python shift operator uses Question: Im just learning about pythons bitwise operator << and >>. as far as I see, it takes the binary version of an integer and shifts it n places left or right. that would mean that saying x<<y is equivalent to x*(2**y) so my question is why is there an operator …
Shifting rows based on current column values in pandas Question: I’m trying to shift all rows that have an E in colB to be blank in colA and then the values of colA and colB shifted over 1. Everything I search on shift or adjusting has to do with moving columns up or down. I …
pandas shifting missing months Question: let’s assume the following dataframe and shift operation: d = {‘col1’: [‘2022-01-01′,’2022-02-01′,’2022-03-01′,’2022-05-01’], ‘col2’: [1,2,3,4]} df = pd.DataFrame(d) df[‘shifted’] = df[‘col2’].shift(1, fill_value=0) I want to create a column containing the values of the month before and filling it up for months which do not exist with 0, so the desired result …
How can I move every other letter of a string Question: I would like help with one cipher, specifically such that I have words/letters and each shift by a certain number example word Example +1 input number output is example +2 input numbers output is Eapexml I have this script but it only works for …
Groupby shift (lagged values) analogue with only Numpy (no pandas) Question: I have a dataframe that looks like this: id date v1 0 0 1983.0 1.574 1 0 1984.0 1.806 2 0 1985.0 4.724 3 1 1986.0 0.320 4 1 1987.0 3.414 … … … 107191 9874 1993.0 52.448 107192 9874 1994.0 108.652 107193 9875 …
Comparing previous row values in Pandas DataFrame Question: import pandas as pd data={‘col1’:[1,3,3,1,2,3,2,2]} df=pd.DataFrame(data,columns=[‘col1’]) print df col1 0 1 1 3 2 3 3 1 4 2 5 3 6 2 7 2 I have the following Pandas DataFrame and I want to create another column that compares the previous row of col1 to see …
Move column by name to front of table in pandas Question: Here is my df: Net Upper Lower Mid Zsore Answer option More than once a day 0% 0.22% -0.12% 2 65 Once a day 0% 0.32% -0.19% 3 45 Several times a week 2% 2.45% 1.10% 4 78 Once a week 1% 1.63% -0.40% …
