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attacks_and_shapes_summed["poss_voters"] = attacks_and_shapes_summed["Total"]/(attacks_and_shapes_summed["Participat"]/100)
attacks_and_shapes_summed["rate_civ"] = attacks_and_shapes_summed["sum_deaths_civ"]/attacks_and_shapes_summed["poss_voters"]
attacks_and_shapes_summed["log_civ"] = np.log10(attacks_and_shapes_summed["rate_civ"])
attacks_and_shapes_summed = attacks_and_shapes_summed.replace([np.inf,-np.inf],np.nan).dropna(subset=["log_civ"])
attacks_and_shapes_summed.groupby("dyad_name").mean()["rate_civ"]*1000
keep = ['AUC - FARC','FARC - Civilians','Government of Colombia - FARC'  ]
test = attacks_and_shapes_summed.loc[attacks_and_shapes_summed["dyad_name"].isin(keep)]
sns.lmplot(x="log_civ",y="Fraction_y",hue="dyad_name",data=test)


import statsmodels.formula.api as smf
mod = smf.logit(formula='Fraction_y ~ Participat ',  data=test)
res = mod.fit()
print(res.summary())