大家好，我是辣條，

Pandas 是Python的核心資料分析支持庫，提供了快速、靈活、明確的資料結構，旨在簡單、直觀地處理關系型、標記型資料，Pandas常用于處理帶行列標簽的矩陣資料、與 SQL 或 Excel 表類似的表格資料，應用于金融、統計、社會科學、工程等領域里的資料整理與清洗、資料分析與建模、資料可視化與制表等作業，

練習題索引

習題編號	內容	相應資料集
練習1 - 開始了解你的資料	探索Chipotle快餐資料	chipotle.tsv
練習2 - 資料過濾與排序	探索2012歐洲杯資料	Euro2012_stats.csv
[練習3 - 資料分組]	探索酒類消費資料	drinks.csv
[練習4 -Apply函式]	探索1960 - 2014 美國犯罪資料	US_Crime_Rates_1960_2014.csv
[練習5 - 合并]	探索虛擬姓名資料	練習中手動內置的資料
[練習6 - 統計	探索風速資料	wind.data
[練習7 - 可視化]	探索泰坦尼克災難資料	train.csv
[練習8 - 創建資料框	探索Pokemon資料	練習中手動內置的資料
[練習9 - 時間序列	探索Apple公司股價資料	Apple_stock.csv
[練習10 - 洗掉資料	探索Iris紙鳶花資料	iris.csv

練習1-開始了解你的資料

探索Chipotle快餐資料

步驟1 匯入必要的庫

In [7]:

# 運行以下代碼
import pandas as pd

步驟2 從如下地址匯入資料集

In [5]:

# 運行以下代碼

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

步驟3 將資料集存入一個名為chipo的資料框內

In [8]:

# 運行以下代碼

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

步驟4 查看前10行內容

In [9]:

# 運行以下代碼

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

步驟6 資料集中有多少個列(columns)

In [236]：

# 運行以下代碼

chipo.shape[1]

Out[236]:

5

步驟7 列印出全部的列名稱

In [237]:

# 運行以下代碼

chipo.columns

Out[237]:

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

       'item_price'],

      dtype='object')

步驟8 資料集的索引是怎樣的

In [238]:

# 運行以下代碼

chipo.index

Out[238]:

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

步驟9 被下單數最多商品(item)是什么?

In [239]:

# 運行以下代碼，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

步驟10 在item_name這一列中，一共有多少種商品被下單？

In [240]:

# 運行以下代碼

chipo['item_name'].nunique()

Out[240]:

50

步驟11 在choice_description中，下單次數最多的商品是什么？

In [241]:

# 運行以下代碼，存在一些小問題

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

步驟12 一共有多少商品被下單？

In [242]:

# 運行以下代碼

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

total_items_orders

Out[242]:

4972

步驟13 將item_price轉換為浮點數

In [243]:

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

步驟14 在該資料集對應的時期內，收入(revenue)是多少

In [244]:

# 運行以下代碼,已經做更正
chipo['sub_total'] = round(chipo['item_price'] * chipo['quantity'],2)
chipo['sub_total'].sum()

Out[244]:

39237.02

步驟15 在該資料集對應的時期內，一共有多少訂單？

In [245]:

# 運行以下代碼
chipo['order_id'].nunique()

Out[245]:

1834

步驟16 每一單(order)對應的平均總價是多少？

In [246]:

# 運行以下代碼，已經做過更正
chipo[['order_id','sub_total']].groupby(by=['order_id']
).agg({'sub_total':'sum'})['sub_total'].mean()

Out[246]:

21.39423118865867

步驟17 一共有多少種不同的商品被售出？

In [247]:

# 運行以下代碼
chipo['item_name'].nunique()

Out[247]:

練習2-資料過濾與排序

探索2012歐洲杯資料

步驟1 - 匯入必要的庫

In [248]:

# 運行以下代碼
import pandas as pd

步驟2 - 從以下地址匯入資料集

In [249]:

# 運行以下代碼
path2 = "./exercise_data/Euro2012_stats.csv"      # Euro2012_stats.csv

步驟3 - 將資料集命名為euro12

In [250]:

# 運行以下代碼
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

步驟4 只選取 Goals 這一列

In [251]:

# 運行以下代碼
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

步驟5 有多少球隊參與了2012歐洲杯？

In [252]:

# 運行以下代碼
euro12.shape[0]

Out[252]:

16

步驟6 該資料集中一共有多少列(columns)?

