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面对小白的pandas命令手册+练习题【三万字详解】

互联网 diligentman 1小时前 1次浏览

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

面对小白的pandas命令手册+练习题【三万字详解】

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]:

5

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]:

50

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]:

4972

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]:

1834

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

面对小白的pandas命令手册+练习题【三万字详解】

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]:

16

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

面对小白的pandas命令手册+练习题【三万字详解】

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

面对小白的pandas命令手册+练习题【三万字详解】

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data1data2两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data1data2两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_datadata3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data1data2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 data1data2 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

面对小白的pandas命令手册+练习题【三万字详解】

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 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()

面对小白的pandas命令手册+练习题【三万字详解】

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)

面对小白的pandas命令手册+练习题【三万字详解】

步骤7 有多少人生还?

In [310]:

# 运行以下代码
titanic.Survived.sum()

Out[310]:

342

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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