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# jupyter:
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# +
import lux
import pandas as pd

# lux.config.plotting_backend = "matplotlib"
lux.config.sampling = False


# +
df = pd.read_csv("/Users/s57405/git/iag_geo/spark_testing/apache_sedona/testing/junkpile/02_spatial_join_sql_st_subdivide_benchmark.csv")

# convert times to seconds
df["processing_time"] = (pd.to_timedelta(df["processing_time"])
                         .astype('timedelta64[s]')
                         .astype(int)
                        )

df["computer"] = df["computer"].astype("string")

# filter by run name
df2 = df[(df["computer"] == "macbook2-no-cache") & (df["processing_time"] < 330)]
# .drop("computer", axis=1)

# df2 = df[df["processing_time"] < 350]


# -

df2

df.recommendation

df.info()

# +
# df.exported
# -



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