1. 数据分析实际案例之：pandas在泰坦尼特号乘客数据中的使用
简介
1912年4月15日，号称永不沉没的泰坦尼克号因为和冰山相撞沉没了。因为没有足够的救援设备，2224个乘客中有1502个乘客不幸遇难。事故已经发生了，但是我们可以从泰坦尼克号中的历史数据中发现一些数据规律吗？今天本文将会带领大家灵活的使用pandas来进行数据分析。
泰坦尼特号乘客数据
我们从kaggle官网中下载了部分泰坦尼特号的乘客数据，主要包含下面几个字段：
| 变量名 | 含义 | 取值 |
|---|---|---|
| survival | 是否生还 | 0 = No, 1 = Yes |
| pclass | 船票的级别 | 1 = 1st, 2 = 2nd, 3 = 3rd |
| sex | 性别 | |
| Age | 年龄 | |
| sibsp | 配偶信息 | |
| parch | 父母或者子女信息 | |
| ticket | 船票编 码 | |
| fare | 船费 | |
| cabin | 客舱编号 | |
| embarked | 登录的港口 | C = Cherbourg, Q = Queenstown, S = Southampton |
下载下来的文件是一个csv文件。接下来我们来看一下怎么使用pandas来对其进行数据分析。
使用pandas对数据进行分析
引入依赖包
本文主要使用pandas和matplotlib，所以需要首先进行下面的通用设置：
from numpy.random import randn
import numpy as np
np.random.seed(123)
import os
import matplotlib.pyplot as plt
import pandas as pd
plt.rc('figure', figsize=(10, 6))
np.set_printoptions(precision=4)
pd.options.display.max_rows = 20
读取和分析数据
pandas提供了一个read_csv方法可以很方便的读取一个csv数据，并将其转换为DataFrame：
path = '../data/titanic.csv'
df = pd.read_csv(path)
df
我们看下读入的数据：
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
| 5 | 897 | 3 | Svensson, Mr. Johan Cervin | male | 14.0 | 0 | 0 | 7538 | 9.2250 | NaN | S |
| 6 | 898 | 3 | Connolly, Miss. Kate | female | 30.0 | 0 | 0 | 330972 | 7.6292 | NaN | Q |
| 7 | 899 | 2 | Caldwell, Mr. Albert Francis | male | 26.0 | 1 | 1 | 248738 | 29.0000 | NaN | S |
| 8 | 900 | 3 | Abrahim, Mrs. Joseph (Sophie Halaut Easu) | female | 18.0 | 0 | 0 | 2657 | 7.2292 | NaN | C |
| 9 | 901 | 3 | Davies, Mr. John Samuel | male | 21.0 | 2 | 0 | A/4 48871 | 24.1500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 408 | 1300 | 3 | Riordan, Miss. Johanna Hannah"" | female | NaN | 0 | 0 | 334915 | 7.7208 | NaN | Q |
| 409 | 1301 | 3 | Peacock, Miss. Treasteall | female | 3.0 | 1 | 1 | SOTON/O.Q. 3101315 | 13.7750 | NaN | S |
| 410 | 1302 | 3 | Naughton, Miss. Hannah | female | NaN | 0 | 0 | 365237 | 7.7500 | NaN | Q |
| 411 | 1303 | 1 | Minahan, Mrs. William Edward (Lillian E Thorpe) | female | 37.0 | 1 | 0 | 19928 | 90.0000 | C78 | Q |
| 412 | 1304 | 3 | Henriksson, Miss. Jenny Lovisa | female | 28.0 | 0 | 0 | 347086 | 7.7750 | NaN | S |
