## Friday, June 16, 2017

### A heatmap of male to female ratios with Seaborn

In this post we will see how to create a heatmap with seaborn. We'll use a dataset from the Wittgenstein Centre Data Explorer. The data extracted is also reported here in csv format. It contains the ratio of males to females in the population by age for 1970 to 2015 (data reported after this period is projected). First, we import the data using Pandas:
```import pandas as pd
import numpy as np

age_code = {a: i for i,a in enumerate(sex_ratios.Age.unique())}
age_label = {i: a for i,a in enumerate(sex_ratios.Age.unique())}
sex_ratios['AgeCode'] = sex_ratios.Age.apply(lambda x: age_code[x])

area_idx = sex_ratios.Area == \
'United Kingdom of Great Britain and Northern Ireland'
years_idx = sex_ratios.Year <= 2015
sex_ratios_uk = sex_ratios[np.logical_and(years_idx, area_idx)]
```
Here take care of the age coding and isolate the data for the United Kingdom and Northern Ireland. Now we can rearrange the data to see ratio per year and age using a pivot table, we can then visualize the result using the heatmap function from seaborn:
```import matplotlib as plt
import seaborn as sns

pivot_uk = sex_ratios_uk.pivot_table(values='Ratio',
index='AgeCode',
columns='Year')
pivot_uk.index = [age_label[a] for a in pivot_uk.index.values]

plt.figure(figsize=(10, 8))
plt.title('Sex ratio per year and age groups')
sns.heatmap(pivot_uk, annot=True)
plt.show()
```

In each year we see that the ratio was above 1 (in favor of males) for young ages it then becomes lower than 1 during adulthood and keeps lowering with the age. It also seems that with time the ratio decreases more slowly. For example, we see that the age group 70-74 had a ratio of 0.63 in 1970, while the ration in 2015 was 0.9.