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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。因为没有足够的救济设施，2224 个乘客中有 1502 个乘客不幸遇难。事变曾经产生了，然而咱们能够从泰坦尼克号中的历史数据中发现一些数据法则吗？明天本文将会率领大家灵便的应用 pandas 来进行数据分析。
泰坦尼特号乘客数据
咱们从 kaggle 官网中下载了局部泰坦尼特号的乘客数据，次要蕴含上面几个字段：
| 变量名 | 含意 | 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。接下来咱们来看一下怎么应用 pandas 来对其进行数据分析。
应用 pandas 对数据进行剖析
引入依赖包
本文次要应用 pandas 和 matplotlib，所以须要首先进行上面的通用设置：
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读取和剖析数据
pandas 提供了一个 read_csv 办法能够很不便的读取一个 csv 数据，并将其转换为 DataFrame：
path = '../data/titanic.csv'
df = pd.read_csv(path)
df咱们看下读入的数据：
| 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 办法能够查看根本的统计信息：
| 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，能够这样抉择：
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应用 value_counts 能够对其进行统计：
embark_counts=df['Embarked'].value_counts()
embark_counts[:10]S 270
C 102
Q 46
Name: Embarked, dtype: int64从后果能够看出，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，咱们能够统计一下 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，咱们能够应用平均数对其填充：
clean_age1 = df['Age'].fillna(df['Age'].mean())
clean_age1.value_counts()能够看出平均数是 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应用平均数来作为年龄可能不是一个好主见，还有一种方法就是抛弃平均数：
clean_age2=df['Age'].dropna()
clean_age2
age_counts = clean_age2.value_counts()
ageset=age_counts.head(10)
ageset24.0 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图形化示意和矩阵转换
图形化对于数据分析十分有帮忙，咱们对于下面得出的前 10 名的 age 应用柱状图来示意：
import seaborn as sns
sns.barplot(x=ageset.index, y=ageset.values)
接下来咱们来做一个简单的矩阵变换，咱们先来过滤掉 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
接下来应用 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应用 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
咱们把同样 age 的人数加起来，而后应用 argsort 进行排序，失去排序过后的 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 中取出最初的 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 |
下面的操作能够简化为上面的代码：
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，不便前面的画图：
stack_subset = count_subset.stack()
stack_subsetAge 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: float64stack_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)
本文例子能够参考：https://github.com/ddean2009/…
本文已收录于 http://www.flydean.com/01-pandas-titanic/
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