| 413 | 1305 | 3 | Spector, Mr. Woolf | male | NaN | 0 | 0 | A.5. 3236 | 8.0500 | NaN | S |
| 414 | 1306 | 1 | Oliva y Ocana, Dona. Fermina | female | 39.0 | 0 | 0 | PC 17758 | 108.9000 | C105 | C |
| 415 | 1307 | 3 | Saether, Mr. Simon Sivertsen | male | 38.5 | 0 | 0 | SOTON/O.Q. 3101262 | 7.2500 | NaN | S |
| 416 | 1308 | 3 | Ware, Mr. Frederick | male | NaN | 0 | 0 | 359309 | 8.0500 | NaN | S |
| 417 | 1309 | 3 | Peter, Master. Michael J | male | NaN | 1 | 1 | 2668 | 22.3583 | NaN | C |
418 rows × 11 columns
调用df的describe方法可以查看基本的统计信息：
| PassengerId | Pclass | Age | SibSp | Parch | Fare | |
|---|---|---|---|---|---|---|
| count | 418.000000 | 418.000000 | 332.000000 | 418.000000 | 418.000000 | 417.000000 |
| mean | 1100.500000 | 2.265550 | 30.272590 | 0.447368 | 0.392344 | 35.627188 |
| std | 120.810458 | 0.841838 | 14.181209 | 0.896760 | 0.981429 | 55.907576 |
| min | 892.000000 | 1.000000 | 0.170000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 996.250000 | 1.000000 | 21.000000 | 0.000000 | 0.000000 | 7.895800 |
| 50% | 1100.500000 | 3.000000 | 27.000000 | 0.000000 | 0.000000 | 14.454200 |
| 75% | 1204.750000 | 3.000000 | 39.000000 | 1.000000 | 0.000000 | 31.500000 |
| max | 1309.000000 | 3.000000 | 76.000000 | 8.000000 | 9.000000 | 512.329200 |
如果要想查看乘客登录的港口，可以这样选择：
df['Embarked'][:10]
0 Q
1 S
2 Q
3 S
4 S
5 S
6 Q
7 S
8 C
9 S
Name: Embarked, dtype: object
使用value_counts 可以对其进行统计：
embark_counts=df['Embarked'].value_counts()
embark_counts[:10]
S 270
C 102
Q 46
Name: Embarked, dtype: int64
从结果可以看出，从S港口登录的乘客有270个，从C港口登录的乘客有102个，从Q港口登录的乘客有46个。
同样的，我们可以统计一下age信息：
age_counts=df['Age'].value_counts()
age_counts.head(10)
前10位的年龄如下：
24.0 17
21.0 17
22.0 16
30.0 15
18.0 13
27.0 12
26.0 12
25.0 11
23.0 11
29.0 10
Name: Age, dtype: int64
计算一下年龄的平均数：
df['Age'].mean()
30.272590361445783
实际上有些数据是没有年龄的，我们可以使用平均数对其填充：
clean_age1 = df['Age'].fillna(df['Age'].mean())
clean_age1.value_counts()
可以看出平均数是30.27，个数是86。
30.27259 86
24.00000 17
21.00000 17
22.00000 16
30.00000 15
18.00000 13
26.00000 12
27.00000 12
25.00000 11
23.00000 11
..
