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    Part 2: Plotting Baby Name data
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#  Part 2: Plotting Baby Name data

In this part of lab, we are working with a subset of data from the [US SSA (Social Security Administration](https://www.ssa.gov/oact/babynames/), which tracks names of babies born in the US.

## Set up for plotting
Running your plotting code in Canopy places your plots in the inline Python window.

To have your plots pop up in a separate window, do this:
1. _Canopy_ > _Preferences..._
2. Click on the **Python** tab
3. In the _PyLab backend_ drop-down menu, select **Interactive (wx)**
<img src="wx.png" style="width:500px">



## Task 0. Familiarize yourself with the data
In your `plotting` folder there is a provided file called `topNamesData.py` that contains 6 variables. Each variable contains a list of tuples with the number of babies born that year and the name. The variables are named `year<XXXX><S>` where `<XXXX>` is one of the following years: 1950, 1980 or 2000 and `<S>` is either `F` for female or `M` for male. (To keep things simple, we are only working with these 6 variables in this part of lab).

The variable `year1950F` contains the number of female babies born in 1950 with the top 10 most popular names.
```py
year1950F = [(80412, 'Linda'),
 (65443, 'Mary'),
 (47920, 'Patricia'),
 (41560, 'Barbara'),
 (38019, 'Susan'),
 (29618, 'Nancy'),
 (29074, 'Deborah'),
 (28885, 'Sandra'),
 (26159, 'Carol'),
 (25693, 'Kathleen')]
 ```

Simiarly, the variable `year1950M` contains the number of male babies born in 1950 with the top 10 most popular names.
```py
year1950M = [(86139, 'James'),
 (83534, 'Robert'),
 (79396, 'John'),
 (65141, 'Michael'),
 (60704, 'David'),
 (60699, 'William'),
 (50969, 'Richard'),
 (45608, 'Thomas'),
 (39107, 'Charles'),
 (33726, 'Gary')]
```
**Create a new file called `babynames.py`**&mdash; at the top of this file import `topNamesData` so you can use the pre-defined lists of tuples.:

```py
from topNamesData import *
```

## Task 1. Plot bar charts of counts of the names
Write a function called `makePlot1` that will take a list of tuples and produce a plot as shown.
Consider the variable `year1950F`: to make a bar chart, the names (e.g. Linda, Mary, Patricia, etc) must be separated from their respective counts (e.g. 80412, 65443, 47920, etc).
### Extract what you need to plot
Given the list of tuples in `year1950F`, figure out how to extract the counts into a list of counts. Hint: a list comprehension would work. 
To make a horizontal bar chart, you will need to plot numbers against numbers.  
Here is a simple plot of the female baby names in 1950.
## Goal: Get a plot to show up
```py
 makePlot1(year1950F)
```
<img src="1950female.png" style="max-width:500px">

## Task 2. Better bar charts 
There are several ways in which we can improve the bar graph shown in the task above.

Write a new version of your function (call it `makePlot2`) by adding these improvements (add them in one at a time):

+ Add a title to your plot 
+ Label the x and y axis
+ Change the thickness of the bars
+ Change bar color [click here for predefined colors in matplotlib](https://matplotlib.org/examples/color/named_colors.html)
+ Add labels to your axis (here is an <a href="http://matplotlib.org/examples/ticks_and_spines/ticklabels_demo_rotation.html">xtick label example</a>)

Hint: Use list comprehensions to get ytick markers at half increments.


```py
 makePlot2(year1950F)
```
<img src="names1.png" style="max-width:500px">

<img src="names2.png" style="max-width:500px">

## Task 3: Most popular name on top 
Write another function called `makePlot3` that will plot the names with the most frequently occurring name on top (as shown below).
```py
makePlot3(year1950F)
```
<img src="names3.png" style="max-width:500px">

## Task 4: Names sorted alphabetically 
Plot the names in alphabetical order from top to bottom (as shown below)
```py
makePlot4(year1950F)
```
<img src="names4.png" style="max-width:500px">

```py
makePlot4(year2000F)
```
<img src="names5.png" style="max-width:500px">

```py
makePlot4(year2000M)
```
<img src="names6.png" style="max-width:500px">

## Task 5: Plot last letter percentages of top 10 names
### Task 5A: `lastLetterNames()`
For this task, you'll need to create a new dictionary. Write a new function called `lastLetterNames` that takes a data variable and **returns** a dictionary. The keys in the dictionary are the last letters of the names and the values are the names that end with that letter. Here are some examples:
```py
In []: lastLetterNames(year2000F)
Out[]: 
{'a': ['Samantha', 'Jessica'],
 'h': ['Hannah', 'Sarah', 'Elizabeth'],
 'n': ['Madison'],
 'r': ['Taylor'],
 's': ['Alexis'],
 'y': ['Emily', 'Ashley']}

In []: lastLetterNames(year1950M)
Out[]: 
{'d': ['David', 'Richard'],
 'l': ['Michael'],
 'm': ['William'],
 'n': ['John'],
 's': ['James', 'Thomas', 'Charles'],
 't': ['Robert'],
 'y': ['Gary']}

In []: lastLetterNames(year1980F)
Out[]: 
{'a': ['Amanda', 'Jessica', 'Melissa'],
 'e': ['Nicole', 'Michelle'],
 'h': ['Sarah', 'Elizabeth'],
 'r': ['Jennifer', 'Heather'],
 'y': ['Amy']}
```
### Task 5B: `plotLastLetterPercents(data)`
Now that you have the last letter dictionaries, you can plot the last letters on the `Y` axis and the percentages on the `X` axis in a horizontal bar chart.
Write a function called `plotLastLetterPercents()` that takes in a list of tuples and displays a plot showing the last letters and the percentage representation within the top 10 names.

Note:
1. We are plotting percents rather than count
2. If you would like to change the background color of the figure,
you can use the `facecolor` attribute of figure:
```py
plt.figure(facecolor='lightsteelblue')
```

###Example 1:
```py
plotLastLetterPercents(year2000F)
```
<img src="percentA.png" style="max-width:500px">
###Example 2:
```py
plotLastLetterPercents(year1950M)
```
<img src="percentB.png" style="max-width:500px">
###Example 3:
```py
plotLastLetterPercents(year1980F)
```
<img src="percentC.png" style="max-width:500px">


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