Pandas进阶大神！从0到100你只差这篇文章！|csv|dataframe|log|python|索引

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作者：youerning | 来源：51CTO博客 | 编辑：真经君

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正文

一、数据对象

pandas主要有两种数据对象：Series、DataFrame

注: 后面代码使用pandas版本0.20.1，通过import pandas as pd引入

1. Series

Series是一种带有索引的序列对象。

简单创建如下：

# 通过传入一个序列给pd.Series初始化一个Series对象, 比如list
s1=pd.Series(list("1234"))
print(s1)
0 1
1 2
2 3
3 4
dtype:object

2. DataFrame

类似与数据库table有行列的数据对象。

创建方式如下：

# 通过传入一个numpy的二维数组或者dict对象给pd.DataFrame初始化一个DataFrame对象
# 通过numpy二维数组
import numpy as np
df1 = pd.DataFrame(np.random.randn(6,4))
print(df1)
0 1 2 3
0 -0.646340 -1.249943 0.393323 -1.561873
1 0.371630 0.069426 1.693097 0.907419
2 -0.328575 -0.256765 0.693798 -0.787343
3 1.875764 -0.416275 -1.028718 0.158259
4 1.644791 -1.321506 -0.33742
5 0.8206895 0.006391 -1.447894 0.506203 0.977295
# 通过dict字典
df2 = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' :pd.Series(1,index=list(range(4)),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : pd.Categorical(["test","train","test","train"]),
'F' : 'foo' })
print(df2)
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo

3. 索引

不管是Series对象还是DataFrame对象都有一个对对象相对应的索引，Series的索引类似于每个元素, DataFrame的索引对应着每一行。

查看：在创建对象的时候，每个对象都会初始化一个起始值为0，自增的索引列表, DataFrame同理。

# 打印对象的时候，第一列就是索引
print(s1)
0 1
1 2
2 3
3 4
dtype: object
# 或者只查看索引, DataFrame同理
print(s1.index)

二、增删查改

这里的增删查改主要基于DataFrame对象，为了有足够数据用于展示，这里选择tushare的数据。

1. tushare安装

pip install tushare

创建数据对象如下：

import tushare as ts
df = ts.get_k_data("000001")

DataFrame 行列，axis 图解：

2. 查询

查看每列的数据类型

# 查看df数据类型
df.dtypes
date object
open float64
close float64
high float64
low float64
volume float64
code object
dtype: object

查看指定指定数量的行：head函数默认查看前5行，tail函数默认查看后5行，可以传递指定的数值用于查看指定行数。

查看前5行
df.head()
date open close high low volume code
0 2015-12-23 9.927 9.935 10.174 9.871 1039018.0 000001
1 2015-12-24 9.919 9.823 9.998 9.744 640229.0 000001
2 2015-12-25 9.855 9.879 9.927 9.815 399845.0 000001
3 2015-12-28 9.895 9.537 9.919 9.537 822408.0 000001
4 2015-12-29 9.545 9.624 9.632 9.529 619802.0 000001
# 查看后5行
df.tail()
date open close high low volume code
636 2018-08-01 9.42 9.15 9.50 9.11 814081.0 000001
637 2018-08-02 9.13 8.94 9.15 8.88 931401.0 000001
638 2018-08-03 8.93 8.91 9.10 8.91 476546.0 000001
639 2018-08-06 8.94 8.94 9.11 8.89 554010.0 000001
640 2018-08-07 8.96 9.17 9.17 8.88 690423.0 000001
# 查看前10行
df.head(10)date open close high low volume code
0 2015-12-23 9.927 9.935 10.174 9.871 1039018.0 000001
1 2015-12-24 9.919 9.823 9.998 9.744 640229.0 000001
2 2015-12-25 9.855 9.879 9.927 9.815 399845.0 000001
3 2015-12-28 9.895 9.537 9.919 9.537 822408.0 000001
4 2015-12-29 9.545 9.624 9.632 9.529 619802.0 000001
5 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001
6 2015-12-31 9.632 9.545 9.656 9.537 491258.0 000001
7 2016-01-04 9.553 8.995 9.577 8.940 563497.0 000001
8 2016-01-05 8.972 9.075 9.210 8.876 663269.0 000001
9 2016-01-06 9.091 9.179 9.202 9.067 515706.0 000001

