prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Lesson 10 - numpy.mean(data) and data.mean
  In lesson 10, when the lesson refers to readings_03.py, the code shows that to calculate the mean over 'data' across all days, numpy.mean is used: numpy.mean(data, axis=1). However when looking at the file readings_03.py (at least the version I downloaded recently) uses the instruction  data.mean(axis=1). Both lead to the same result, but for consistency I would suggest to either  modify the readings_*.py to use numpy.mean (as this is what it has been used throughout the entire lesson), or explain explicitly that both expressions lead to the same result (it would be a good time to remind students about object attributes). 
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `code/readings_03.py`
  Content:
  ```
  1 import sys
  2 import numpy
  3 
  4 
  5 def main():
  6     script = sys.argv[0]
  7     for filename in sys.argv[1:]:
  8         data = numpy.loadtxt(filename, delimiter=',')
  9         for m in data.mean(axis=1):
  10             print(m)
  11 
  12 
  13 if __name__ == '__main__':
  14     main()
  15
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/code/readings_03.py b/code/readings_03.py
  --- a/code/readings_03.py
  +++ b/code/readings_03.py
  @@ -6,7 +6,7 @@
       script = sys.argv[0]
       for filename in sys.argv[1:]:
           data = numpy.loadtxt(filename, delimiter=',')
  -        for m in data.mean(axis=1):
  +        for m in numpy.mean(data, axis=1):
               print(m)
  
verification_info: {"golden_diff": "diff --git a/code/readings_03.py b/code/readings_03.py\n--- a/code/readings_03.py\n+++ b/code/readings_03.py\n@@ -6,7 +6,7 @@\n     script = sys.argv[0]\n     for filename in sys.argv[1:]:\n         data = numpy.loadtxt(filename, delimiter=',')\n-        for m in data.mean(axis=1):\n+        for m in numpy.mean(data, axis=1):\n             print(m)\n", "issue": "Lesson 10 - numpy.mean(data) and data.mean\nIn lesson 10, when the lesson refers to readings_03.py, the code shows that to calculate the mean over 'data' across all days, numpy.mean is used: numpy.mean(data, axis=1). However when looking at the file readings_03.py (at least the version I downloaded recently) uses the instruction  data.mean(axis=1). Both lead to the same result, but for consistency I would suggest to either  modify the readings_*.py to use numpy.mean (as this is what it has been used throughout the entire lesson), or explain explicitly that both expressions lead to the same result (it would be a good time to remind students about object attributes). \n", "before_files": [{"content": "import sys\nimport numpy\n\n\ndef main():\n    script = sys.argv[0]\n    for filename in sys.argv[1:]:\n        data = numpy.loadtxt(filename, delimiter=',')\n        for m in data.mean(axis=1):\n            print(m)\n\n\nif __name__ == '__main__':\n    main()\n", "path": "code/readings_03.py"}], "after_files": [{"content": "import sys\nimport numpy\n\n\ndef main():\n    script = sys.argv[0]\n    for filename in sys.argv[1:]:\n        data = numpy.loadtxt(filename, delimiter=',')\n        for m in numpy.mean(data, axis=1):\n            print(m)\n\n\nif __name__ == '__main__':\n    main()\n", "path": "code/readings_03.py"}]}