In [253]:

# 運行以下代碼
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

步驟7 將資料集中的列Team, Yellow Cards和Red Cards單獨存為一個名叫discipline的資料框

In [254]:

# 運行以下代碼
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

步驟8 對資料框discipline按照先Red Cards再Yellow Cards進行排序

In [255]:

# 運行以下代碼
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

步驟9 計算每個球隊拿到的黃牌數的平均值

In [256]:

# 運行以下代碼
round(discipline['Yellow Cards'].mean())

Out[256]:

7.0

步驟10 找到進球數Goals超過6的球隊資料

In [257]:

# 運行以下代碼
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

步驟11 選取以字母G開頭的球隊資料

In [258]:

# 運行以下代碼
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

步驟12 選取前7列

In [259]:

# 運行以下代碼
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

步驟13 選取除了最后3列之外的全部列

In [260]:

# 運行以下代碼
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

步驟14 找到英格蘭(England)、意大利(Italy)和俄羅斯(Russia)的射正率(Shooting Accuracy)

In [261]:

# 運行以下代碼
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%

練習3-資料分組

探索酒類消費資料

步驟1 匯入必要的庫

In [262]:

# 運行以下代碼
import pandas as pd

步驟2 從以下地址匯入資料

In [10]:

# 運行以下代碼
path3 ='./exercise_data/drinks.csv'    #'drinks.csv'

步驟3 將資料框命名為drinks

In [11]:

# 運行以下代碼
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

步驟4 哪個大陸(continent)平均消耗的啤酒(beer)更多？

In [12]:

# 運行以下代碼
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

步驟5 列印出每個大陸(continent)的紅酒消耗(wine_servings)的描述性統計值

In [13]:

# 運行以下代碼
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

步驟6 列印出每個大陸每種酒類別的消耗平均值

In [15]:

# 運行以下代碼
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

步驟7 列印出每個大陸每種酒類別的消耗中位數

In [268]:

# 運行以下代碼
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

步驟8 列印出每個大陸對spirit飲品消耗的平均值，最大值和最小值

In [269]:

# 運行以下代碼
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

練習4-Apply函式

探索1960 - 2014 美國犯罪資料

步驟1 匯入必要的庫

In [16]:

# 運行以下代碼
import numpy as np
import pandas as pd

步驟2 從以下地址匯入資料集

In [27]:

# 運行以下代碼
path4 = './exercise_data/US_Crime_Rates_1960_2014.csv'    # "US_Crime_Rates_1960_2014.csv"

步驟3 將資料框命名為crime

In [28]:

# 運行以下代碼
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

步驟4 每一列(column)的資料型別是什么樣的？

In [29]:

# 運行以下代碼
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

注意到了嗎，Year的資料型別為 int64，但是pandas有一個不同的資料型別去處理時間序列(time series)，我們現在來看看，

步驟5 將Year的資料型別轉換為 datetime64

In [30]:

# 運行以下代碼
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

步驟6 將列Year設定為資料框的索引

In [31]:

# 運行以下代碼
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

步驟7 洗掉名為Total的列

In [32]:

# 運行以下代碼
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

步驟8 按照Year對資料框進行分組并求和

*注意Population這一列，若直接對其求和，是不正確的**

In [34]:

# 更多關于 .resample 的介紹
# (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)
# 更多關于 Offset Aliases的介紹 
# (http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)
# 運行以下代碼
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

步驟9 何時是美國歷史上生存最危險的年代？

In [279]:

# 運行以下代碼
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]

練習5-合并

探索虛擬姓名資料

步驟1 匯入必要的庫

In [280]:

# 運行以下代碼
import numpy as np
import pandas as pd

步驟2 按照如下的元資料內容創建資料框

In [281]:

# 運行以下代碼
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]}

步驟3 將上述的資料框分別命名為data1, data2, data3

In [282]:

# 運行以下代碼
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'])

步驟4 將data1和data2兩個資料框按照行的維度進行合并，命名為all_data

In [283]:

# 運行以下代碼
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

步驟5 將data1和data2兩個資料框按照列的維度進行合并，命名為all_data_col

In [284]:

# 運行以下代碼
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

步驟6 列印data3

In [285]:

# 運行以下代碼
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

步驟7 按照subject_id的值對all_data和data3作合并

In [286]:

# 運行以下代碼
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

步驟8 對data1和data2按照subject_id作連接

In [287]:

# 運行以下代碼
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

步驟9 找到 data1 和 data2 合并之后的所有匹配結果

In [288]:

# 運行以下代碼
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

練習6-統計

探索風速資料

步驟1 匯入必要的庫

In [289]:

# 運行以下代碼
import pandas as pd
import datetime

步驟2 從以下地址匯入資料

In [290]:

import pandas as pd

In [35]:

# 運行以下代碼
path6 = "./exercise_data/wind.data"  # wind.data

步驟3 將資料作存盤并且設定前三列為合適的索引

In [292]:

import datetime

In [293]:

# 運行以下代碼
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

步驟4 2061年？我們真的有這一年的資料？創建一個函式并用它去修復這個bug

In [294]:

# 運行以下代碼
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

步驟5 將日期設為索引，注意資料型別，應該是datetime64[ns]

In [295]:

# 運行以下代碼
# 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

步驟6 對應每一個location，一共有多少資料值缺失

In [296]:

# 運行以下代碼
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

步驟7 對應每一個location，一共有多少完整的資料值

In [297]:

# 運行以下代碼
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

步驟8 對于全體資料，計算風速的平均值

In [298]:

# 運行以下代碼
data.mean().mean()

Out[298]:

10.227982360836924

步驟9 創建一個名為loc_stats的資料框去計算并存盤每個location的風速最小值，最大值，平均值和標準差

In [299]:

# 運行以下代碼
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

步驟10 創建一個名為day_stats的資料框去計算并存盤所有location的風速最小值，最大值，平均值和標準差

In [300]:

# 運行以下代碼
# 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

步驟11 對于每一個location，計算一月份的平均風速

注意，1961年的1月和1962年的1月應該區別對待

In [301]:

# 運行以下代碼
# 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

步驟12 對于資料記錄按照年為頻率取樣

In [302]:

# 運行以下代碼
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

步驟13 對于資料記錄按照月為頻率取樣

In [303]:

# 運行以下代碼
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

練習7-可視化

探索泰坦尼克災難資料

步驟1 匯入必要的庫

In [304]:

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

%matplotlib inline

步驟2 從以下地址匯入資料

In [36]:

# 運行以下代碼
path7 = '../input/pandas_exercise/pandas_exercise/exercise_data/train.csv'  # train.csv

步驟3 將資料框命名為titanic

In [306]:

# 運行以下代碼
titanic = pd.read_csv(path7)
titanic.head()

Out[306]:

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

步驟4 將PassengerId設定為索引

In [307]:

# 運行以下代碼
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

步驟5 繪制一個展示男女乘客比例的扇形圖

In [308]:

# 運行以下代碼
# 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()

步驟6 繪制一個展示船票Fare, 與乘客年齡和性別的散點圖

In [309]:

# 運行以下代碼
# 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)

步驟7 有多少人生還？

In [310]:

# 運行以下代碼
titanic.Survived.sum()

Out[310]:

342

步驟8 繪制一個展示船票價格的直方圖

In [311]:

# 運行以下代碼
# 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()

行業資料：添加即可領取PPT模板、簡歷模板、行業經典書籍PDF，
面試題庫：歷年經典，熱乎的大廠面試真題，持續更新中，添加獲取，
學習資料：含Python、爬蟲、資料分析、演算法等學習視頻和檔案，添加獲取
交流加群：大佬指點迷津，你的問題往往有人遇到過，技識訓助交流，