查看某一行或多行,某一列或多列

# 查看第一行
df[0:1]
date open close high low volume code
0 2015-12-23 9.927 9.935 10.174 9.871 1039018.0 000001
# 查看 10到20行
df[10:21]
date open close high low volume code
10 2016-01-07 9.083 8.709 9.083 8.685 174761.0 000001
11 2016-01-08 8.924 8.852 8.987 8.677 747527.0 000001
12 2016-01-11 8.757 8.566 8.820 8.502 732013.0 000001
13 2016-01-12 8.621 8.605 8.685 8.470 561642.0 000001
14 2016-01-13 8.669 8.526 8.709 8.518 391709.0 000001
15 2016-01-14 8.430 8.574 8.597 8.343 666314.0 000001
16 2016-01-15 8.486 8.327 8.597 8.295 448202.0 000001
17 2016-01-18 8.231 8.287 8.406 8.199 421040.0 000001
18 2016-01-19 8.319 8.526 8.582 8.287 501109.0 000001
19 2016-01-20 8.518 8.390 8.597 8.311 603752.0 000001
20 2016-01-21 8.343 8.215 8.558 8.215 606145.0 000001
# 查看看Date列前5个数据
df["date"].head() # 或者df.date.head()
0 2015-12-23
1 2015-12-24
2 2015-12-25
3 2015-12-28
4 2015-12-29
Name: date, dtype: object
# 查看看Date列,code列, open列前5个数据
df[["date","code", "open"]].head()
date code open
0 2015-12-23 000001 9.927
1 2015-12-24 000001 9.919
2 2015-12-25 000001 9.855
3 2015-12-28 000001 9.895
4 2015-12-29 000001 9.545

使用行列组合条件查询

# 查看date, code列的第10行
df.loc[10, ["date", "code"]]
date 2016-01-07
code 000001
Name: 10, dtype: object
# 查看date, code列的第10行到20行
df.loc[10:20, ["date", "code"]]
date code
10 2016-01-07 000001
11 2016-01-08 000001
12 2016-01-11 000001
13 2016-01-12 000001
14 2016-01-13 000001
15 2016-01-14 000001
16 2016-01-15 000001
17 2016-01-18 000001
18 2016-01-19 000001
19 2016-01-20 000001
20 2016-01-21 000001
# 查看第一行,open列的数据
df.loc[0, "open"]
9.9269999999999996

通过位置查询：值得注意的是上面的索引值就是特定的位置。

# 查看第1行()
df.iloc[0]
date 2015-12-24
open 9.919
close 9.823
high 9.998
low 9.744
volume 640229
code 000001
Name: 0, dtype: object
# 查看最后一行
df.iloc[-1]
date 2018-08-08
open 9.16
close 9.12
high 9.16
low 9.1
volume 29985
code 000001
Name: 640, dtype: object
# 查看第一列，前5个数值
df.iloc[:,0].head()
0 2015-12-24
1 2015-12-25
2 2015-12-28
3 2015-12-29
4 2015-12-30
Name: date, dtype: object
# 查看前2到4行，第1，3列
df.iloc[2:4,[0,2]]
date close
2 2015-12-28 9.537
3 2015-12-29 9.624

通过条件筛选：

查看open列大于10的前5行
df[df.open > 10].head()
date open close high low volume code
378 2017-07-14 10.483 10.570 10.609 10.337 1722570.0 000001
379 2017-07-17 10.619 10.483 10.987 10.396 3273123.0 000001
380 2017-07-18 10.425 10.716 10.803 10.299 2349431.0 000001
381 2017-07-19 10.657 10.754 10.851 10.551 1933075.0 000001
382 2017-07-20 10.745 10.638 10.880 10.580 1537338.0 000001
# 查看open列大于10且open列小于10.6的前五行
df[(df.open > 10) & (df.open < 10.6)].head()
date open close high low volume code
378 2017-07-14 10.483 10.570 10.609 10.337 1722570.0 000001
380 2017-07-18 10.425 10.716 10.803 10.299 2349431.0 000001
387 2017-07-27 10.550 10.422 10.599 10.363 1194490.0 000001
388 2017-07-28 10.441 10.569 10.638 10.412 819195.0 000001
390 2017-08-01 10.471 10.865 10.904 10.432 2035709.0 000001
# 查看open列大于10或open列小于10.6的前五行
df[(df.open > 10) | (df.open < 10.6)].head()
date open close high low volume code
0 2015-12-24 9.919 9.823 9.998 9.744 640229.0 000001
1 2015-12-25 9.855 9.879 9.927 9.815 399845.0 000001
2 2015-12-28 9.895 9.537 9.919 9.537 822408.0 000001
3 2015-12-29 9.545 9.624 9.632 9.529 619802.0 000001
4 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001

