglary              55 non-null int64
Larceny_Theft         55 non-null int64
Vehicle_Theft         55 non-null int64
dtypes: int64(12)
memory usage: 5.2 KB

注意到了吗，Year的数据类型为 int64，但是pandas有一个不同的数据类型去处理时间序列(time series)，我们现在来看看。

步骤5 将Year的数据类型转换为 `datetime64`

In [30]:

# 运行以下代码
crime.Year = pd.to_datetime(crime.Year, format='%Y')
crime.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 55 entries, 0 to 54
Data columns (total 12 columns):
Year                  55 non-null datetime64[ns]
Population            55 non-null int64
Total                 55 non-null int64
Violent               55 non-null int64
Property              55 non-null int64
Murder                55 non-null int64
Forcible_Rape         55 non-null int64
Robbery               55 non-null int64
Aggravated_assault    55 non-null int64
Burglary              55 non-null int64
Larceny_Theft         55 non-null int64
Vehicle_Theft         55 non-null int64
dtypes: datetime64[ns](1), int64(11)
memory usage: 5.2 KB

步骤6 将列Year设置为数据框的索引

In [31]:

# 运行以下代码
crime = crime.set_index('Year', drop = True)
crime.head()

Out[31]:

	Population	Total	Violent	Property	Murder	Forcible_Rape	Robbery	Aggravated_assault	Burglary	Larceny_Theft	Vehicle_Theft
Year
1960-01-01	179323175	3384200	288460	3095700	9110	17190	107840	154320	912100	1855400	328200
1961-01-01	182992000	3488000	289390	3198600	8740	17220	106670	156760	949600	1913000	336000
1962-01-01	185771000	3752200	301510	3450700	8530	17550	110860	164570	994300	2089600	366800
1963-01-01	188483000	4109500	316970	3792500	8640	17650	116470	174210	1086400	2297800	408300
1964-01-01	191141000	4564600	364220	4200400	9360	21420	130390	203050	1213200	2514400	472800

步骤7 删除名为Total的列

In [32]:

# 运行以下代码
del crime['Total']
crime.head()

Out[32]:

	Population	Violent	Property	Murder	Forcible_Rape	Robbery	Aggravated_assault	Burglary	Larceny_Theft	Vehicle_Theft
Year
1960-01-01	179323175	288460	3095700	9110	17190	107840	154320	912100	1855400	328200
1961-01-01	182992000	289390	3198600	8740	17220	106670	156760	949600	1913000	336000
1962-01-01	185771000	301510	3450700	8530	17550	110860	164570	994300	2089600	366800
1963-01-01	188483000	316970	3792500	8640	17650	116470	174210	1086400	2297800	408300
1964-01-01	191141000	364220	4200400	9360	21420	130390	203050	1213200	2514400	472800

In [33]:

crime.resample('10AS').sum()

Out[33]:

	Population	Violent	Property	Murder	Forcible_Rape	Robbery	Aggravated_assault	Burglary	Larceny_Theft	Vehicle_Theft
Year
1960-01-01	1915053175	4134930	45160900	106180	236720	1633510	2158520	13321100	26547700	5292100
1970-01-01	2121193298	9607930	91383800	192230	554570	4159020	4702120	28486000	53157800	9739900
1980-01-01	2371370069	14074328	117048900	206439	865639	5383109	7619130	33073494	72040253	11935411
1990-01-01	2612825258	17527048	119053499	211664	998827	5748930	10568963	26750015	77679366	14624418
2000-01-01	2947969117	13968056	100944369	163068	922499	4230366	8652124	21565176	67970291	11412834
2010-01-01	1570146307	6072017	44095950	72867	421059	1749809	3764142	10125170	30401698	3569080

步骤8 按照Year对数据框进行分组并求和

*注意Population这一列，若直接对其求和，是不正确的**

In [34]:

# 更多关于 .resample 的介绍
# (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)
# 更多关于 Offset Aliases的介绍 
# (http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)
# 运行以下代码
crimes = crime.resample('10AS').sum() # resample a time series per decades


# 用resample去得到“Population”列的最大值
population = crime['Population'].resample('10AS').max()

# 更新 "Population" 
crimes['Population'] = population

crimes

Out[34]:

