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Ode to Solitude
You’re a compulsory condition It’s like rainbow of emotions brewing a brittle glass Loneliness,Eagerness,Distaste,Jealousy Freedom,Gratitude,Love,Content What a Dilemma! You’re my enemy in short term decisions You’re my friend in longer ones All in all, if I can keep it together What comes about, I hope is Realization It just so happens that some of us
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Weather Data Analysis – Part II
Oakland Weather Pattern with T-Stats. This is part II of my previous posting regarding Oakland’s weird weather despite Global Warming. You can go ahead and read what I’ve written there (which is a jumble of IPython Notebook code that I can no longer change because Blogger is acting weird. If you’ve seen it already skip
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Finding Out All of your NAN and NAT rows in your Data Frame At Once
Coming into DataFrames, one of my biggest pain points was finding out where the NAN (Not A Number) or NAT (Not A Time) is in my plethora of data. I prefer not to drop these rows or ignore them during crucial calculations. Sometimes I may want to find out exactly where these values are so
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Converting All Unix Time Stamp to DateTime in a DataFrame in One Run!
While Data Munging for my Weather Data Analysis blog, I skipped out a bunch of other valuable data which were all related converting to date_time. I managed to come across a solution today while playing with Pandas’ .apply() and .applymap() functions. Here’s how the raw data looked like prior human readable date time conversion. To
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Weather Data Analysis – Part I
So I was playing with IPython along with the holy trinity matplotlib, pandas, and numpy. I needed some data to really learn something here. It was March, 2016, and I was thinking, “Wow, it seems to be getting colder year after year in San Francisco! Sure don’t feel the Global Warming yet!” I didn’t have
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