3. 增加

在前面已经简单的说明Series, DataFrame的创建，这里说一些常用有用的创建方式。

# 创建2018-08-08到2018-08-15的时间序列，默认时间间隔为Day
s2 = pd.date_range("20180808", periods=7)
print(s2)
DatetimeIndex(['2018-08-08', '2018-08-09', '2018-08-10', '2018-08-11',
'2018-08-12', '2018-08-13', '2018-08-14'],
dtype='datetime64[ns]', freq='D')
# 指定2018-08-08 00：00 到2018-08-09 00:00 时间间隔为小时
# freq参数可使用参数, 参考: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
s3 = pd.date_range("20180808", "20180809", freq="H")
print(s2)
DatetimeIndex(['2018-08-08 00:00:00', '2018-08-08 01:00:00',
'2018-08-08 02:00:00', '2018-08-08 03:00:00',
'2018-08-08 04:00:00', '2018-08-08 05:00:00',
'2018-08-08 06:00:00', '2018-08-08 07:00:00',
'2018-08-08 08:00:00', '2018-08-08 09:00:00',
'2018-08-08 10:00:00', '2018-08-08 11:00:00',
'2018-08-08 12:00:00', '2018-08-08 13:00:00',
'2018-08-08 14:00:00', '2018-08-08 15:00:00',
'2018-08-08 16:00:00', '2018-08-08 17:00:00',
'2018-08-08 18:00:00', '2018-08-08 19:00:00',
'2018-08-08 20:00:00', '2018-08-08 21:00:00',
'2018-08-08 22:00:00', '2018-08-08 23:00:00',
'2018-08-09 00:00:00'],
dtype='datetime64[ns]', freq='H')
# 通过已有序列创建时间序列
s4 = pd.to_datetime(df.date.head())
print(s4)
0 2015-12-24
1 2015-12-25
2 2015-12-28
3 2015-12-29
4 2015-12-30
Name: date, dtype: datetime64[ns]

4. 修改

# 将df 的索引修改为date列的数据,并且将类型转换为datetime类型
df.index = pd.to_datetime(df.date)
df.head()
date open close high low volume code date
2015-12-24 2015-12-24 9.919 9.823 9.998 9.744 640229.0 000001
2015-12-25 2015-12-25 9.855 9.879 9.927 9.815 399845.0 000001
2015-12-28 2015-12-28 9.895 9.537 9.919 9.537 822408.0 000001
2015-12-29 2015-12-29 9.545 9.624 9.632 9.529 619802.0 000001
2015-12-30 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001
# 修改列的字段
df.columns = ["Date", "Open","Close","High","Low","Volume","Code"]
print(df.head())
Date Open Close High Low Volume Code date
2015-12-24 2015-12-24 9.919 9.823 9.998 9.744 640229.0 000001
2015-12-25 2015-12-25 9.855 9.879 9.927 9.815 399845.0 000001
2015-12-28 2015-12-28 9.895 9.537 9.919 9.537 822408.0 000001
2015-12-29 2015-12-29 9.545 9.624 9.632 9.529 619802.0 000001
2015-12-30 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001
# 将Open列每个数值加1, apply方法并不直接修改源数据，所以需要将新值复制给df
df.Open = df.Open.apply(lambda x: x+1)
df.head()
Date Open Close High Low Volume Code date
2015-12-24 2015-12-24 10.919 9.823 9.998 9.744 640229.0 000001
2015-12-25 2015-12-25 10.855 9.879 9.927 9.815 399845.0 000001
2015-12-28 2015-12-28 10.895 9.537 9.919 9.537 822408.0 000001
2015-12-29 2015-12-29 10.545 9.624 9.632 9.529 619802.0 000001
2015-12-30 2015-12-30 10.624 9.632 9.640 9.513 532667.0 000001
# 将Open，Close列都数值上加1,如果多列，apply接收的对象是整个列
df[["Open", "Close"]].head().apply(lambda x: x.apply(lambda x: x+1))
Open Close
date
2015-12-24 11.919 10.823
2015-12-25 11.855 10.879
2015-12-28 11.895 10.537
2015-12-29 11.545 10.624
2015-12-30 11.624 10.632