	Population	Violent	Property	Murder	Forcible_Rape	Robbery	Aggravated_assault	Burglary	Larceny_Theft	Vehicle_Theft
Year
1960-01-01	201385000	4134930	45160900	106180	236720	1633510	2158520	13321100	26547700	5292100
1970-01-01	220099000	9607930	91383800	192230	554570	4159020	4702120	28486000	53157800	9739900
1980-01-01	248239000	14074328	117048900	206439	865639	5383109	7619130	33073494	72040253	11935411
1990-01-01	272690813	17527048	119053499	211664	998827	5748930	10568963	26750015	77679366	14624418
2000-01-01	307006550	13968056	100944369	163068	922499	4230366	8652124	21565176	67970291	11412834
2010-01-01	318857056	6072017	44095950	72867	421059	1749809	3764142	10125170	30401698	3569080

步骤9 何时是美国历史上生存最危险的年代？

In [279]:

# 运行以下代码
crime.idxmax(0)

Out[279]:

Population           2014-01-01
Violent              1992-01-01
Property             1991-01-01
Murder               1991-01-01
Forcible_Rape        1992-01-01
Robbery              1991-01-01
Aggravated_assault   1993-01-01
Burglary             1980-01-01
Larceny_Theft        1991-01-01
Vehicle_Theft        1991-01-01
dtype: datetime64[ns]

练习5-合并

探索虚拟姓名数据

步骤1 导入必要的库

In [280]:

# 运行以下代码
import numpy as np
import pandas as pd

步骤2 按照如下的元数据内容创建数据框

In [281]:

# 运行以下代码
raw_data_1 = {
        'subject_id': ['1', '2', '3', '4', '5'],
        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 
        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}

raw_data_2 = {
        'subject_id': ['4', '5', '6', '7', '8'],
        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], 
        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}

raw_data_3 = {
        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],
        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}

步骤3 将上述的数据框分别命名为`data1, data2, data3`

In [282]:

# 运行以下代码
data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])
data2 = pd.DataFrame(raw_data_2, columns = ['subject_id', 'first_name', 'last_name'])
data3 = pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])

步骤4 将`data1`和`data2`两个数据框按照行的维度进行合并，命名为`all_data`

In [283]:

# 运行以下代码
all_data = pd.concat([data1, data2])
all_data

Out[283]:

	subject_id	first_name	last_name
0	1	Alex	Anderson
1	2	Amy	Ackerman
2	3	Allen	Ali
3	4	Alice	Aoni
4	5	Ayoung	Atiches
0	4	Billy	Bonder
1	5	Brian	Black
2	6	Bran	Balwner
3	7	Bryce	Brice
4	8	Betty	Btisan

步骤5 将`data1`和`data2`两个数据框按照列的维度进行合并，命名为`all_data_col`

In [284]:

# 运行以下代码
all_data_col = pd.concat([data1, data2], axis = 1)
all_data_col

Out[284]:

	subject_id	first_name	last_name	subject_id	first_name	last_name
0	1	Alex	Anderson	4	Billy	Bonder
1	2	Amy	Ackerman	5	Brian	Black
2	3	Allen	Ali	6	Bran	Balwner
3	4	Alice	Aoni	7	Bryce	Brice
4	5	Ayoung	Atiches	8	Betty	Btisan

步骤6 打印`data3`

In [285]:

# 运行以下代码
data3

Out[285]:

	subject_id	test_id
0	1	51
1	2	15
2	3	15
3	4	61
4	5	16
5	7	14
6	8	15
7	9	1
8	10	61
9	11	16

步骤7 按照`subject_id`的值对`all_data`和`data3`作合并

In [286]:

# 运行以下代码
pd.merge(all_data, data3, on='subject_id')

Out[286]:

	subject_id	first_name	last_name	test_id
0	1	Alex	Anderson	51
1	2	Amy	Ackerman	15
2	3	Allen	Ali	15
3	4	Alice	Aoni	61
4	4	Billy	Bonder	61
5	5	Ayoung	Atiches	16
6	5	Brian	Black	16
7	7	Bryce	Brice	14
8	8	Betty	Btisan	15

步骤8 对`data1`和`data2`按照`subject_id`作连接

In [287]:

# 运行以下代码
pd.merge(data1, data2, on='subject_id', how='inner')

Out[287]:

	subject_id	first_name_x	last_name_x	first_name_y	last_name_y
0	4	Alice	Aoni	Billy	Bonder
1	5	Ayoung	Atiches	Brian	Black

步骤9 找到 `data1` 和 `data2` 合并之后的所有匹配结果

In [288]:

# 运行以下代码
pd.merge(data1, data2, on='subject_id', how='outer')

Out[288]:

	subject_id	first_name_x	last_name_x	first_name_y	last_name_y
0	1	Alex	Anderson	NaN	NaN
1	2	Amy	Ackerman	NaN	NaN
2	3	Allen	Ali	NaN	NaN
3	4	Alice	Aoni	Billy	Bonder
4	5	Ayoung	Atiches	Brian	Black
5	6	NaN	NaN	Bran	Balwner
6	7	NaN	NaN	Bryce	Brice
7	8	NaN	NaN	Betty	Btisan

练习6-统计

探索风速数据

image description

步骤1 导入必要的库

In [289]:

# 运行以下代码
import pandas as pd
import datetime

步骤2 从以下地址导入数据

In [290]:

import pandas as pd

In [35]:

# 运行以下代码
path6 = "./exercise_data/wind.data"  # wind.data

步骤3 将数据作存储并且设置前三列为合适的索引

In [292]:

import datetime

In [293]:

# 运行以下代码
data = pd.read_table(path6, sep = "\s+", parse_dates = [[0,1,2]]) 
data.head()

Out[293]:

	Yr_Mo_Dy	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
0	2061-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1	2061-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
2	2061-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
3	2061-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
4	2061-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

步骤4 2061年？我们真的有这一年的数据？创建一个函数并用它去修复这个bug

In [294]:

# 运行以下代码
def fix_century(x):
    year = x.year - 100 if x.year > 1989 else x.year
    return datetime.date(year, x.month, x.day)

# apply the function fix_century on the column and replace the values to the right ones
data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)

# data.info()
data.head()

Out[294]:

	Yr_Mo_Dy	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
0	1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1	1961-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
2	1961-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
3	1961-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
4	1961-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

步骤5 将日期设为索引，注意数据类型，应该是`datetime64[ns]`

In [295]:

# 运行以下代码
# transform Yr_Mo_Dy it to date type datetime64
data["Yr_Mo_Dy"] = pd.to_datetime(data["Yr_Mo_Dy"])

# set 'Yr_Mo_Dy' as the index
data = data.set_index('Yr_Mo_Dy')

data.head()
# data.info()

Out[295]:

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1961-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
1961-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
1961-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
1961-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

步骤6 对应每一个location，一共有多少数据值缺失

In [296]:

# 运行以下代码
data.isnull().sum()

Out[296]:

RPT    6
VAL    3
ROS    2
KIL    5
SHA    2
BIR    0
DUB    3
CLA    2
MUL    3
CLO    1
BEL    0
MAL    4
dtype: int64

步骤7 对应每一个location，一共有多少完整的数据值

In [297]:

# 运行以下代码
data.shape[0] - data.isnull().sum()

Out[297]:

RPT    6568
VAL    6571
ROS    6572
KIL    6569
SHA    6572
BIR    6574
DUB    6571
CLA    6572
MUL    6571
CLO    6573
BEL    6574
MAL    6570
dtype: int64

步骤8 对于全体数据，计算风速的平均值

In [298]:

# 运行以下代码
data.mean().mean()

Out[298]:

10.227982360836924

步骤9 创建一个名为`loc_stats`的数据框去计算并存储每个location的风速最小值，最大值，平均值和标准差

In [299]:

# 运行以下代码
loc_stats = pd.DataFrame()

loc_stats['min'] = data.min() # min
loc_stats['max'] = data.max() # max 
loc_stats['mean'] = data.mean() # mean
loc_stats['std'] = data.std() # standard deviations

loc_stats

Out[299]:

	min	max	mean	std
RPT	0.67	35.80	12.362987	5.618413
VAL	0.21	33.37	10.644314	5.267356
ROS	1.50	33.84	11.660526	5.008450
KIL	0.00	28.46	6.306468	3.605811
SHA	0.13	37.54	10.455834	4.936125
BIR	0.00	26.16	7.092254	3.968683
DUB	0.00	30.37	9.797343	4.977555
CLA	0.00	31.08	8.495053	4.499449
MUL	0.00	25.88	8.493590	4.166872
CLO	0.04	28.21	8.707332	4.503954
BEL	0.13	42.38	13.121007	5.835037
MAL	0.67	42.54	15.599079	6.699794

步骤10 创建一个名为`day_stats`的数据框去计算并存储所有location的风速最小值，最大值，平均值和标准差

In [300]:

# 运行以下代码
# create the dataframe
day_stats = pd.DataFrame()

# this time we determine axis equals to one so it gets each row.
day_stats['min'] = data.min(axis = 1) # min
day_stats['max'] = data.max(axis = 1) # max 
day_stats['mean'] = data.mean(axis = 1) # mean
day_stats['std'] = data.std(axis = 1) # standard deviations

day_stats.head()

Out[300]:

	min	max	mean	std
Yr_Mo_Dy
1961-01-01	9.29	18.50	13.018182	2.808875
1961-01-02	6.50	17.54	11.336364	3.188994
1961-01-03	6.17	18.50	11.641818	3.681912
1961-01-04	1.79	11.75	6.619167	3.198126
1961-01-05	6.17	13.33	10.630000	2.445356

步骤11 对于每一个location，计算一月份的平均风速

注意，1961年的1月和1962年的1月应该区别对待

In [301]:

# 运行以下代码
# creates a new column 'date' and gets the values from the index
data['date'] = data.index

# creates a column for each value from date
data['month'] = data['date'].apply(lambda date: date.month)
data['year'] = data['date'].apply(lambda date: date.year)
data['day'] = data['date'].apply(lambda date: date.day)

# gets all value from the month 1 and assign to janyary_winds
january_winds = data.query('month == 1')

# gets the mean from january_winds, using .loc to not print the mean of month, year and day
january_winds.loc[:,'RPT':"MAL"].mean()

Out[301]:

RPT    14.847325
VAL    12.914560
ROS    13.299624
KIL     7.199498
SHA    11.667734
BIR     8.054839
DUB    11.819355
CLA     9.512047
MUL     9.543208
CLO    10.053566
BEL    14.550520
MAL    18.028763
dtype: float64

步骤12 对于数据记录按照年为频率取样

In [302]:

# 运行以下代码
data.query('month == 1 and day == 1')

Out[302]:

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL	date	month	year	day
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04	1961-01-01	1	1961	1
1962-01-01	9.29	3.42	11.54	3.50	2.21	1.96	10.41	2.79	3.54	5.17	4.38	7.92	1962-01-01	1	1962	1
1963-01-01	15.59	13.62	19.79	8.38	12.25	10.00	23.45	15.71	13.59	14.37	17.58	34.13	1963-01-01	1	1963	1
1964-01-01	25.80	22.13	18.21	13.25	21.29	14.79	14.12	19.58	13.25	16.75	28.96	21.00	1964-01-01	1	1964	1
1965-01-01	9.54	11.92	9.00	4.38	6.08	5.21	10.25	6.08	5.71	8.63	12.04	17.41	1965-01-01	1	1965	1
1966-01-01	22.04	21.50	17.08	12.75	22.17	15.59	21.79	18.12	16.66	17.83	28.33	23.79	1966-01-01	1	1966	1
1967-01-01	6.46	4.46	6.50	3.21	6.67	3.79	11.38	3.83	7.71	9.08	10.67	20.91	1967-01-01	1	1967	1
1968-01-01	30.04	17.88	16.25	16.25	21.79	12.54	18.16	16.62	18.75	17.62	22.25	27.29	1968-01-01	1	1968	1
1969-01-01	6.13	1.63	5.41	1.08	2.54	1.00	8.50	2.42	4.58	6.34	9.17	16.71	1969-01-01	1	1969	1
1970-01-01	9.59	2.96	11.79	3.42	6.13	4.08	9.00	4.46	7.29	3.50	7.33	13.00	1970-01-01	1	1970	1
1971-01-01	3.71	0.79	4.71	0.17	1.42	1.04	4.63	0.75	1.54	1.08	4.21	9.54	1971-01-01	1	1971	1
1972-01-01	9.29	3.63	14.54	4.25	6.75	4.42	13.00	5.33	10.04	8.54	8.71	19.17	1972-01-01	1	1972	1
1973-01-01	16.50	15.92	14.62	7.41	8.29	11.21	13.54	7.79	10.46	10.79	13.37	9.71	1973-01-01	1	1973	1
1974-01-01	23.21	16.54	16.08	9.75	15.83	11.46	9.54	13.54	13.83	16.66	17.21	25.29	1974-01-01	1	1974	1
1975-01-01	14.04	13.54	11.29	5.46	12.58	5.58	8.12	8.96	9.29	5.17	7.71	11.63	1975-01-01	1	1975	1
1976-01-01	18.34	17.67	14.83	8.00	16.62	10.13	13.17	9.04	13.13	5.75	11.38	14.96	1976-01-01	1	1976	1
1977-01-01	20.04	11.92	20.25	9.13	9.29	8.04	10.75	5.88	9.00	9.00	14.88	25.70	1977-01-01	1	1977	1
1978-01-01	8.33	7.12	7.71	3.54	8.50	7.50	14.71	10.00	11.83	10.00	15.09	20.46	1978-01-01	1	1978	1

步骤13 对于数据记录按照月为频率取样

In [303]:

# 运行以下代码
data.query('day == 1')

Out[303]:

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL	date	month	year	day
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04	1961-01-01	1	1961	1
1961-02-01	14.25	15.12	9.04	5.88	12.08	7.17	10.17	3.63	6.50	5.50	9.17	8.00	1961-02-01	2	1961	1
1961-03-01	12.67	13.13	11.79	6.42	9.79	8.54	10.25	13.29	NaN	12.21	20.62	NaN	1961-03-01	3	1961	1
1961-04-01	8.38	6.34	8.33	6.75	9.33	9.54	11.67	8.21	11.21	6.46	11.96	7.17	1961-04-01	4	1961	1
1961-05-01	15.87	13.88	15.37	9.79	13.46	10.17	9.96	14.04	9.75	9.92	18.63	11.12	1961-05-01	5	1961	1
1961-06-01	15.92	9.59	12.04	8.79	11.54	6.04	9.75	8.29	9.33	10.34	10.67	12.12	1961-06-01	6	1961	1
1961-07-01	7.21	6.83	7.71	4.42	8.46	4.79	6.71	6.00	5.79	7.96	6.96	8.71	1961-07-01	7	1961	1
1961-08-01	9.59	5.09	5.54	4.63	8.29	5.25	4.21	5.25	5.37	5.41	8.38	9.08	1961-08-01	8	1961	1
1961-09-01	5.58	1.13	4.96	3.04	4.25	2.25	4.63	2.71	3.67	6.00	4.79	5.41	1961-09-01	9	1961	1
1961-10-01	14.25	12.87	7.87	8.00	13.00	7.75	5.83	9.00	7.08	5.29	11.79	4.04	1961-10-01	10	1961	1
1961-11-01	13.21	13.13	14.33	8.54	12.17	10.21	13.08	12.17	10.92	13.54	20.17	20.04	1961-11-01	11	1961	1
1961-12-01	9.67	7.75	8.00	3.96	6.00	2.75	7.25	2.50	5.58	5.58	7.79	11.17	1961-12-01	12	1961	1
1962-01-01	9.29	3.42	11.54	3.50	2.21	1.96	10.41	2.79	3.54	5.17	4.38	7.92	1962-01-01	1	1962	1
1962-02-01	19.12	13.96	12.21	10.58	15.71	10.63	15.71	11.08	13.17	12.62	17.67	22.71	1962-02-01	2	1962	1
1962-03-01	8.21	4.83	9.00	4.83	6.00	2.21	7.96	1.87	4.08	3.92	4.08	5.41	1962-03-01	3	1962	1
1962-04-01	14.33	12.25	11.87	10.37	14.92	11.00	19.79	11.67	14.09	15.46	16.62	23.58	1962-04-01	4	1962	1
1962-05-01	9.62	9.54	3.58	3.33	8.75	3.75	2.25	2.58	1.67	2.37	7.29	3.25	1962-05-01	5	1962	1
1962-06-01	5.88	6.29	8.67	5.21	5.00	4.25	5.91	5.41	4.79	9.25	5.25	10.71	1962-06-01	6	1962	1
1962-07-01	8.67	4.17	6.92	6.71	8.17	5.66	11.17	9.38	8.75	11.12	10.25	17.08	1962-07-01	7	1962	1
1962-08-01	4.58	5.37	6.04	2.29	7.87	3.71	4.46	2.58	4.00	4.79	7.21	7.46	1962-08-01	8	1962	1
1962-09-01	10.00	12.08	10.96	9.25	9.29	7.62	7.41	8.75	7.67	9.62	14.58	11.92	1962-09-01	9	1962	1
1962-10-01	14.58	7.83	19.21	10.08	11.54	8.38	13.29	10.63	8.21	12.92	18.05	18.12	1962-10-01	10	1962	1
1962-11-01	16.88	13.25	16.00	8.96	13.46	11.46	10.46	10.17	10.37	13.21	14.83	15.16	1962-11-01	11	1962	1
1962-12-01	18.38	15.41	11.75	6.79	12.21	8.04	8.42	10.83	5.66	9.08	11.50	11.50	1962-12-01	12	1962	1
1963-01-01	15.59	13.62	19.79	8.38	12.25	10.00	23.45	15.71	13.59	14.37	17.58	34.13	1963-01-01	1	1963	1
1963-02-01	15.41	7.62	24.67	11.42	9.21	8.17	14.04	7.54	7.54	10.08	10.17	17.67	1963-02-01	2	1963	1
1963-03-01	16.75	19.67	17.67	8.87	19.08	15.37	16.21	14.29	11.29	9.21	19.92	19.79	1963-03-01	3	1963	1
1963-04-01	10.54	9.59	12.46	7.33	9.46	9.59	11.79	11.87	9.79	10.71	13.37	18.21	1963-04-01	4	1963	1
1963-05-01	18.79	14.17	13.59	11.63	14.17	11.96	14.46	12.46	12.87	13.96	15.29	21.62	1963-05-01	5	1963	1
