Commit 94540f77 authored by Jens Ehlers's avatar Jens Ehlers
Browse files

Python intro update

parent 036628b2
......@@ -184,21 +184,22 @@
(1, 2, 3)
%% Cell type:code id: tags:
``` python
{"apple": "a fruit", "banana": "an herb", "monkey": "a mammal"}
a = {"apple": "a fruit", "banana": "an herb", "monkey": "a mammal"}
a
```
%%%% Output: execute_result
{'apple': 'a fruit', 'banana': 'an herb', 'monkey': 'a mammal'}
%% Cell type:code id: tags:
``` python
{"apple": "a fruit", "banana": "an herb", "monkey": "a mammal"}["apple"]
a["apple"]
```
%%%% Output: execute_result
'a fruit'
......@@ -332,14 +333,13 @@
%% Cell type:markdown id: tags:
**How to Read Python Error Messages**
Python error messages
`TypeError: Can't convert 'int' object to str implicitly`
Python error messages `TypeError: can only concatenate str (not "int") to str`
Above the error message is the "traceback" also called the "call stack". This is a representation of the sequence of procedure calls that lead to the error. If the procedure call originated from code from a file, the filename would be listed after the word "File" on each line. If the procedure call originated from a notebook cell, then the word "ipython-input-...".
Above the error message is the "traceback" also called the "call stack". This is a representation of the sequence of procedure calls that lead to the error. If the procedure call originated from code from a file, the filename would be listed after the word "File" on each line. If the procedure call originated from a notebook cell, then we see the "ipython-input-...".
%% Cell type:markdown id: tags:
## Equality
......@@ -393,11 +393,11 @@
False
%% Cell type:code id: tags:
``` python
(1, 2) is (1, 2) # tuples compared by value
(1, 2) is (1, 2) # if tuples are compared by reference or value, depends on your Python version
```
%%%% Output: execute_result
True
......@@ -518,11 +518,11 @@
function()
```
%%%% Output: execute_result
<generator object function at 0x000002122AA82900>
<generator object function at 0x0000017816A305F0>
%% Cell type:code id: tags:
``` python
for y in function():
......@@ -661,11 +661,11 @@
plt.plot([5, 8, 2, 6, 1, 8, 2, 3, 4, 5, 6])
```
%%%% Output: execute_result
[<matplotlib.lines.Line2D at 0x21229948be0>]
[<matplotlib.lines.Line2D at 0x17858c222b0>]
%%%% Output: display_data
![](data:image/png;base64,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)
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