Look at specific values of proximity.
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d3f42ea402
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1aaf95fe9e
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@ -66,4 +66,47 @@ sns.displot(
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# %%
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# %%
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df_proximity_inactive.double_proximity.value_counts()
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df_proximity_inactive.double_proximity.value_counts()
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# %% [markdown]
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# We have already seen 5.0 and 5.000305 from the same device. What about other values?
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# %% [markdown]
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# # Participant proximity values
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# %%
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# %%
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df_proximity_combinations = pd.crosstab(
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df_proximity_inactive.username, df_proximity_inactive.double_proximity
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)
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display(df_proximity_combinations)
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# %% [markdown]
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# Proximity labelled as 0 and 1.
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# %%
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df_proximity_combinations[df_proximity_combinations[1] != 0]
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# %% [markdown]
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# Proximity labelled as 0 and 8.
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# %%
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df_proximity_combinations[df_proximity_combinations[8] != 0]
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# %% [markdown]
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# The rest of the devices have proximity labelled as 0 and 5.00030517578125 or both of these values for "far".
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# %%
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df_proximity_combinations[
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(df_proximity_combinations[5.0] != 0)
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& (df_proximity_combinations[5.00030517578125] == 0)
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]
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# %%
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df_proximity_combinations[
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(df_proximity_combinations[5.0] == 0)
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& (df_proximity_combinations[5.00030517578125] != 0)
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]
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# %%
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df_proximity_combinations[
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(df_proximity_combinations[5.0] != 0)
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& (df_proximity_combinations[5.00030517578125] != 0)
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]
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