5. 删除

通过drop方法drop指定的行或者列。

注意:drop方法并不直接修改源数据，如果需要使源dataframe对象被修改，需要传入inplace=True，通过之前的axis图解，知道行的值(或者说label)在axis=0，列的值(或者说label)在axis=1。

# 删除指定列,删除Open列
df.drop("Open", axis=1).head() #或者df.drop(df.columns[1])
Date Close High Low Volume Code date
2015-12-24 2015-12-24 9.823 9.998 9.744 640229.0 000001
2015-12-25 2015-12-25 9.879 9.927 9.815 399845.0 000001
2015-12-28 2015-12-28 9.537 9.919 9.537 822408.0 000001
2015-12-29 2015-12-29 9.624 9.632 9.529 619802.0 000001
2015-12-30 2015-12-30 9.632 9.640 9.513 532667.0 000001
# 删除第1，3列. 即Open,High列
df.drop(df.columns[[1,3]], axis=1).head() # 或df.drop(["Open", "High], axis=1).head()
Date Close Low Volume Code date
2015-12-24 2015-12-24 9.823 9.744 640229.0 000001
2015-12-25 2015-12-25 9.879 9.815 399845.0 000001
2015-12-28 2015-12-28 9.537 9.537 822408.0 000001
2015-12-29 2015-12-29 9.624 9.529 619802.0 000001
2015-12-30 2015-12-30 9.632 9.513 532667.0 000001

三、pandas常用函数

1. 统计

# descibe方法会计算每列数据对象是数值的count, mean, std, min, max, 以及一定比率的值
df.describe()
Open Close High Low Volume
count 641.0000 641.0000 641.0000 641.0000 641.0000
mean 10.7862 9.7927 9.8942 9.6863 833968.6162
std 1.5962 1.6021 1.6620 1.5424 607731.6934
min 8.6580 7.6100 7.7770 7.4990 153901.0000
25% 9.7080 8.7180 8.7760 8.6500 418387.0000
50% 10.0770 9.0960 9.1450 8.9990 627656.0000
75% 11.8550 10.8350 10.9920 10.7270 1039297.0000
max 15.9090 14.8600 14.9980 14.4470 4262825.0000
# 单独统计Open列的平均值
df.Open.mean()
10.786248049922001
# 查看居于95%的值, 默认线性拟合
df.Open.quantile(0.95)
14.187
# 查看Open列每个值出现的次数
df.Open.value_counts().head()
9.8050 12
9.8630 10
9.8440 10
9.8730 10
9.8830 8
Name: Open, dtype: int64

2. 缺失值处理

删除或者填充缺失值。

# 删除含有NaN的任意行
df.dropna(how='any')
# 删除含有NaN的任意列
df.dropna(how='any', axis=1)
# 将NaN的值改为5
df.fillna(value=5)

3. 排序

按行或者列排序, 默认也不修改源数据。

# 按列排序
df.sort_index(axis=1).head()
Close Code Date High Low Open Volume
date
2015-12-24 9.8230 000001 2015-12-24 9.9980 9.7440 10.9190 640229.0000
2015-12-25 1.0000 000001 2015-12-25 1.0000 9.8150 10.8550 399845.0000
2015-12-28 1.0000 000001 2015-12-28 1.0000 9.5370 10.8950 822408.0000
2015-12-29 9.6240 000001 2015-12-29 9.6320 9.5290 10.5450 619802.0000
2015-12-30 9.6320 000001 2015-12-30 9.6400 9.5130 10.6240 532667.0000
# 按行排序，不递增
df.sort_index(ascending=False).head()
Date Open Close High Low Volume Code
date
2018-08-08 2018-08-08 10.1600 9.1100 9.1600 9.0900 153901.0000 000001
2018-08-07 2018-08-07 9.9600 9.1700 9.1700 8.8800 690423.0000 000001
2018-08-06 2018-08-06 9.9400 8.9400 9.1100 8.8900 554010.0000 000001
2018-08-03 2018-08-03 9.9300 8.9100 9.1000 8.9100 476546.0000 000001
2018-08-02 2018-08-02 10.1300 8.9400 9.1500 8.8800 931401.0000 000001

安装某一列的值排序

# 按照Open列的值从小到大排序
df.sort_values(by="Open")
Date Open Close High Low Volume Code
date 2016-03-01 2016-03-01 8.6580 7.7220 7.7770 7.6260 377910.0000 000001
2016-02-15 2016-02-15 8.6900 7.7930 7.8410 7.6820 278499.0000 000001
2016-01-29 2016-01-29 8.7540 7.9610 8.0240 7.7140 544435.0000 000001
2016-03-02 2016-03-02 8.7620 8.0400 8.0640 7.7380 676613.0000 000001
2016-02-26 2016-02-26 8.7770 7.7930 7.8250 7.6900 392154.0000 000001

4. 合并

concat, 按照行方向或者列方向合并。

# 分别取0到2行，2到4行，4到9行组成一个列表，通过concat方法按照axis=0，行方向合并, axis参数不指定，默认为0
split_rows = [df.iloc[0:2,:],df.iloc[2:4,:], df.iloc[4:9]]
pd.concat(split_rows)
Date Open Close High Low Volume Code
date
2015-12-24 2015-12-24 10.9190 9.8230 9.9980 9.7440 640229.0000 000001
2015-12-25 2015-12-25 10.8550 1.0000 1.0000 9.8150 399845.0000 000001
2015-12-28 2015-12-28 10.8950 1.0000 1.0000 9.5370 822408.0000 000001
2015-12-29 2015-12-29 10.5450 9.6240 9.6320 9.5290 619802.0000 000001
2015-12-30 2015-12-30 10.6240 9.6320 9.6400 9.5130 532667.0000 000001
2015-12-31 2015-12-31 10.6320 9.5450 9.6560 9.5370 491258.0000 000001
2016-01-04 2016-01-04 10.5530 8.9950 9.5770 8.9400 563497.0000 000001
2016-01-05 2016-01-05 9.9720 9.0750 9.2100 8.8760 663269.0000 000001
2016-01-06 2016-01-06 10.0910 9.1790 9.2020 9.0670 515706.0000 000001
# 分别取2到3列，3到5列，5列及以后列数组成一个列表，通过concat方法按照axis=1，列方向合并
split_columns = [df.iloc[:,1:2], df.iloc[:,2:4], df.iloc[:,4:]]
pd.concat(split_columns, axis=1).head()
Open Close High Low Volume Code date
2015-12-24 10.9190 9.8230 9.9980 9.7440 640229.0000 000001
2015-12-25 10.8550 1.0000 1.0000 9.8150 399845.0000 000001
2015-12-28 10.8950 1.0000 1.0000 9.5370 822408.0000 000001
2015-12-29 10.5450 9.6240 9.6320 9.5290 619802.0000 000001
2015-12-30 10.6240 9.6320 9.6400 9.5130 532667.0000 000001

追加行, 相应的还有insert, 插入插入到指定位置

# 将第一行追加到最后一行
df.append(df.iloc[0,:], ignore_index=True).tail()
Date Open Close High Low Volume Code
637 2018-08-03 9.9300 8.9100 9.1000 8.9100 476546.0000 000001
638 2018-08-06 9.9400 8.9400 9.1100 8.8900 554010.0000 000001
639 2018-08-07 9.9600 9.1700 9.1700 8.8800 690423.0000 000001
640 2018-08-08 10.1600 9.1100 9.1600 9.0900 153901.0000 000001
641 2015-12-24 10.9190 9.8230 9.9980 9.7440 640229.0000 000001

5. 对象复制

由于dataframe是引用对象，所以需要显示调用copy方法用以复制整个dataframe对象。

四、绘图

pandas的绘图是使用matplotlib，如果想要画的更细致, 可以使用matplotplib，不过简单的画一些图还是不错的。

因为上图太麻烦，这里就不配图了，可以在资源文件里面查看pandas-blog.ipynb文件或者自己敲一遍代码。

# 这里使用notbook，为了直接在输出中显示，需要以下配置
%matplotlib inline
# 绘制Open,Low,Close.High的线性图
df[["Open", "Low", "High", "Close"]].plot()
# 绘制面积图
df[["Open", "Low", "High", "Close"]].plot(kind="area")

五、数据读写

读写常见文件格式，如csv，excel，json等，甚至是读取“系统的剪切板”这个功能有时候很有用。直接将鼠标选中复制的内容读取创建dataframe对象。

# 将df数据保存到当前工作目录的stock.csv文件
df.to_csv("stock.csv")
# 查看stock.csv文件前5行
with open("stock.csv") as rf:
print(rf.readlines()[:5])
['date,Date,Open,Close,High,Low,Volume,Code\n', '2015-12-24,2015-12-24,9.919,9.823,9.998,9.744,640229.0,000001\n', '2015-12-25,2015-12-25,9.855,9.879,9.927,9.815,399845.0,000001\n', '2015-12-28,2015-12-28,9.895,9.537,9.919,9.537,822408.0,000001\n', '2015-12-29,2015-12-29,9.545,9.624,9.632,9.529,619802.0,000001\n']
# 读取stock.csv文件并将第一行作为index
df2 = pd.read_csv("stock.csv", index_col=0)
df2.head()
Date Open Close High Low Volume Code
date
2015-12-24 2015-12-24 9.9190 9.8230 9.9980 9.7440 640229.0000 1
2015-12-25 2015-12-25 9.8550 9.8790 9.9270 9.8150 399845.0000 1
2015-12-28 2015-12-28 9.8950 9.5370 9.9190 9.5370 822408.0000 1
2015-12-29 2015-12-29 9.5450 9.6240 9.6320 9.5290 619802.0000 1
2015-12-30 2015-12-30 9.6240 9.6320 9.6400 9.5130 532667.0000 1
# 读取stock.csv文件并将第一行作为index，并且将000001作为str类型读取, 不然会被解析成整数
df2 = pd.read_csv("stock.csv", index_col=0, dtype={"Code": str})
df2.head()

六、简单实例

这里以处理web日志为例，也许不太实用，因为ELK处理这些绰绰有余，不过喜欢什么自己来也未尝不可。

1. 分析access.log

日志文件: https://raw.githubusercontent.com/Apache-Labor/labor/master/labor-04/labor-04-example-access.log

2. 日志格式及示例

# 日志格式
# 字段说明, 参考:https://ru.wikipedia.org/wiki/Access.log
％h％l％u％t \“％r \”％> s％b \“％{Referer} i \”\“％{User-Agent} i \”
# 具体示例
75.249.65.145 US - [2015-09-02 10:42:51.003372] "GET /cms/tina-access-editor-for-download/ HTTP/1.1" 200 7113 "-" "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)" www.example.com 124.165.3.7 443 redirect-handler - + "-" Vea2i8CoAwcAADevXAgAAAAB TLSv1.2 ECDHE-RSA-AES128-GCM-SHA256 701 12118 -% 88871 803 0 0 0 0

3. 读取并解析日志文件

解析日志文件

HOST = r'^(?P
SPACE = r'\s'
IDENTITY = r'\S+'
USER = r"\S+"
TIME = r'\[(?P.*?)\]'
# REQUEST = r'\"(?P
REQUEST = r'\"(?P
STATUS = r'(?P
SIZE = r'(?P
REFER = r"\S+"
USER_AGENT = r'\"(?P
REGEX = HOST+SPACE+IDENTITY+SPACE+USER+SPACE+TIME+SPACE+REQUEST+SPACE+STATUS+SPACE+SIZE+SPACE+IDENTITY+USER_AGENT+SPACE
line = '79.81.243.171 - - [30/Mar/2009:20:58:31 +0200] "GET /exemples.php HTTP/1.1" 200 11481 "http://www.facades.fr/" "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; .NET CLR 1.0.3705; .NET CLR 1.1.4322; Media Center PC 4.0; .NET CLR 2.0.50727)" "-"'
reg = re.compile(REGEX)
reg.match(line).groups()

.*?)'