36.50000 1
40.50000 1
11.50000 1
34.00000 1
15.00000 1
7.00000 1
60.50000 1
26.50000 1
76.00000 1
34.50000 1
Name: Age, Length: 80, dtype: int64
使用平均数来作为年龄可能不是一个好主意，还有一种办法就是丢弃平均数：
clean_age2=df['Age'].dropna()
clean_age2
age_counts = clean_age2.value_counts()
ageset=age_counts.head(10)
ageset
24.0 17
21.0 17
22.0 16
30.0 15
18.0 13
27.0 12
26.0 12
25.0 11
23.0 11
29.0 10
Name: Age, dtype: int64
图形化表示和矩阵转换
图形化对于数据分析非常有帮助，我们对于上面得出的前10名的age使用柱状图来表示：
import seaborn as sns
sns.barplot(x=ageset.index, y=ageset.values)
接下来我们来做一个复杂的矩阵变换，我们先来过滤掉age和sex都为空的数据：
cframe=df[df.Age.notnull() & df.Sex.notnull()]
cframe
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
| 5 | 897 | 3 | Svensson, Mr. Johan Cervin | male | 14.0 | 0 | 0 | 7538 | 9.2250 | NaN | S |
| 6 | 898 | 3 | Connolly, Miss. Kate | female | 30.0 | 0 | 0 | 330972 | 7.6292 | NaN | Q |
| 7 | 899 | 2 | Caldwell, Mr. Albert Francis | male | 26.0 | 1 | 1 | 248738 | 29.0000 | NaN | S |
| 8 | 900 | 3 | Abrahim, Mrs. Joseph (Sophie Halaut Easu) | female | 18.0 | 0 | 0 | 2657 | 7.2292 | NaN | C |
| 9 | 901 | 3 | Davies, Mr. John Samuel | male | 21.0 | 2 | 0 | A/4 48871 | 24.1500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 403 | 1295 | 1 | Carrau, Mr. Jose Pedro | male | 17.0 | 0 | 0 | 113059 | 47.1000 | NaN | S |
| 404 | 1296 | 1 | Frauenthal, Mr. Isaac Gerald | male | 43.0 | 1 | 0 | 17765 | 27.7208 | D40 | C |
| 405 | 1297 | 2 | Nourney, Mr. Alfred (Baron von Drachstedt")" | male | 20.0 | 0 | 0 | SC/PARIS 2166 | 13.8625 | D38 | C |
| 406 | 1298 | 2 | Ware, Mr. William Jeffery | male | 23.0 | 1 | 0 | 28666 | 10.5000 | NaN | S |
| 407 | 1299 | 1 | Widener, Mr. George Dunton | male | 50.0 | 1 | 1 | 113503 | 211.5000 | C80 | C |
| 409 | 1301 | 3 | Peacock, Miss. Treasteall | female | 3.0 | 1 | 1 | SOTON/O.Q. 3101315 | 13.7750 | NaN | S |
| 411 | 1303 | 1 | Minahan, Mrs. William Edward (Lillian E Thorpe) | female | 37.0 | 1 | 0 | 19928 | 90.0000 | C78 | Q |
| 412 | 1304 | 3 | Henriksson, Miss. Jenny Lovisa | female | 28.0 | 0 | 0 | 347086 | 7.7750 | NaN | S |
| 414 | 1306 | 1 | Oliva y Ocana, Dona. Fermina | female | 39.0 | 0 | 0 | PC 17758 | 108.9000 | C105 | C |
| 415 | 1307 | 3 | Saether, Mr. Simon Sivertsen | male | 38.5 | 0 | 0 | SOTON/O.Q. 3101262 | 7.2500 | NaN | S |
332 rows × 11 columns
接下来使用groupby对age 和sex进行分组：
by_sex_age = cframe.groupby(['Age', 'Sex'])
by_sex_age.size()
Age Sex
0.17 female 1
0.33 male 1
0.75 male 1
0.83 male 1
0.92 female 1
1.00 female 3
2.00 female 1
male 1
3.00 female 1
5.00 male 1
..
