Task 2 数据分析
2.1 EDA 目标
- EDA的价值主要在于熟悉数据集，了解数据集，对数据集进行验证来确定所获得数据集可以用于接下来的机器学习或者深度学习使用。
- 当了解了数据集之后我们下一步就是要去了解变量间的相互关系以及变量与预测值之间的存在关系。
- 引导数据科学从业者进行数据处理以及特征工程的步骤,使数据集的结构和特征集让接下来的预测问题更加可靠。
- 完成对于数据的探索性分析，并对于数据进行一些图表或者文字总结并打卡。
2.2 内容介绍
- 载入各种数据科学以及可视化库:
- 数据科学库 pandas、numpy、scipy；
- 可视化库 matplotlib、seabon；
- 载入数据：
- 载入训练集和测试集；
- 简略观察数据(head()+shape)；
- 数据总览:
- 通过describe()来熟悉数据的相关统计量
- 通过info()来熟悉数据类型
- 判断数据缺失和异常
- 查看每列的存在nan情况
- 异常值检测
- 了解预测值的分布
- 总体分布概况
- 查看skewness and kurtosis
- 查看预测值的具体频数
2.3 代码示例
2.3.1 载入各种数据科学与可视化库
#coding:utf-8 #导入warnings包，利用过滤器来实现忽略警告语句。 import warnings warnings.filterwarnings('ignore') import missingno as msno import pandas as pd from pandas import DataFrame import matplotlib.pyplot as plt import seaborn as sns import numpy as np
2.3.2 载入训练集和测试集
导入训练集train.csv
import pandas as pd from pandas import DataFrame, Series import matplotlib.pyplot as plt Train_data = pd.read_csv('./train.csv')
导入测试集testA.csv
import pandas as pd from pandas import DataFrame, Series import matplotlib.pyplot as plt Test_data = pd.read_csv('./testA.csv')
所有特征集均脱敏处理(方便大家观看)
- id - 心跳信号分配的唯一标识
- heartbeat_signals - 心跳信号序列
- label - 心跳信号类别（0、1、2、3）
data.head().append(data.tail())——观察首尾数据
data.shape——观察数据集的行列信息
观察train首尾数据
Train_data.head().append(Train_data.tail())
<bound method DataFrame.info of id heartbeat_signals label 0 0 0.9912297987616655,0.9435330436439665,0.764677... 0.0 1 1 0.9714822034884503,0.9289687459588268,0.572932... 0.0 2 2 1.0,0.9591487564065292,0.7013782792997189,0.23... 2.0 3 3 0.9757952826275774,0.9340884687738161,0.659636... 0.0 4 4 0.0,0.055816398940721094,0.26129357194994196,0... 2.0 ... ... ... ... 99995 99995 1.0,0.677705342021188,0.22239242747868546,0.25... 0.0 99996 99996 0.9268571578157265,0.9063471198026871,0.636993... 2.0 99997 99997 0.9258351628306013,0.5873839035878395,0.633226... 3.0 99998 99998 1.0,0.9947621698382489,0.8297017704865509,0.45... 2.0 99999 99999 0.9259994004527861,0.916476635326053,0.4042900... 0.0 [100000 rows x 3 columns]>
观察train数据集的行列信息
Train_data.shape
(100000, 3)
观察testA首尾数据
Test_data.head().append(Test_data.tail())
id heartbeat_signals 0 100000 0.9915713654170097,1.0,0.6318163407681274,0.13... 1 100001 0.6075533139615096,0.5417083883163654,0.340694... 2 100002 0.9752726292239277,0.6710965234906665,0.686758... 3 100003 0.9956348033996116,0.9170249621481004,0.521096... 4 100004 1.0,0.8879490481178918,0.745564725322326,0.531... 19995 119995 1.0,0.8330283177934747,0.6340472606311671,0.63... 19996 119996 1.0,0.8259705825857048,0.4521053488322387,0.08... 19997 119997 0.951744840752379,0.9162611283848351,0.6675251... 19998 119998 0.9276692903808186,0.6771898159607004,0.242906... 19999 119999 0.6653212231837624,0.527064114047737,0.5166625...
观察testA数据集的行列信
Test_data.shape
(20000, 2)
要养成看数据集的head()以及shape的习惯，这会让你每一步更放心，导致接下里的连串的错误, 如果对自己的pandas等操作不放心，建议执行一步看一下，这样会有效的方便你进行理解函数并进行操作
2.3.3 总览数据概况
- describe种有每列的统计量，个数count、平均值mean、方差std、最小值min、中位数25% 50% 75% 、以及最大值 看这个信息主要是瞬间掌握数据的大概的范围以及每个值的异常值的判断，比如有的时候会发现999 9999 -1 等值这些其实都是nan的另外一种表达方式，有的时候需要注意下
- info 通过info来了解数据每列的type，有助于了解是否存在除了nan以外的特殊符号异常
data.describe()——获取数据的相关统计量
data.info()——获取数据类型
获取train数据的相关统计量
Train_data.describe()
id label count 100000.000000 100000.000000 mean 49999.500000 0.856960 std 28867.657797 1.217084 min 0.000000 0.000000 25% 24999.750000 0.000000 50% 49999.500000 0.000000 75% 74999.250000 2.000000 max 99999.000000 3.000000
获取train数据类型
Train_data.info
<bound method DataFrame.info of id heartbeat_signals label 0 0 0.9912297987616655,0.9435330436439665,0.764677... 0.0 1 1 0.9714822034884503,0.9289687459588268,0.572932... 0.0 2 2 1.0,0.9591487564065292,0.7013782792997189,0.23... 2.0 3 3 0.9757952826275774,0.9340884687738161,0.659636... 0.0 4 4 0.0,0.055816398940721094,0.26129357194994196,0... 2.0 ... ... ... ... 99995 99995 1.0,0.677705342021188,0.22239242747868546,0.25... 0.0 99996 99996 0.9268571578157265,0.9063471198026871,0.636993... 2.0 99997 99997 0.9258351628306013,0.5873839035878395,0.633226... 3.0 99998 99998 1.0,0.9947621698382489,0.8297017704865509,0.45... 2.0 99999 99999 0.9259994004527861,0.916476635326053,0.4042900... 0.0 [100000 rows x 3 columns]>
获取testA数据的相关统计量
Test_data.describe()
id count 20000.000000 mean 109999.500000 std 5773.647028 min 100000.000000 25% 104999.750000 50% 109999.500000 75% 114999.250000 max 119999.000000
获取testA数据类型
Test_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 20000 entries, 0 to 19999 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 20000 non-null int64 1 heartbeat_signals 20000 non-null object dtypes: int64(1), object(1) memory usage: 312.6+ KB
2.3.4 判断数据缺失和异常
data.isnull().sum()——查看每列的存在nan情况
查看trian每列的存在nan情况
Train_data.isnull().sum()
id 0 heartbeat_signals 0 label 0 dtype: int64
查看testA每列的存在nan情况
Test_data.isnull().sum()
id 0 heartbeat_signals 0 dtype: int64
2.3.5 了解预测值的分布
Train_data['label']
0 0.0 1 0.0 2 4.0 3 0.0 4 0.0 ... 99995 4.0 99996 0.0 99997 0.0 99998 0.0 99999 1.0 Name: label, Length: 100000, dtype: float64
Train_data['label'].value_counts()
0.0 58883 4.0 19660 2.0 12994 1.0 6522 3.0 1941 Name: label, dtype: int64
## 1) 总体分布概况（无界约翰逊分布等） import scipy.stats as st y = Train_data['label'] plt.figure(1); plt.title('Default') sns.distplot(y, rug=True, bins=20) plt.figure(2); plt.title('Normal') sns.distplot(y, kde=False, fit=st.norm) plt.figure(3); plt.title('Log Normal') sns.distplot(y, kde=False, fit=st.lognorm)
# 2）查看skewness and kurtosis sns.distplot(Train_data['label']); print("Skewness: %f" % Train_data['label'].skew()) print("Kurtosis: %f" % Train_data['label'].kurt())
Skewness: 0.917596 Kurtosis: -0.825276
Train_data.skew(), Train_data.kurt()
(id 0.000000 label 0.917596 dtype: float64, id -1.200000 label -0.825276 dtype: float64)
sns.distplot(Train_data.kurt(),color='orange',axlabel ='Kurtness')
## 3) 查看预测值的具体频数 plt.hist(Train_data['label'], orientation = 'vertical',histtype = 'bar', color ='red') plt.show()
