Merge pull request #281 from pitmonticone/master

Fix typos

Author: bast

content/binder.rst

@@ -117,7 +117,7 @@ then using `Binder <https://mybinder.org/>`__.
    - Check that your notebook repository now has a "launch binder"
      badge in your `README.md` file on GitHub.
    - Try clicking the button and see how your repository is launched
-     on Binder (can take a minute or two). Your notebooks can now be expored and executed in the cloud.
+     on Binder (can take a minute or two). Your notebooks can now be explored and executed in the cloud.
    - Enjoy being fully reproducible!
 
 

content/data-formats.rst

@@ -50,7 +50,7 @@ Let's consider another example::
     np.info(data_array)
 
 
-Here we have a different data structure: we have a two-dimentional array of numbers.
+Here we have a different data structure: we have a two-dimensional array of numbers.
 This is different to a Pandas DataFrame as data is stored as one contiguous block instead of individual columns.
 This also means that the whole array must have one data type.
 
@@ -90,7 +90,7 @@ Usually, you'll want to consider the following things when choosing a file forma
 
 1. Is the file format good for my data structure (is it fast/space efficient/easy to use)?
 2. Is everybody else / leading authorities in my field recommending a certain format?
-3. Do I need a human-readable format or is it enought to work on it using code?
+3. Do I need a human-readable format or is it enough to work on it using code?
 4. Do I want to archive / share the data or do I just want to store it while I'm working?
 
 Pandas supports `many file formats <https://pandas.pydata.org/docs/user_guide/io.html>`__ for tidy data and Numpy supports `some file formats <https://numpy.org/doc/stable/reference/routines.io.html>`__ for array data.

content/data-visualization.md

@@ -184,7 +184,7 @@ ax.legend()
 ````
 
 ```{discussion} Why these colors?
-This qualitative color palette is opimized for all color-vision
+This qualitative color palette is optimized for all color-vision
 deficiencies, see <https://clauswilke.com/dataviz/color-pitfalls.html> and
 [Okabe, M., and K. Ito. 2008. "Color Universal Design (CUD):
 How to Make Figures and Presentations That Are Friendly to Colorblind People"](http://jfly.iam.u-tokyo.ac.jp/color/).

content/installation.rst

@@ -22,7 +22,7 @@ of the tools.
 * **Python 3** (Anaconda is recommended, it will include everything)
 
   * With some extra packages installed.  They are all included in
-    Aanconda, and are listed in the ``environment.yml`` file you can
+    Anaconda, and are listed in the ``environment.yml`` file you can
     find under miniconda below.
 * Text editor (several lessons, can also be done through Jupyterlab)
 * Command-line shell (several lessons, can also be done through Jupyterlab)

content/numpy-advanced.rst

@@ -70,7 +70,7 @@ Exercise 1
    Can you beat the C version?
 
    If you are having trouble with this, we recommend completing the
-   :ref:`basic NumPy lession <numpy>` before continuing with this
+   :ref:`basic NumPy lesson <numpy>` before continuing with this
    advanced lesson.  If you are taking a live course - don't
    worry, watch and learn and explore some during the exercises!
 

content/pandas.rst

@@ -113,7 +113,7 @@ Unlike a NumPy array, a dataframe can combine multiple data types, such as
 numbers and text, but the data in each column is of the same type. So we say a
 column is of type ``int64`` or of type ``object``.
 
-Let's inspect one column of the Titanic passanger list data (first downloading
+Let's inspect one column of the Titanic passenger list data (first downloading
 and reading the titanic.csv datafile into a dataframe if needed, see above)::
 
     titanic["Age"]
@@ -592,7 +592,7 @@ apply your own functions to the data using :obj:`~pandas.DataFrame.apply`::
     df['Number of Rabbits'] = df['Generation'].apply(fib)
 
 
-Note that the numpy precisision for integers caps at int64 while python ints are unbounded --
+Note that the numpy precision for integers caps at int64 while python ints are unbounded --
 limited by memory size. Thus, the result from fibonacci(99) would be erroneous when
 using numpy ints. The type of df['Number of Rabbits'][99] given by both functions above
 is in fact <class 'int'>.

content/productivity.rst

@@ -95,7 +95,7 @@ get more errors:
 Here we see the following suggestions:
 
 - On line 1 we're missing a module docstring. This is a warning that we're
-  going against a coding convetion and thus we get a ``CXXXX`` message code.
+  going against a coding convention and thus we get a ``CXXXX`` message code.
   This is not critical, so let's not focus on this for now.
 - On lines 4 and 5 we have undefined variable ``np``. This will create
   error if we would execute the code and thus we get a ``EXXXX`` message code.
@@ -110,7 +110,7 @@ correctly.
 From these messages we can deduce that the main problem is that the import
 statement does not use ``import numpy as np`` and thus ``np`` is undefined.
 
-After changing the import stamement, the code works correctly and running
+After changing the import statement, the code works correctly and running
 ``pylint lint_example.py`` will only warn about the missing docstring.
 You can also notice that the changes have increased the rating and
 Pylint will show the improvement since last run.

content/web-apis.ipynb

@@ -570,7 +570,7 @@
    "source": [
     "## After exercises: Saving retrieved data to disk\n",
     "\n",
-    "Usually, we want to save the retrieved data to disk for later use. For example, we might collect data for one year and later analyze it for a longitudal study.\n",
+    "Usually, we want to save the retrieved data to disk for later use. For example, we might collect data for one year and later analyze it for a longitudinal study.\n",
     "\n",
     "To save the retrieved JSON objects to disk, it is practical to use the JSONLINES file format. The JSONLINES format contains a single valid JSON object on each line. This is preferable to saving each object as its own file since we don't, in general, want to end up with excessive amounts of individual files (say, hundreds of thousands or millions).\n",
     "\n",