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"# Vector Recap\n",
"\n",
"- Numpy is the workhorse of data science in Python. Numpy is not only orders of magnitude faster than vanilla Python, but it uses memory much more efficiently, \n",
"- Vectors are collections of data *of the same type*. \n",
"- Simple vectors can be easily created by passing a list to the `np.array()` function, or by using the `np.arange()` function the same way you would use `range()`.\n",
"- You can easily do math between any vector and a scalar/vector of length 1. The operation will just be repeated for each entry in the longer vector.\n",
"- You can also easily do math between a vector and another vector of the same length. Entries in the two vectors will just be matched up pair-wise.\n",
"- If data of different types are passed to the `np.array()` function, `numpy` will type promote them to the lowest type that can store all the input types."
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"## Next Steps\n",
"\n",
"Now that we're familiar with vectors, [let's do some exercises!](25_EX_vectors.ipynb)"
]
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