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Pandas进阶大神！从0到100你只差这篇文章！

發布時間：2024/9/15 编程问答 29 豆豆

生活随笔收集整理的這篇文章主要介紹了 Pandas进阶大神！从0到100你只差这篇文章！小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

作者：youerning

來源：51CTO博客

閱讀文本大概需要 8?分鐘

一、數據對象

pandas主要有兩種數據對象：Series、DataFrame

注:?后面代碼使用pandas版本0.20.1，通過import pandas as pd引入

1. Series

Series是一種帶有索引的序列對象。

簡單創建如下：

#?通過傳入一個序列給pd.Series初始化一個Series對象,?比如list s1=pd.Series(list("1234")) print(s1) 0????1 1????2 2????3 3????4 dtype:object

2. DataFrame

類似與數據庫table有行列的數據對象。

創建方式如下：

#?通過傳入一個numpy的二維數組或者dict對象給pd.DataFrame初始化一個DataFrame對象#?通過numpy二維數組 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#?通過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對象還是DataFrame對象都有一個對對象相對應的索引，Series的索引類似于每個元素, DataFrame的索引對應著每一行。

查看：在創建對象的時候，每個對象都會初始化一個起始值為0，自增的索引列表, DataFrame同理。

#?打印對象的時候，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、增刪查改

這里的增刪查改主要基于DataFrame對象，為了有足夠數據用于展示，這里選擇tushare的數據。

1. tushare安裝

pip?install?tushare

創建數據對象如下：

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

DataFrame 行列，axis 圖解：

2. 查詢

查看每列的數據類型

#?查看df數據類型 df.dtypes date???????object open????????float64 close????????float64 high?????????float64 low??????????float64 volume????float64 code???????object dtype:?object

查看指定指定數量的行：head函數默認查看前5行，tail函數默認查看后5行，可以傳遞指定的數值用于查看指定行數。

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個數據 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個數據 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

使用行列組合條件查詢

#?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列的數據 df.loc[0,?"open"] 9.9269999999999996

通過位置查詢：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個數值 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

通過條件篩選：

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

在前面已經簡單的說明Series, DataFrame的創建，這里說一些常用有用的創建方式。

#?創建2018-08-08到2018-08-15的時間序列，默認時間間隔為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?時間間隔為小時 #?freq參數可使用參數,?參考:?http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliasess3?=?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') #?通過已有序列創建時間序列 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

#?將df?的索引修改為date列的數據,并且將類型轉換為datetime類型 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 #?將Open列每個數值加1,?apply方法并不直接修改源數據，所以需要將新值復制給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 #?將Open，Close列都數值上加1,如果多列，apply接收的對象是整個列 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. 刪除

通過drop方法drop指定的行或者列。

注意:?drop方法并不直接修改源數據，如果需要使源dataframe對象被修改，需要傳入inplace=True，通過之前的axis圖解，知道行的值(或者說label)在axis=0，列的值(或者說label)在axis=1。

#?刪除指定列,刪除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 #?刪除第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常用函數

1. 統計

#?descibe方法會計算每列數據對象是數值的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#?單獨統計Open列的平均值 df.Open.mean() 10.786248049922001#?查看居于95%的值,?默認線性擬合 df.Open.quantile(0.95) 14.187#?查看Open列每個值出現的次數 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. 缺失值處理

刪除或者填充缺失值。

#?刪除含有NaN的任意行 df.dropna(how='any')#?刪除含有NaN的任意列 df.dropna(how='any',?axis=1)#?將NaN的值改為5 df.fillna(value=5)

3. 排序

按行或者列排序, 默認也不修改源數據。

#?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，不遞增 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

安裝某一列的值排序

#?按照Open列的值從小到大排序 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。

#?分別取0到2行，2到4行，4到9行組成一個列表，通過concat方法按照axis=0，行方向合并,?axis參數不指定，默認為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#?分別取2到3列，3到5列，5列及以后列數組成一個列表，通過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, 相應的還有insert, 插入插入到指定位置

#?將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. 對象復制

由于dataframe是引用對象，所以需要顯示調用copy方法用以復制整個dataframe對象。

四、繪圖

pandas的繪圖是使用matplotlib，如果想要畫的更細致, 可以使用matplotplib，不過簡單的畫一些圖還是不錯的。

因為上圖太麻煩，這里就不配圖了，可以在資源文件里面查看pandas-blog.ipynb文件或者自己敲一遍代碼。

#?這里使用notbook，為了直接在輸出中顯示，需要以下配置 %matplotlib?inline #?繪制Open,Low,Close.High的線性圖 df[["Open",?"Low",?"High",?"Close"]].plot()#?繪制面積圖 df[["Open",?"Low",?"High",?"Close"]].plot(kind="area")

五、數據讀寫

讀寫常見文件格式，如csv，excel，json等，甚至是讀取“系統的剪切板”這個功能有時候很有用。直接將鼠標選中復制的內容讀取創建dataframe對象。

#?將df數據保存到當前工作目錄的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']#?讀取stock.csv文件并將第一行作為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#?讀取stock.csv文件并將第一行作為index，并且將000001作為str類型讀取,?不然會被解析成整數 df2?=?pd.read_csv("stock.csv",?index_col=0,?dtype={"Code":?str}) df2.head()

六、簡單實例

這里以處理web日志為例，也許不太實用，因為ELK處理這些綽綽有余，不過喜歡什么自己來也未嘗不可。

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 #?字段說明,?參考:https://ru.wikipedia.org/wiki/Access.log％h％l％u％t?\“％r?\”％>?s％b?\“％{Referer}?i?\”\“％{User-Agent}?i?\” #?具體示例 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. 讀取并解析日志文件

解析日志文件

HOST?=?r'^(?P<host>.*?)' SPACE?=?r'\s' IDENTITY?=?r'\S+' USER?=?r"\S+" TIME?=?r'\[(?P<time>.*?)\]' #?REQUEST?=?r'\"(?P<request>.*?)\"' REQUEST?=?r'\"(?P<method>.+?)\s(?P<path>.+?)\s(?P<http_protocol>.*?)\"' STATUS?=?r'(?P<status>\d{3})' SIZE?=?r'(?P<size>\S+)' REFER?=?r"\S+" USER_AGENT?=?r'\"(?P<user_agent>.*?)\"'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()

將數據注入DataFrame對象

COLUMNS?=?["Host",?"Time",?"Method",?"Path",?"Protocol",?"status",?"size",?"User_Agent"]field_lis?=?[] with?open("access.log")?as?rf: for?line?in?rf: #?由于一些記錄不能匹配,所以需要捕獲異常,?不能捕獲的數據格式如下 #?80.32.156.105?-?-?[27/Mar/2009:13:39:51?+0100]?"GET??HTTP/1.1"?400?-?"-"?"-"?"-" #?由于重點不在寫正則表達式這里就略過了try: fields?=?reg.match(line).groups() except?Exception?as?e: #print(e) #print(line)passfield_lis.append(fields)log_df??=?pd.DataFrame(field_lis) #?修改列名 log_df.columns?=?COLUMNSdef?parse_time(value):try: return?pd.to_datetime(value) except?Exception?as?e:print(e)print(value)#?將Time列的值修改成pandas可解析的時間格式 log_df.Time?=?log_df.Time.apply(lambda?x:?x.replace(":",?"?",?1)) log_df.Time?=?log_df.Time.apply(parse_time)#?修改index,?將Time列作為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...

查看數據類型

#?查看數據類型 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字段是時間類型，其他都是object，但是Size, Status應該為數字

def?parse_number(value): try: return?pd.to_numeric(value)except?Exception?as?e:pass return?0#?將Size，Status字段值改為數值類型 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

統計status數據

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

繪制pie圖

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

查看日志文件時間跨度

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

分別查看起始，終止時間

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

按照此方法還可以統計Method, User_Agent字段，不過User_Agent還需要額外清洗以下數據。

統計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

繪制請求走勢圖

log_df2?=?log_df.copy() #?為每行加一個request字段，值為1 log_df2["Request"]?=?1 #?每一小時統計一次request數量，并將NaN值替代為0，最后繪制線性圖，尺寸為16x9 log_df2.Request.resample("H").sum().fillna(0).plot(kind="line",figsize=(16,10))

分別繪圖

分別對202，304，404狀態重新取樣，并放在一個列表里面 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)? ]#?將三個dataframe組合起來 req_df?=?pd.concat(req_df_lis,axis=1) req_df.columns?=?["200",?"304",?"404"] #?繪圖 req_df.plot(figsize=(16,10))

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