# Intermediate Python Lesson 1: Analyzing Mosquito Data¶

## Introduction¶

This material assumes that you have programmed before. This first lecture provides a quick introduction to programming in Python for those who either haven't used Python before or need a quick refresher.

Let's start with a hypothetical problem we want to solve. We are interested in understanding the relationship between the weather and the number of mosquitos occuring in a particular year so that we can plan mosquito control measures accordingly. Since we want to apply these mosquito control measures at a number of different sites we need to understand both the relationship at a particular site and whether or not it is consistent across sites. The data we have to address this problem comes from the local government and are stored in tables in comma-separated values (CSV) files. Each file holds the data for a single location, each row holds the information for a single year at that location, and the columns hold the data on both mosquito numbers and the average temperature and rainfall from the beginning of mosquito breeding season. The first few rows of our first file look like:

year,temperature,rainfall,mosquitos
2001,87,222,198
2002,72,103,105
2003,77,176,166



## Learning Objectives¶

• Conduct variable assignment, looping, and conditionals in Python
• Use an external Python library
• Read tabular data from a file
• Subset and perform analysis on data
• Display simple graphs

In order to load the data, we need to import a library called Pandas that knows how to operate on tables of data.

In [1]:
import pandas


We can now use Pandas to read our data file.

In [4]:
pandas.read_csv('data/A1_mosquito_data.csv')

Out[4]:
year temperature rainfall mosquitos
0 2001 80 157 150
1 2002 85 252 217
2 2003 86 154 153
3 2004 87 159 158
4 2005 74 292 243
5 2006 75 283 237
6 2007 80 214 190
7 2008 85 197 181
8 2009 74 231 200
9 2010 74 207 184

The read_csv() function belongs to the pandas library. In order to run it we need to tell Python that it is part of pandas and we do this using the dot notation, which is used everywhere in Python to refer to parts of larger things.

When we are finished typing and press Shift+Enter, the notebook runs our command and shows us its output. In this case, the output is the data we just loaded.

Our call to pandas.read_csv() read data into memory, but didn't save it anywhere. To do that, we need to assign the array to a variable. In Python we use = to assign a new value to a variable like this:

In [6]:
data = pandas.read_csv('data/A1_mosquito_data.csv')


This statement doesn't produce any output because assignment doesn't display anything. If we want to check that our data has been loaded, we can print the variable's value:

In [9]:
print(data)

   year  temperature  rainfall  mosquitos
0  2001           80       157        150
1  2002           85       252        217
2  2003           86       154        153
3  2004           87       159        158
4  2005           74       292        243
5  2006           75       283        237
6  2007           80       214        190
7  2008           85       197        181
8  2009           74       231        200
9  2010           74       207        184


print(data) tells Python to display the text. Alternatively we could just include data as the last value in a code cell:

In [10]:
data

Out[10]:
year temperature rainfall mosquitos
0 2001 80 157 150
1 2002 85 252 217
2 2003 86 154 153
3 2004 87 159 158
4 2005 74 292 243
5 2006 75 283 237
6 2007 80 214 190
7 2008 85 197 181
8 2009 74 231 200
9 2010 74 207 184

This tells the IPython Notebook to display the data object, which is why we see a pretty formatted table.

## Manipulating data¶

Once we have imported the data we can start doing things with it. First, let's ask what type of thing data refers to:

In [11]:
type(data)

Out[11]:
pandas.core.frame.DataFrame

The data is stored in a data structure called a DataFrame. There are other kinds of data structures that are also commonly used in scientific computing including Numpy arrays, and Numpy matrices, which can be used for doing linear algebra.

We can select an individual column of data using its name:

In [12]:
data['year']

Out[12]:
0    2001
1    2002
2    2003
3    2004
4    2005
5    2006
6    2007
7    2008
8    2009
9    2010
Name: year, dtype: int64

Or we can select several columns of data at once:

In [13]:
data[['rainfall', 'temperature']]

Out[13]:
rainfall temperature
0 157 80
1 252 85
2 154 86
3 159 87
4 292 74
5 283 75
6 214 80
7 197 85
8 231 74
9 207 74

We can also select subsets of rows using slicing. Say we just want the first two rows of data:

In [14]:
data[0:2]

Out[14]:
year temperature rainfall mosquitos
0 2001 80 157 150
1 2002 85 252 217

There are a couple of important things to note here. First, Python indexing starts at zero. In contrast, programming languages like R and MATLAB start counting at 1, because that's what human beings have done for thousands of years. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that's simpler for computers to do. This means that if we have 5 things in Python they are numbered 0, 1, 2, 3, 4, and the first row in a data frame is always row 0.

