From junior to geek the road is sleek
Hello. I have been using np.arrange for a long time but only now I have noticed that this built-in function doesn’t include the last maximum value. Like for example: step = 2 print(np.arange(0, 26, step)) results in [0 2 4 6 8 10 12 14 16 18 20 22 24] So how do we make … Read more
Unfortunately you can use ternary operator like this a if x>y else b on pandas dataframe logic. With that said you can use numpy.where instead: df[‘result’] = np.where(df1[‘col1’] > df1[‘col2′], 1, 0) There you go. It’s also much faster.
I found a great cheat-sheet image that shows how Numpy slicing works: Thanks to scipy-lectures.org
In my case the .csv file had strings as date and time with the following format: 30/07/2017 21:01:17 I find this to be the simplest way to convert this column to float: df[‘dateTime’] = df[‘dateTime’].str.replace(‘/’, ”) df[‘dateTime’] = df[‘dateTime’].str.replace(‘:’, ”) df[‘dateTime’] = df[‘dateTime’].str.replace(‘ ‘, ”) df[‘dateTime’] = df[‘dateTime’].astype(float) So we are removing any signs and … Read more