.*?)\"'

.+?)\s(?P

.*?)\"'

\d{3})'

\S+)'

.*?)\"'

将数据注入DataFrame对象

COLUMNS = ["Host", "Time", "Method", "Path", "Protocol", "status", "size", "User_Agent"]
field_lis = []
with open("access.log") as rf:
for line in rf:
# 由于一些记录不能匹配,所以需要捕获异常, 不能捕获的数据格式如下
# 80.32.156.105 - - [27/Mar/2009:13:39:51 +0100] "GET HTTP/1.1" 400 - "-" "-" "-"
# 由于重点不在写正则表达式这里就略过了
try:
fields = reg.match(line).groups()
except Exception as e:
#print(e)
#print(line)
pass
field_lis.append(fields)
log_df = pd.DataFrame(field_lis)
# 修改列名
log_df.columns = COLUMNS
def parse_time(value):
try:
return pd.to_datetime(value)
except Exception as e:
print(e)
print(value)
# 将Time列的值修改成pandas可解析的时间格式
log_df.Time = log_df.Time.apply(lambda x: x.replace(":", " ", 1))
log_df.Time = log_df.Time.apply(parse_time)
# 修改index, 将Time列作为index，并drop掉在Time列
log_df.index = pd.to_datetime(log_df.Time)
log_df.drop("Time", inplace=True)
log_df.head()
Host Time Method Path Protocol status size User_Agent
Time
2009-03-22 06:00:32 88.191.254.20 2009-03-22 06:00:32 GET / HTTP/1.0 200 8674 "-
2009-03-22 06:06:20 66.249.66.231 2009-03-22 06:06:20 GET /popup.php?choix=-89 HTTP/1.1 200 1870 "Mozilla/5.0 (compatible; Googlebot/2.1; +htt...
2009-03-22 06:11:20 66.249.66.231 2009-03-22 06:11:20 GET /specialiste.php HTTP/1.1 200 10743 "Mozilla/5.0 (compatible; Googlebot/2.1; +htt...
2009-03-22 06:40:06 83.198.250.175 2009-03-22 06:40:06 GET / HTTP/1.1 200 8714 "Mozilla/4.0 (compatible; MSIE 7.0; Windows N...
2009-03-22 06:40:06 83.198.250.175 2009-03-22 06:40:06 GET /style.css HTTP/1.1 200 1692 "Mozilla/4.0 (compatible; MSIE 7.0; Windows N...

查看数据类型

# 查看数据类型
log_df.dtypes
Host object
Time datetime64[ns]
Method object
Path object
Protocol object
status object
size object
User_Agent object
dtype: object

由上可知, 除了Time字段是时间类型，其他都是object，但是Size, Status应该为数字

def parse_number(value):
try:
return pd.to_numeric(value)
except Exception as e:
pass
return 0
# 将Size，Status字段值改为数值类型
log_df[["Status","Size"]] = log_df[["Status","Size"]].apply(lambda x: x.apply(parse_number))
log_df.dtypes
Host object
Time datetime64[ns]
Method object
Path object
Protocol object
Status int64
Size int64
User_Agent object
dtype: object

统计status数据

# 统计不同status值的次数
log_df.Status.value_counts()
200 5737
304 1540
404 1186
400 251
302 37
403 3
206 2
Name: Status, dtype: int64

绘制pie图

log_df.Status.value_counts().plot(kind="pie", figsize=(10,8))

查看日志文件时间跨度

log_df.index.max() - log_df.index.min()
Timedelta('15 days 11:12:03')

分别查看起始，终止时间

print(log_df.index.max())
print(log_df.index.min())
2009-04-06 17:12:35
2009-03-22 06:00:32

按照此方法还可以统计Method, User_Agent字段，不过User_Agent还需要额外清洗以下数据。

统计top 10 IP地址

91.121.31.184 745
88.191.254.20 441
41.224.252.122 420
194.2.62.185 255
86.75.35.144 184
208.89.192.106 170
79.82.3.8 161
90.3.72.207 157
62.147.243.132 150
81.249.221.143 141
Name: Host, dtype: int64

绘制请求走势图

log_df2 = log_df.copy()
# 为每行加一个request字段，值为1
log_df2["Request"] = 1
# 每一小时统计一次request数量，并将NaN值替代为0，最后绘制线性图，尺寸为16x9
log_df2.Request.resample("H").sum().fillna(0).plot(kind="line",figsize=(16,10))

分别绘图

分别对202，304，404状态重新取样，并放在一个列表里面
req_df_lis = [
log_df2[log_df2.Status == 200].Request.resample("H").sum().fillna(0),
log_df2[log_df2.Status == 304].Request.resample("H").sum().fillna(0),
log_df2[log_df2.Status == 404].Request.resample("H").sum().fillna(0)
]
# 将三个dataframe组合起来
req_df = pd.concat(req_df_lis,axis=1)
req_df.columns = ["200", "304", "404"]
# 绘图
req_df.plot(figsize=(16,10))