1963-06-01	13.37	6.87	12.00	8.50	10.04	9.42	10.92	12.96	11.79	11.04	10.92	13.67	1963-06-01	6	1963	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1976-07-01	8.50	1.75	6.58	2.13	2.75	2.21	5.37	2.04	5.88	4.50	4.96	10.63	1976-07-01	7	1976	1
1976-08-01	13.00	8.38	8.63	5.83	12.92	8.25	13.00	9.42	10.58	11.34	14.21	20.25	1976-08-01	8	1976	1
1976-09-01	11.87	11.00	7.38	6.87	7.75	8.33	10.34	6.46	10.17	9.29	12.75	19.55	1976-09-01	9	1976	1
1976-10-01	10.96	6.71	10.41	4.63	7.58	5.04	5.04	5.54	6.50	3.92	6.79	5.00	1976-10-01	10	1976	1
1976-11-01	13.96	15.67	10.29	6.46	12.79	9.08	10.00	9.67	10.21	11.63	23.09	21.96	1976-11-01	11	1976	1
1976-12-01	13.46	16.42	9.21	4.54	10.75	8.67	10.88	4.83	8.79	5.91	8.83	13.67	1976-12-01	12	1976	1
1977-01-01	20.04	11.92	20.25	9.13	9.29	8.04	10.75	5.88	9.00	9.00	14.88	25.70	1977-01-01	1	1977	1
1977-02-01	11.83	9.71	11.00	4.25	8.58	8.71	6.17	5.66	8.29	7.58	11.71	16.50	1977-02-01	2	1977	1
1977-03-01	8.63	14.83	10.29	3.75	6.63	8.79	5.00	8.12	7.87	6.42	13.54	13.67	1977-03-01	3	1977	1
1977-04-01	21.67	16.00	17.33	13.59	20.83	15.96	25.62	17.62	19.41	20.67	24.37	30.09	1977-04-01	4	1977	1
1977-05-01	6.42	7.12	8.67	3.58	4.58	4.00	6.75	6.13	3.33	4.50	19.21	12.38	1977-05-01	5	1977	1
1977-06-01	7.08	5.25	9.71	2.83	2.21	3.50	5.29	1.42	2.00	0.92	5.21	5.63	1977-06-01	6	1977	1
1977-07-01	15.41	16.29	17.08	6.25	11.83	11.83	12.29	10.58	10.41	7.21	17.37	7.83	1977-07-01	7	1977	1
1977-08-01	4.33	2.96	4.42	2.33	0.96	1.08	4.96	1.87	2.33	2.04	10.50	9.83	1977-08-01	8	1977	1
1977-09-01	17.37	16.33	16.83	8.58	14.46	11.83	15.09	13.92	13.29	13.88	23.29	25.17	1977-09-01	9	1977	1
1977-10-01	16.75	15.34	12.25	9.42	16.38	11.38	18.50	13.92	14.09	14.46	22.34	29.67	1977-10-01	10	1977	1
1977-11-01	16.71	11.54	12.17	4.17	8.54	7.17	11.12	6.46	8.25	6.21	11.04	15.63	1977-11-01	11	1977	1
1977-12-01	13.37	10.92	12.42	2.37	5.79	6.13	8.96	7.38	6.29	5.71	8.54	12.42	1977-12-01	12	1977	1
1978-01-01	8.33	7.12	7.71	3.54	8.50	7.50	14.71	10.00	11.83	10.00	15.09	20.46	1978-01-01	1	1978	1
1978-02-01	27.25	24.21	18.16	17.46	27.54	18.05	20.96	25.04	20.04	17.50	27.71	21.12	1978-02-01	2	1978	1
1978-03-01	15.04	6.21	16.04	7.87	6.42	6.67	12.29	8.00	10.58	9.33	5.41	17.00	1978-03-01	3	1978	1
1978-04-01	3.42	7.58	2.71	1.38	3.46	2.08	2.67	4.75	4.83	1.67	7.33	13.67	1978-04-01	4	1978	1
1978-05-01	10.54	12.21	9.08	5.29	11.00	10.08	11.17	13.75	11.87	11.79	12.87	27.16	1978-05-01	5	1978	1
1978-06-01	10.37	11.42	6.46	6.04	11.25	7.50	6.46	5.96	7.79	5.46	5.50	10.41	1978-06-01	6	1978	1
1978-07-01	12.46	10.63	11.17	6.75	12.92	9.04	12.42	9.62	12.08	8.04	14.04	16.17	1978-07-01	7	1978	1
1978-08-01	19.33	15.09	20.17	8.83	12.62	10.41	9.33	12.33	9.50	9.92	15.75	18.00	1978-08-01	8	1978	1
1978-09-01	8.42	6.13	9.87	5.25	3.21	5.71	7.25	3.50	7.33	6.50	7.62	15.96	1978-09-01	9	1978	1
1978-10-01	9.50	6.83	10.50	3.88	6.13	4.58	4.21	6.50	6.38	6.54	10.63	14.09	1978-10-01	10	1978	1
1978-11-01	13.59	16.75	11.25	7.08	11.04	8.33	8.17	11.29	10.75	11.25	23.13	25.00	1978-11-01	11	1978	1
1978-12-01	21.29	16.29	24.04	12.79	18.21	19.29	21.54	17.21	16.71	17.83	17.75	25.70	1978-12-01	12	1978	1