The other thing to note is that the subset of rows starts at the first value and goes up to, but does not include, the second value. Again, the up-to-but-not-including takes a bit of getting used to, but the rule is that the difference between the upper and lower bounds is the number of values in the slice.

One thing that we can't do with this syntax is directly ask for the data from a single row:

In [15]:
data[1]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-15-c805864c0d75> in <module>()
----> 1 data[1]

/usr/lib/python3/dist-packages/pandas/core/frame.py in __getitem__(self, key)
1741             return self._getitem_multilevel(key)
1742         else:
-> 1743             return self._getitem_column(key)
1744
1745     def _getitem_column(self, key):

/usr/lib/python3/dist-packages/pandas/core/frame.py in _getitem_column(self, key)
1748         # get column
1749         if self.columns.is_unique:
-> 1750             return self._get_item_cache(key)
1751
1752         # duplicate columns & possible reduce dimensionaility

/usr/lib/python3/dist-packages/pandas/core/generic.py in _get_item_cache(self, item)
1056         res = cache.get(item)
1057         if res is None:
-> 1058             values = self._data.get(item)
1059             res = self._box_item_values(item, values)
1060             cache[item] = res

/usr/lib/python3/dist-packages/pandas/core/internals.py in get(self, item, fastpath)
2804
2805             if not isnull(item):
-> 2806                 loc = self.items.get_loc(item)
2807             else:
2808                 indexer = np.arange(len(self.items))[isnull(self.items)]

/usr/lib/python3/dist-packages/pandas/core/index.py in get_loc(self, key)
1383         loc : int if unique index, possibly slice or mask if not
1384         """
-> 1385         return self._engine.get_loc(_values_from_object(key))
1386
1387     def get_value(self, series, key):

index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3767)()

index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3645)()

hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11911)()

hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11864)()

KeyError: 1

This is because there are several things that we could mean by data[1] so if we want a single row we can either take a slice that returns a single row:

In [16]:
data[1:2]

Out[16]:
year temperature rainfall mosquitos
1 2002 85 252 217

or use the .iloc method, which stands for "integer location" since we are looking up the row based on its integer index.

In [17]:
data.iloc[1]

Out[17]:
year           2002
temperature      85
rainfall        252
mosquitos       217
Name: 1, dtype: int64

We can also use this same syntax for getting larger subsets of rows:

In [18]:
data.iloc[1:3]

Out[18]:
year temperature rainfall mosquitos
1 2002 85 252 217
2 2003 86 154 153

We can also subset the data based on the value of other rows:

In [19]:
data['temperature'][data['year'] > 2005]

Out[19]:
5    75
6    80
7    85
8    74
9    74
Name: temperature, dtype: int64

Data frames also know how to perform common mathematical operations on their values. If we want to find the average value for each variable, we can just ask the data frame for its mean values

In [20]:
data.mean()

Out[20]:
year           2005.5
temperature      80.0
rainfall        214.6
mosquitos       191.3
dtype: float64

Data frames have lots of useful methods:

In [21]:
data.max()

Out[21]:
year           2010
temperature      87
rainfall        292
mosquitos       243
dtype: int64
In [22]:
data['temperature'].min()

Out[22]:
74
In [23]:
data['mosquitos'][1:3].std()

Out[23]:
45.254833995939045

## Challenge¶

Import the data from A2_mosquito_data.csv, create a new variable that holds a data frame with only the weather data, and print the means and standard deviations for the weather variables.

In [ ]:



## Loops¶

Once we have some data we often want to be able to loop over it to perform the same operation repeatedly. A for loop in Python takes the general form:

for item in list:
do_something


So if we want to loop over the temperatures and print out their values in degrees Celsius (instead of Farenheit) we can use:

In [25]:
temps = data['temperature']
for temp_in_f in temps:
temp_in_c = (temp_in_f - 32) * 5 / 9.0
print(temp_in_c)

26.6666666667
29.4444444444
30.0
30.5555555556
23.3333333333
23.8888888889
26.6666666667
29.4444444444
23.3333333333
23.3333333333


That looks good, but why did we use 9.0 instead of 9? If you try changing it, you'll still get the same results.

