diff --git a/01_materials/slides/10_numpy.ipynb b/01_materials/slides/10_numpy.ipynb index 924e4458f..ebd0e056a 100644 --- a/01_materials/slides/10_numpy.ipynb +++ b/01_materials/slides/10_numpy.ipynb @@ -239,7 +239,7 @@ }, "outputs": [], "source": [ - "# create a 1D array from 1 til 10 in steps of 2\n", + "# create a 1D array from 1 until 10 in steps of 2\n", "np.arange(1, 10, 2)" ] }, @@ -1163,7 +1163,7 @@ "source": [ "## Logic and filtering\n", "\n", - "`numpy` arrays work with boolean expressions. Each element is checked, and the result is an array of `True`/`False` values. They resulting arrays sometimes called _masks_ because they are used to mask, or filter, data." + "`numpy` arrays work with Boolean expressions. Each element is checked, and the result is an array of `True`/`False` values. They resulting arrays sometimes called _masks_ because they are used to mask, or filter, data." ] }, { @@ -1231,7 +1231,7 @@ "id": "bae12ead" }, "source": [ - "To filter use a boolean expression as a mask, pass it into square brackets after the array to mask." + "To filter use a Boolean expression as a mask, pass it into square brackets after the array to mask." ] }, { @@ -1299,7 +1299,7 @@ "id": "cb4a4676" }, "source": [ - "We can even use masks to generate new arrays with conditionals. `np.where()` takes as its arguments a boolean expression, an expression to evaluate if `True`, and an expression to evaluate elsewise. This is analagous to creating a new array based on an old one with a `for` loop and `if`/`else` statements, but much faster." + "We can even use masks to generate new arrays with conditionals. `np.where()` takes as its arguments a Boolean expression, an expression to evaluate if `True`, and an expression to evaluate elsewise. This is analogous to creating a new array based on an old one with a `for` loop and `if`/`else` statements, but much faster." ] }, {