216 rows × 16 columns

练习7-可视化

探索泰坦尼克灾难数据

步骤1 导入必要的库

In [304]:

# 运行以下代码
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

%matplotlib inline

步骤2 从以下地址导入数据

In [36]:

# 运行以下代码
path7 = '../input/pandas_exercise/pandas_exercise/exercise_data/train.csv'  # train.csv

步骤3 将数据框命名为titanic

In [306]:

# 运行以下代码
titanic = pd.read_csv(path7)
titanic.head()

Out[306]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

步骤4 将PassengerId设置为索引

In [307]:

# 运行以下代码
titanic.set_index('PassengerId').head()

Out[307]:

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

步骤5 绘制一个展示男女乘客比例的扇形图

In [308]:

# 运行以下代码
# sum the instances of males and females
males = (titanic['Sex'] == 'male').sum()
females = (titanic['Sex'] == 'female').sum()

# put them into a list called proportions
proportions = [males, females]

# Create a pie chart
plt.pie(
    # using proportions
    proportions,
    
    # with the labels being officer names
    labels = ['Males', 'Females'],
    
    # with no shadows
    shadow = False,
    
    # with colors
    colors = ['blue','red'],
    
    # with one slide exploded out
    explode = (0.15 , 0),
    
    # with the start angle at 90%
    startangle = 90,
    
    # with the percent listed as a fraction
    autopct = '%1.1f%%'
    )

# View the plot drop above
plt.axis('equal')

# Set labels
plt.title("Sex Proportion")

# View the plot
plt.tight_layout()
plt.show()

步骤6 绘制一个展示船票`Fare`, 与乘客年龄和性别的散点图

In [309]:

# 运行以下代码
# creates the plot using
lm = sns.lmplot(x = 'Age', y = 'Fare', data = titanic, hue = 'Sex', fit_reg=False)

# set title
lm.set(title = 'Fare x Age')

# get the axes object and tweak it
axes = lm.axes
axes[0,0].set_ylim(-5,)
axes[0,0].set_xlim(-5,85)

Out[309]:

(-5, 85)

步骤7 有多少人生还？

In [310]:

# 运行以下代码
titanic.Survived.sum()

Out[310]:

步骤8 绘制一个展示船票价格的直方图

In [311]:

# 运行以下代码
# sort the values from the top to the least value and slice the first 5 items
df = titanic.Fare.sort_values(ascending = False)
df

# create bins interval using numpy
binsVal = np.arange(0,600,10)
binsVal

# create the plot
plt.hist(df, bins = binsVal)

# Set the title and labels
plt.xlabel('Fare')
plt.ylabel('Frequency')
plt.title('Fare Payed Histrogram')

# show the plot
plt.show()

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面对小白的pandas命令手册+练习题【三万字详解】

练习题索引

练习1-开始了解你的数据

探索Chipotle快餐数据

步骤1 导入必要的库

步骤2 从如下地址导入数据集

步骤3 将数据集存入一个名为chipo的数据框内

步骤4 查看前10行内容

步骤6 数据集中有多少个列(columns)

步骤7 打印出全部的列名称

步骤8 数据集的索引是怎样的

步骤9 被下单数最多商品(item)是什么?

步骤10 在item_name这一列中，一共有多少种商品被下单？

步骤11 在choice_description中，下单次数最多的商品是什么？

步骤12 一共有多少商品被下单？

步骤13 将item_price转换为浮点数

步骤14 在该数据集对应的时期内，收入(revenue)是多少

步骤15 在该数据集对应的时期内，一共有多少订单？

步骤16 每一单(order)对应的平均总价是多少？

步骤17 一共有多少种不同的商品被售出？

练习2-数据过滤与排序

探索2012欧洲杯数据

步骤1 - 导入必要的库

步骤2 - 从以下地址导入数据集

步骤3 - 将数据集命名为euro12

步骤4 只选取 Goals 这一列

步骤5 有多少球队参与了2012欧洲杯？

步骤6 该数据集中一共有多少列(columns)?

步骤7 将数据集中的列Team, Yellow Cards和Red Cards单独存为一个名叫discipline的数据框

步骤8 对数据框discipline按照先Red Cards再Yellow Cards进行排序

步骤9 计算每个球队拿到的黄牌数的平均值

步骤10 找到进球数Goals超过6的球队数据

步骤11 选取以字母G开头的球队数据

步骤12 选取前7列

步骤13 选取除了最后3列之外的全部列

步骤14 找到英格兰(England)、意大利(Italy)和俄罗斯(Russia)的射正率(Shooting Accuracy)

练习3-数据分组

探索酒类消费数据

步骤1 导入必要的库

步骤2 从以下地址导入数据

步骤3 将数据框命名为drinks

步骤4 哪个大陆(continent)平均消耗的啤酒(beer)更多？

步骤5 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值

步骤6 打印出每个大陆每种酒类别的消耗平均值

步骤7 打印出每个大陆每种酒类别的消耗中位数

步骤8 打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值

练习4-Apply函数

探索1960 - 2014 美国犯罪数据

步骤1 导入必要的库

步骤2 从以下地址导入数据集

步骤3 将数据框命名为crime

步骤4 每一列(column)的数据类型是什么样的？

步骤5 将Year的数据类型转换为 datetime64

步骤6 将列Year设置为数据框的索引

步骤7 删除名为Total的列

步骤8 按照Year对数据框进行分组并求和

步骤9 何时是美国历史上生存最危险的年代？

练习5-合并

探索虚拟姓名数据

步骤1 导入必要的库

步骤2 按照如下的元数据内容创建数据框

步骤3 将上述的数据框分别命名为data1, data2, data3

步骤4 将data1和data2两个数据框按照行的维度进行合并，命名为all_data

步骤5 将data1和data2两个数据框按照列的维度进行合并，命名为all_data_col

步骤6 打印data3

步骤7 按照subject_id的值对all_data和data3作合并

步骤8 对data1和data2按照subject_id作连接

步骤9 找到 data1 和 data2 合并之后的所有匹配结果

练习6-统计

探索风速数据

步骤1 导入必要的库

步骤2 从以下地址导入数据

步骤3 将数据作存储并且设置前三列为合适的索引

步骤4 2061年？我们真的有这一年的数据？创建一个函数并用它去修复这个bug

步骤5 将日期设为索引，注意数据类型，应该是datetime64[ns]

步骤6 对应每一个location，一共有多少数据值缺失

步骤7 对应每一个location，一共有多少完整的数据值

步骤8 对于全体数据，计算风速的平均值

步骤9 创建一个名为loc_stats的数据框去计算并存储每个location的风速最小值，最大值，平均值和标准差

步骤10 创建一个名为day_stats的数据框去计算并存储所有location的风速最小值，最大值，平均值和标准差

步骤4 只选取 `Goals` 这一列

步骤5 将Year的数据类型转换为 `datetime64`

步骤3 将上述的数据框分别命名为`data1, data2, data3`

步骤4 将`data1`和`data2`两个数据框按照行的维度进行合并，命名为`all_data`

步骤5 将`data1`和`data2`两个数据框按照列的维度进行合并，命名为`all_data_col`

步骤6 打印`data3`

步骤7 按照`subject_id`的值对`all_data`和`data3`作合并

步骤8 对`data1`和`data2`按照`subject_id`作连接

步骤9 找到 `data1` 和 `data2` 合并之后的所有匹配结果

步骤5 将日期设为索引，注意数据类型，应该是`datetime64[ns]`

步骤9 创建一个名为`loc_stats`的数据框去计算并存储每个location的风速最小值，最大值，平均值和标准差

步骤10 创建一个名为`day_stats`的数据框去计算并存储所有location的风速最小值，最大值，平均值和标准差

步骤6 绘制一个展示船票`Fare`, 与乘客年龄和性别的散点图