60.00 female 3
60.50 male 1
61.00 male 2
62.00 male 1
63.00 female 1
male 1
64.00 female 2
male 1
67.00 male 1
76.00 female 1
Length: 115, dtype: int64
使用unstack将Sex的列数据变成行：
| Sex | female | male |
|---|---|---|
| Age | ||
| 0.17 | 1.0 | 0.0 |
| 0.33 | 0.0 | 1.0 |
| 0.75 | 0.0 | 1.0 |
| 0.83 | 0.0 | 1.0 |
| 0.92 | 1.0 | 0.0 |
| 1.00 | 3.0 | 0.0 |
| 2.00 | 1.0 | 1.0 |
| 3.00 | 1.0 | 0.0 |
| 5.00 | 0.0 | 1.0 |
| 6.00 | 0.0 | 3.0 |
| ... | ... | ... |
| 58.00 | 1.0 | 0.0 |
| 59.00 | 1.0 | 0.0 |
| 60.00 | 3.0 | 0.0 |
| 60.50 | 0.0 | 1.0 |
| 61.00 | 0.0 | 2.0 |
| 62.00 | 0.0 | 1.0 |
| 63.00 | 1.0 | 1.0 |
| 64.00 | 2.0 | 1.0 |
| 67.00 | 0.0 | 1.0 |
| 76.00 | 1.0 | 0.0 |
79 rows × 2 columns
我们把同样age的人数加起来，然后使用argsort进行排序，得到排序过后的index：
indexer = agg_counts.sum(1).argsort()
indexer.tail(10)
Age
58.0 37
59.0 31
60.0 29
60.5 32
61.0 34
62.0 22
63.0 38
64.0 27
67.0 26
76.0 30
dtype: int64
从agg_counts中取出最后的10个，也就是最大的10个：
count_subset = agg_counts.take(indexer.tail(10))
count_subset=count_subset.tail(10)
count_subset
| Sex | female | male |
|---|---|---|
| Age | ||
| 29.0 | 5.0 | 5.0 |
| 25.0 | 1.0 | 10.0 |
| 23.0 | 5.0 | 6.0 |
| 26.0 | 4.0 | 8.0 |
| 27.0 | 4.0 | 8.0 |
| 18.0 | 7.0 | 6.0 |
| 30.0 | 6.0 | 9.0 |
| 22.0 | 10.0 | 6.0 |
| 21.0 | 3.0 | 14.0 |
| 24.0 | 5.0 | 12.0 |
上面的操作可以简化为下面的代码：
agg_counts.sum(1).nlargest(10)
Age
21.0 17.0
24.0 17.0
22.0 16.0
30.0 15.0
18.0 13.0
26.0 12.0
27.0 12.0
23.0 11.0
25.0 11.0
29.0 10.0
dtype: float64
将count_subset 进行stack操作，方便后面的画图：
stack_subset = count_subset.stack()
stack_subset
Age Sex
29.0 female 5.0
male 5.0
25.0 female 1.0
male 10.0
23.0 female 5.0
male 6.0
26.0 female 4.0
male 8.0
27.0 female 4.0
male 8.0
18.0 female 7.0
male 6.0
30.0 female 6.0
male 9.0
22.0 female 10.0
male 6.0
21.0 female 3.0
male 14.0
24.0 female 5.0
male 12.0
dtype: float64
stack_subset.name = 'total'
stack_subset = stack_subset.reset_index()
stack_subset
| Age | Sex | total | |
|---|---|---|---|
| 0 | 29.0 | female | 5.0 |
| 1 | 29.0 | male | 5.0 |
| 2 | 25.0 | female | 1.0 |
| 3 | 25.0 | male | 10.0 |
| 4 | 23.0 | female | 5.0 |
| 5 | 23.0 | male | 6.0 |
| 6 | 26.0 | female | 4.0 |
| 7 | 26.0 | male | 8.0 |
| 8 | 27.0 | female | 4.0 |
| 9 | 27.0 | male | 8.0 |
| 10 | 18.0 | female | 7.0 |
| 11 | 18.0 | male | 6.0 |
| 12 | 30.0 | female | 6.0 |
| 13 | 30.0 | male | 9.0 |
| 14 | 22.0 | female | 10.0 |
| 15 | 22.0 | male | 6.0 |
| 16 | 21.0 | female | 3.0 |
| 17 | 21.0 | male | 14.0 |
| 18 | 24.0 | female | 5.0 |
| 19 | 24.0 | male | 12.0 |
作图如下：
sns.barplot(x='total', y='Age', hue='Sex', data=stack_subset)
本文例子可以参考： https://github.com/ddean2009/learn-ai/
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