<jupyter_start><jupyter_text>Appendices to making Land Cover Type time series  Appendices  Rough working  Correlations between variables Let's look at how the counts of different groups correlate with each other within each study site through time.<jupyter_code>top_epd_data.head()
df['group_pct']['grass'].loc['Sanabria Marsh'].head()
import seaborn as sns
sns.pairplot(df.loc[u'Navarrés''<NAME>', :]['count_group_tot'])<jupyter_output><empty_output><jupyter_text>Are there clusters we can find in the counts of different pollen? Could investigate using KNN.<jupyter_code>pollen_conts.head()
pollen_conts['pollen_pct']['mean'].sort_values(ascending=False)
nav_pine = nav_dat[nav_dat.varname=='Pinus']
nav_pine['count'].hist()
nav_pine['pollen_pct'].hist()
nav_dat[nav_dat.varname=='Concentration spikes'].pollen_pct.plot()<jupyter_output><empty_output>
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