Computers store two different kinds of numbers: integers and floating point numbers (or floats). 9 creates an integer, 9.0 creates a float. In Python 2, dividing one integer by another would throw away the remainder, so 5/9 would give 0. In Python 3, division does what you'd expect - the result is a floating point number. But it's a good idea to be careful, so we made sure that at least one of the numbers for division is a float.

## Conditionals¶

The other standard thing we need to know how to do in Python is conditionals, or if/then/else statements. In Python the basic syntax is:

if condition:
do_something


So if we want to loop over the temperatures and print out only those temperatures that are greater than 80 degrees we would use:

In [31]:
temp = data['temperature'][0]
if temp > 75:
print("The temperature is greater than 75")

The temperature is greater than 75


We can also use == for equality, <= for less than or equal to, >= for greater than or equal to, and != for not equal to.

Additional conditions can be handled using elif and else:

In [1]:
temp = data['temperature'][0]
if temp < 80:
print("The temperature is < 80")
elif temp > 80:
print("The temperature is > 80")
else:
print("The temperature is equal to 80")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-d4f03b5de90c> in <module>()
----> 1 temp = data['temperature'][0]
2 if temp < 80:
3     print("The temperature is < 80")
4 elif temp > 80:
5     print("The temperature is > 80")

NameError: name 'data' is not defined

## Challenge¶

Import the data from A2_mosquito_data.csv, determine the mean temperate, and loop over the temperature values. For each value print out whether it is greater than the mean, less than the mean, or equal to the mean.

In [ ]:



## Plotting¶

The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers," and the best way to develop insight is often to visualize data. The main plotting library in Python is matplotlib. To get started, let's tell the IPython Notebook that we want our plots displayed inline, rather than in a separate viewing window:

In [34]:
%matplotlib inline


The % at the start of the line signals that this is a command for the notebook, rather than a statement in Python. Next, we will import the pyplot module from matplotlib, but since pyplot is a fairly long name to type repeatedly let's give it an alias.

In [35]:
from matplotlib import pyplot as plt


This import statement shows two new things. First, we can import part of a library by using the from library import submodule syntax. Second, we can use a different name to refer to the imported library by using as newname.

Now, let's make a simple plot showing how the number of mosquitos varies over time. We'll use the site you've been doing exercises with since it has a longer time-series.

In [37]:
data = pandas.read_csv('data/A2_mosquito_data.csv')
plt.plot(data['year'], data['mosquitos'])

Out[37]:
[<matplotlib.lines.Line2D at 0x7fa8ec4e4a20>]

More complicated plots can be created by adding a little additional information. Let's say we want to look at how the different weather variables vary over time.

In [38]:
plt.figure(figsize=(10.0, 3.0))

plt.subplot(1, 2, 1)
plt.plot(data['year'], data['temperature'], 'ro-')
plt.xlabel('Year')
plt.ylabel('Temperature')

plt.subplot(1, 2, 2)
plt.plot(data['year'], data['rainfall'], 'bs-')
plt.xlabel('Year')
plt.ylabel('Rain Fall')
plt.show()


## Challenge¶

Using the data in A2_mosquito_data.csv, plot the relationship between the number of mosquitos and temperature and the number of mosquitos and rainfall.

In [ ]:



### Key Points¶

• Import a library into a program using import libraryname.
• Use the pandas library to work with data tables in Python.
• Use variable = value to assign a value to a variable.
• Use print something to display the value of something.
• Use dataframe['columnname'] to select a column of data.
• Use dataframe[start_row:stop_row] to select rows from a data frame.
• Indices start at 0, not 1.
• Use dataframe.mean(), dataframe.max(), and dataframe.min() to calculate simple statistics.
• Use for x in list: to loop over values
• Use if condition: to make conditional decisions
• Use the pyplot library from matplotlib for creating simple visualizations.

## Next steps¶

With the requisite Python background out of the way, now we're ready to dig in to analyzing our data, and along the way learn how to write better code, more efficiently, that is more likely to be correct.