Before final plot of data i tried sorting the values my own way, and ended up with different results
agg_counts = agg_counts.sort_values(by=['Not Windows','Windows'],ascending=False).head(10)
agg_counts
agg_counts.sum(1).sort_values(ascending=False)
Results in different output why?
This is what i obtain, before final plot
tz
America/New_York 1251.0
521.0
America/Chicago 400.0
America/Los_Angeles 382.0
America/Denver 191.0
Europe/London 74.0
Europe/Madrid 35.0
America/Sao_Paulo 33.0
America/Rainy_River 25.0
Europe/Amsterdam 22.0
But, if i use agrsort(), as prescribed in the book the result is a bit different, i have placed * on different tz
Arsim2
commented
6 years ago
Before final plot of data i tried sorting the values my own way, and ended up with different results
agg_counts = agg_counts.sort_values(by=['Not Windows','Windows'],ascending=False).head(10) agg_counts agg_counts.sum(1).sort_values(ascending=False) Results in different output why? This is what i obtain, before final plot tz America/New_York 1251.0 521.0 America/Chicago 400.0 America/Los_Angeles 382.0 America/Denver 191.0 Europe/London 74.0 Europe/Madrid 35.0 America/Sao_Paulo 33.0 America/Rainy_River 25.0 Europe/Amsterdam 22.0 But, if i use agrsort(), as prescribed in the book the result is a bit different, i have placed * on different tz
tz America/New_York 1251.0 521.0 America/Chicago 400.0 America/Los_Angeles 382.0 America/Denver 191.0 Europe/London 74.0 Asia/Tokyo 37.0 Pacific/Honolulu 36.0 Europe/Madrid 35.0 America/Sao_Paulo 33.0 dtype: float64