Exercise 1 hints
================

Add below some tips for working on Exercise 1 if needed.

Check of what type an object is
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The ``isinstance(actual_object, variable_type)`` function is a Python builtin function that will answer your question,
if the ``actual_object`` is of the variable typpe ``variable_type``. Basic types are for example:

- str: String
- int: Integer number
- float: Floating point numbers
- list: List of thing aka [ ] brackety things
- dict: Dictionaries, Python versatile data structures, based on associative lists and objects, where you address via named fields (see `Python recap lecture <../L0/recap-python.html>`_ )

An example:

.. ipython:: python

    a_string_var = "I am a string"
    an_int_var = 42
    a_float_var = 3.5
    a_boolean_true_false_var = True

    is_string = isinstance(a_string_var, str)
    print(is_string)
    is_int = isinstance(an_int_var, int)
    print(is_int)
    is_float = isinstance(a_float_var, float)
    print(is_float)
    true_or_false = isinstance(a_float_var, str)
    print(true_or_false)


Control flow for checks with ``if`` and ``else``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If you want to make a "left" or "right" decision, you can use Python's if then construct.
For that you need to check a *condition* (or a fact)  if it's *true* or *false*. If it's true, go only through the first block,
if it's false, go only through the else block.

.. code::

    initial_demo_output = 0

    if 3 > 2:
        print("3 is larger than 2")
        initial_demo_output = 3
    else:
        print("3 not larger than 2")
        initial_demo_output = 2

    # guess the final value of ``initial_demo_output`` ?
    print(initial_demo_output)

For more details and practice, see `Python recap lecture <../L0/recap-python.html>`_

Reading a CSV file into Pandas
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

With Python it is basically possible to read data from any kind of input datafile (such as csv-, txt-, etc).
The widely used library Pandas can easily read a file with tabular data and present it to us as a so called dataframe:

.. ipython:: python

    import pandas as pd

    # make sure you have the correct path to your working file, ideally in the same folder
    df = pd.read_csv('source/_static/data/L1/global-city-population-estimates.csv', sep=';', encoding='latin1')

    pd.set_option('max_columns',20)
    print(df.head(5))


Applying a function to every row of a Pandas dataframe
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. ipython:: python

    # we make a function, that takes a row object coming from Pandas. The single fields per row are addressed by their column name.
    def increase_by_factor_2(row):
        field_value = row['Population_2015']
        calc_value = field_value * 2
        return calc_value

    # Go through every row, and calculate the value for  a new column ``Population_doubled``, by **apply**ing the function from above (downwards row by row -> axis=1)
    df['Population_doubled'] = df.apply(increase_by_factor_2, axis=1)

    print(df.head(5))


Tricky directory path names in windows
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In the lesson and exercise 1 hints for reading a CSV file in Pandas, a few students got a very cryptic error message, something about "decoding of sequence \UCXXXXX not possible".
The error occurs in the line with 'pd.read_csv' and you likely have used the complete path:

.. code::

    df = pd.read_csv('c:\users\alex\geopython\L1\global-city-population-estimates.csv', sep=';', encoding='latin1')

Windows uses backslashes '\' as folder separators. However, using backslashes can cause problems in String variables in programming languages.
Therefore in Python we put an 'r' for 'raw' in front of the quotes for the String with the path name to the file, like so:

.. code::

    df = pd.read_csv(r'c:\users\alex\geopython\L1\global-city-population-estimates.csv', sep=';', encoding='latin1')

You could also just omit the long path and use only the filename. For that the file should also be saved where you Jupyter Notebook *.ipynb is located.