#!/usr/bin/env python
# coding: utf-8

# # Loops
# 
# ## The `for` statement
# 
# Consider the scenario where we are examining wind observations related to a site being considered for wind energy production. The wind data consist of a time series of [tuples](http://nbviewer.jupyter.org/github/Unidata/online-python-training/blob/master/notebooks/Basic%2520Data%2520Structures.ipynb#Tuples) containing the time of the observation and the wind gusts in meters per second. [Wind turbines can handle winds up to 40-72 meters per second](https://en.wikipedia.org/wiki/Wind_turbine_design) before measures have to be taken to limit their rotation. The goal here is to find the times at which the observations exceeded 35 meters per second.
# 
# Consider the following wind gust data. Values of -1 are missing observations. UTC times are represented as strings.

# In[1]:


data = [('2016-12-27T19:02:00Z', 19.03), ('2016-12-27T19:22:00Z', -1),
        ('2016-12-27T19:42:00Z', -1), ('2016-12-27T20:02:00Z', -1),
        ('2016-12-27T20:22:00Z', -1), ('2016-12-27T21:02:00Z', 19.03),
        ('2016-12-27T21:22:00Z', -1), ('2016-12-27T22:02:00Z', 28.29),
        ('2016-12-27T22:22:00Z', -1), ('2016-12-27T23:02:00Z', 34.98),
        ('2016-12-27T23:22:00Z', 35.5), ('2016-12-28T00:01:00Z', -1),
        ('2016-12-28T00:21:00Z', 33.44), ('2016-12-28T01:02:00Z', -1),
        ('2016-12-28T01:22:00Z', 36.01), ('2016-12-28T02:01:00Z', 37.55),
        ('2016-12-28T02:22:00Z', 44.76), ('2016-12-28T03:02:00Z', 38.58),
        ('2016-12-28T03:22:00Z', 36.53), ('2016-12-28T04:02:00Z', 26.75),
        ('2016-12-28T04:22:00Z', 23.15), ('2016-12-28T05:02:00Z', 24.18),
        ('2016-12-28T05:22:00Z', 22.12), ('2016-12-28T06:02:00Z', 27.78),
        ('2016-12-28T06:22:00Z', 27.27), ('2016-12-28T07:02:00Z', 28.29)]


# We will find observations greater than 35 meters per second and append them to a `gusts` list. There are a few ways we could achieve this objective with Python. We could employ a `while` statement or the `range` function to enumerate incrementally through the list index to retrieve the value at a particular list index. This style of programming is familiar to Fortran or C family languages with the inevitable `for` loops (e.g., `for(int i = 0; i < n; i++)`). But often, keeping track of the list index is completely unnecessary. We are simply proceeding through a list, grabbing values of interest. In Python, when trying to process a list, instinctively gravitate towards using a `for` loop statement:
# 
# ```ipython
# for iterating_var in sequence:
#    statements(s) # usually involving the iterating_var variable
# ```
# Let's make use of the Python `for` statement for our wind gusts:
# 

# In[2]:


gusts = []
for time, obs in data:
    if obs > 35:
        gusts.append((time, obs))
gusts


# 
# Take a closer look at the `for time, obs in data` statement. We could have written a statement such as `for observation in data` to retrieve each `observation` tuple in the series and subsequently pull apart the `time` and `obs` using tuple operations. Python, however, allows us to pull the tuple apart right in the `for` statement, yielding a concise way to obtain the observation. After that, we test the wind gust speed and append it to the `gusts` list if the wind speed exceeds the threshold.
# 
# Another even more concise approach we could take is **list comprehension** which we cover in the [Functions notebook](http://nbviewer.jupyter.org/github/Unidata/online-python-training/blob/master/notebooks/Functions.ipynb) and in an example in the [Basic Input and Output notebook](http://nbviewer.jupyter.org/github/Unidata/online-python-training/blob/master/notebooks/Basic%2520Input%2520and%2520Output.ipynb#List-Comprehension-Explanation). Indeed [Guido](https://en.wikipedia.org/wiki/Guido_van_Rossum) would have preferred:

# In[3]:


[(time, obs) for time, obs in data if obs > 35]


# In sum, you will be employing loops extensively and you will benefit by making use of Python `for` loops to process sequences.