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Merge pull request #1 from janekpaw321/janekpaw321-patch-1

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Fixed typos + made numpy.shape example more clear
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janekpaw321 2022-10-01 19:18:03 +02:00 committed by GitHub
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24_Day_Statistics/24_statistics.md
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@ -172,12 +172,15 @@ The shape method provide the shape of the array as a tuple. The first is the row
nums = np.array([1, 2, 3, 4, 5])
print(nums)
print('shape of nums: ', nums.shape)
numpy_two_dimensional_list = np.array([[0,1,2],[3,4,5],[6,7,8]])
print(numpy_two_dimensional_list)
print('shape of numpy_two_dimensional_list: ', numpy_two_dimensional_list.shape)
three_by_four_array = np.array([[0, 1, 2, 3],
[4,5,6,7],
[8,9,10, 11]])
print(three_by_four_array.shape)
[8,9,10,11]]
print(three_by_four_array)
print('shape of three_by_four_array: ', three_by_four_array.shape)
```
```sh
@ -930,7 +933,7 @@ Numpy is equipped with the robust statistical function as listed below
- Max np.max()
- Mean np.mean()
- Median np.median()
- Varience
- Variance
- Percentile
- Standard deviation np.std()
@ -1218,7 +1221,7 @@ plt.show()
![png](../test_files/test_143_0.png)
# Summery
# Summary
To summarize, the main differences with python lists are: