Code Challenge: Log Analysis

Given a JSON or CSV log file, analyze the data provided and solve each of these scenarios:

1. Generate a table of response codes returned for each request.
2. Generate a table showing the sum of each response code returned.
3. Plot a graph showing the sum of each response code returned.

Analyzing Log Files with Python

To analyze a JSON or CSV log file with Python, we can use Pandas. Pandas DataFrames are two-dimensional tables with labeled axes that are good for analyzing datasets:

1. Import the necessary libraries (pandas):

import pandas as pd

2. Load the log file as a DataFrame. The pandas library has the methods read_json() and read_csv(), which convert the respective formats into a pandas DataFrame. The lines bool reads a JSON log file as one JSON object per line.

data = pd.read_json('nginx.json', lines=True) # read_json() example
data = pd.read_csv('nginx.csv') # read_csv() example

3. View and analyze the data.

The info method prints information about a DataFrame including the index dtype and columns, non-null values, and memory usage.

print(data.info())

describe will generate descriptive statistics which include those that summarize the central tendency, dispersion, and shape of a dataset’s distribution, excluding NaN values.

print(data.describe())

To view the first five entries, we can use the head method.

print(data.head())

Let's put it all together and see what we get.

import pandas as pd

data = pd.read_csv('nginx.csv') # load csv file as a pandas dataframe
data = pd.read_json('nginx.json', lines=True) # load json file as a pandas dataframe

print(data.info()) # dataframe info
print(data.describe()) # describe dataframe
print(data.head()) # show first 5 entries

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 51462 entries, 0 to 51461
    Data columns (total 8 columns):
     #   Column       Non-Null Count  Dtype 
    ---  ------       --------------  ----- 
     0   time         51462 non-null  object
     1   remote_ip    51462 non-null  object
     2   remote_user  51462 non-null  object
     3   request      51462 non-null  object
     4   response     51462 non-null  int64 
     5   bytes        51462 non-null  int64 
     6   referrer     51462 non-null  object
     7   agent        51462 non-null  object
    dtypes: int64(2), object(6)
    memory usage: 3.1+ MB
    None
               response         bytes
    count  51462.000000  5.146200e+04
    mean     361.414597  6.595095e+05
    std       64.620998  6.518840e+06
    min      200.000000  0.000000e+00
    25%      304.000000  0.000000e+00
    50%      404.000000  3.340000e+02
    75%      404.000000  3.380000e+02
    max      416.000000  8.637717e+07
                             time     remote_ip remote_user  \
    0  17/May/2015:08:05:32 +0000   93.180.71.3           -   
    1  17/May/2015:08:05:23 +0000   93.180.71.3           -   
    2  17/May/2015:08:05:24 +0000  80.91.33.133           -   
    3  17/May/2015:08:05:34 +0000  217.168.17.5           -   
    4  17/May/2015:08:05:09 +0000  217.168.17.5           -   

                                 request  response  bytes referrer  \
    0  GET /downloads/product_1 HTTP/1.1       304      0        -   
    1  GET /downloads/product_1 HTTP/1.1       304      0        -   
    2  GET /downloads/product_1 HTTP/1.1       304      0        -   
    3  GET /downloads/product_1 HTTP/1.1       200    490        -   
    4  GET /downloads/product_2 HTTP/1.1       200    490        -   

                                               agent  
    0  Debian APT-HTTP/1.3 (0.8.16~exp12ubuntu10.21)  
    1  Debian APT-HTTP/1.3 (0.8.16~exp12ubuntu10.21)  
    2  Debian APT-HTTP/1.3 (0.8.16~exp12ubuntu10.17)  
    3                 Debian APT-HTTP/1.3 (0.8.10.3)  
    4                 Debian APT-HTTP/1.3 (0.8.10.3)

Prompt 1

Generate a table of response codes returned for each request.

responses = data[['time','request','response']] # extract requests and responses

print(responses.head()) # output responses

time                          request        response
    0   17/May/2015:08:05:32 +0000  GET /downloads/product_1 HTTP/1.1       304
    1   17/May/2015:08:05:23 +0000  GET /downloads/product_1 HTTP/1.1       304
    2   17/May/2015:08:05:24 +0000  GET /downloads/product_1 HTTP/1.1       304
    3   17/May/2015:08:05:34 +0000  GET /downloads/product_1 HTTP/1.1       200
    4   17/May/2015:08:05:09 +0000  GET /downloads/product_2 HTTP/1.1       200

Prompt 2

Generate a table showing the sum of each response code returned.

count_responses = responses.value_counts(responses['response']) # count response codes

print(count_responses) # ouput counts

response
    404    33876
    304    13330
    200     4028
    206      186
    403       38
    416        4
    Name: count, dtype: int64

Prompt 3

Plot a graph showing the sum of each response code returned.

import matplotlib.pyplot as plt

# plot graph of count by response code
count_responses.plot.bar(
    title='Count of Response Codes',
    xlabel='Response Codes',
    ylabel='Count'
)

plt.show() # output plot

Resources

• https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html
• https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html
• https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html
• https://pandas.pydata.org/docs/getting_started/intro_tutorials/04_plotting.html